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Sangga123
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, LabelEncoder
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from sklearn.metrics.pairwise import cosine_similarity
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from sklearn.model_selection import KFold
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import warnings
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warnings.filterwarnings('ignore')
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class JobRecommendationSystem:
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def __init__(self):
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self.skill_encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')
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self.interest_encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')
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self.major_encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')
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self.job_encoder = LabelEncoder()
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self.ipk_scaler = MinMaxScaler()
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self.is_fitted = False
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def preprocess_data(self, df):
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"""Preprocess data untuk feature engineering"""
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# Copy data untuk menghindari modifikasi asli
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data = df.copy()
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# Normalisasi nama kolom
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data.columns = data.columns.str.strip()
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# Clean dan standardisasi text
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data['keterampilan'] = data['keterampilan'].str.strip().str.lower()
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data['minat'] = data['minat'].str.strip().str.lower()
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data['Jurusan'] = data['Jurusan'].str.strip().str.lower()
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data['dream job'] = data['dream job'].str.strip()
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return data
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def create_features(self, data, fit=False):
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"""Membuat feature matrix dari data"""
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# Reshape untuk encoder
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skills = data['keterampilan'].values.reshape(-1, 1)
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interests = data['minat'].values.reshape(-1, 1)
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majors = data['Jurusan'].values.reshape(-1, 1)
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ipk = data['IPK'].values.reshape(-1, 1)
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if fit:
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# Fit encoders
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skill_features = self.skill_encoder.fit_transform(skills)
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interest_features = self.interest_encoder.fit_transform(interests)
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major_features = self.major_encoder.fit_transform(majors)
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ipk_features = self.ipk_scaler.fit_transform(ipk)
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else:
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# Transform only
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skill_features = self.skill_encoder.transform(skills)
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interest_features = self.interest_encoder.transform(interests)
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major_features = self.major_encoder.transform(majors)
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ipk_features = self.ipk_scaler.transform(ipk)
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# Gabungkan semua features
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features = np.hstack([
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ipk_features,
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skill_features,
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interest_features,
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major_features
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])
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return features
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def fit(self, X_train, y_train):
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"""Fit model dengan training data"""
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self.train_features = self.create_features(X_train, fit=True)
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self.train_jobs = self.job_encoder.fit_transform(y_train)
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self.train_job_names = y_train.values
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self.is_fitted = True
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def predict(self, X_test, k=3):
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"""Predict jobs untuk test data menggunakan cosine similarity"""
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if not self.is_fitted:
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raise ValueError("Model belum di-fit!")
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test_features = self.create_features(X_test, fit=False)
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# Hitung cosine similarity
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similarities = cosine_similarity(test_features, self.train_features)
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predictions = []
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for i, sim_scores in enumerate(similarities):
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# Ambil k tetangga terdekat
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top_k_indices = np.argsort(sim_scores)[::-1][:k]
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# Ambil job dari tetangga terdekat
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neighbor_jobs = [self.train_job_names[idx] for idx in top_k_indices]
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# Hitung frequency-based recommendation
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job_counts = {}
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for job in neighbor_jobs:
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job_counts[job] = job_counts.get(job, 0) + 1
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# Ambil job dengan frekuensi tertinggi
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recommended_job = max(job_counts.keys(), key=job_counts.get)
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predictions.append(recommended_job)
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return predictions
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def predict_top_k(self, X_test, k=3, top_jobs=3):
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"""Predict top-k jobs untuk test data"""
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if not self.is_fitted:
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raise ValueError("Model belum di-fit!")
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test_features = self.create_features(X_test, fit=False)
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similarities = cosine_similarity(test_features, self.train_features)
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predictions = []
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for i, sim_scores in enumerate(similarities):
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top_k_indices = np.argsort(sim_scores)[::-1][:k]
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neighbor_jobs = [self.train_job_names[idx] for idx in top_k_indices]
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# Hitung job frequency
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job_counts = {}
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for job in neighbor_jobs:
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job_counts[job] = job_counts.get(job, 0) + 1
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# Ambil top jobs berdasarkan frequency
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sorted_jobs = sorted(job_counts.items(), key=lambda x: x[1], reverse=True)
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top_recommended = [job for job, count in sorted_jobs[:top_jobs]]
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predictions.append(top_recommended)
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return predictions
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def evaluate_model(df, n_splits=5, k_neighbors=5):
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"""Evaluasi model menggunakan 5-fold cross validation"""
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# Preprocess data
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model = JobRecommendationSystem()
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processed_data = model.preprocess_data(df)
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# Inisialisasi KFold
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kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
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# Metrics untuk setiap fold
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fold_results = []
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print("=" * 80)
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print("EVALUASI 5-FOLD CROSS-VALIDATION DENGAN COSINE SIMILARITY")
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print("=" * 80)
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print()
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for fold, (train_idx, test_idx) in enumerate(kf.split(processed_data), 1):
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print(f"Processing Fold {fold}...")
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# Split data
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train_data = processed_data.iloc[train_idx]
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test_data = processed_data.iloc[test_idx]
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X_train = train_data[['IPK', 'Jurusan', 'keterampilan', 'minat']]
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y_train = train_data['dream job']
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X_test = test_data[['IPK', 'Jurusan', 'keterampilan', 'minat']]
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y_test = test_data['dream job']
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# Fit dan predict
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fold_model = JobRecommendationSystem()
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fold_model.fit(X_train, y_train)
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# Single prediction (top-1)
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predictions_top1 = fold_model.predict(X_test, k=k_neighbors)
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# Top-3 predictions
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predictions_top3 = fold_model.predict_top_k(X_test, k=k_neighbors, top_jobs=3)
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# Hitung metrics
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# Accuracy (exact match)
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exact_matches = sum(1 for pred, actual in zip(predictions_top1, y_test)
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if pred.lower() == actual.lower())
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accuracy = exact_matches / len(y_test)
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# Top-3 accuracy
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top3_matches = 0
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for pred_list, actual in zip(predictions_top3, y_test):
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if any(pred.lower() == actual.lower() for pred in pred_list):
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top3_matches += 1
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top3_accuracy = top3_matches / len(y_test)
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# Partial match (similar to original hit rate)
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partial_matches = 0
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for pred, actual in zip(predictions_top1, y_test):
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pred_lower = pred.lower()
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actual_lower = actual.lower()
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if pred_lower in actual_lower or actual_lower in pred_lower:
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partial_matches += 1
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partial_accuracy = partial_matches / len(y_test)
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# Simpan hasil fold
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fold_result = {
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'fold': fold,
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'train_size': len(train_data),
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'test_size': len(test_data),
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'exact_accuracy': accuracy,
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'partial_accuracy': partial_accuracy,
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'top3_accuracy': top3_accuracy,
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'predictions': predictions_top1,
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'actual': y_test.tolist()
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}
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fold_results.append(fold_result)
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# Print hasil fold
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print(f"Fold {fold} Results:")
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print(f" Train Size: {len(train_data)}, Test Size: {len(test_data)}")
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print(f" Exact Accuracy: {accuracy:.3f} ({accuracy*100:.1f}%)")
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print(f" Partial Accuracy: {partial_accuracy:.3f} ({partial_accuracy*100:.1f}%)")
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print(f" Top-3 Accuracy: {top3_accuracy:.3f} ({top3_accuracy*100:.1f}%)")
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print()
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return fold_results
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def analyze_results(fold_results):
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"""Analisis hasil cross-validation"""
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print("=" * 80)
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print("RINGKASAN HASIL 5-FOLD CROSS-VALIDATION")
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print("=" * 80)
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# Hitung rata-rata metrics
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exact_accs = [result['exact_accuracy'] for result in fold_results]
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partial_accs = [result['partial_accuracy'] for result in fold_results]
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top3_accs = [result['top3_accuracy'] for result in fold_results]
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print(f"\nRata-rata Metrics:")
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print(f"Exact Accuracy: {np.mean(exact_accs):.3f} ± {np.std(exact_accs):.3f}")
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print(f"Partial Accuracy: {np.mean(partial_accs):.3f} ± {np.std(partial_accs):.3f}")
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print(f"Top-3 Accuracy: {np.mean(top3_accs):.3f} ± {np.std(top3_accs):.3f}")
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print(f"\nDetail per Fold:")
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print(f"{'Fold':<6} {'Exact':<8} {'Partial':<8} {'Top-3':<8}")
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print("-" * 35)
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for result in fold_results:
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print(f"{result['fold']:<6} {result['exact_accuracy']:.3f} {result['partial_accuracy']:.3f} {result['top3_accuracy']:.3f}")
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# Analisis error
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print(f"\nAnalisis Kesalahan Prediksi:")
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all_predictions = []
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all_actual = []
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for result in fold_results:
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all_predictions.extend(result['predictions'])
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all_actual.extend(result['actual'])
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# Hitung confusion untuk job yang sering salah
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error_analysis = {}
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for pred, actual in zip(all_predictions, all_actual):
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if pred.lower() != actual.lower():
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key = f"'{actual}' → '{pred}'"
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error_analysis[key] = error_analysis.get(key, 0) + 1
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print("\nKesalahan Prediksi Terbanyak:")
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sorted_errors = sorted(error_analysis.items(), key=lambda x: x[1], reverse=True)
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for error, count in sorted_errors[:10]:
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print(f" {error}: {count} kali")
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return {
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'mean_exact_accuracy': np.mean(exact_accs),
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'std_exact_accuracy': np.std(exact_accs),
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'mean_partial_accuracy': np.mean(partial_accs),
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'std_partial_accuracy': np.std(partial_accs),
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'mean_top3_accuracy': np.mean(top3_accs),
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'std_top3_accuracy': np.std(top3_accs),
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'fold_results': fold_results
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}
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def main():
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"""Fungsi utama untuk menjalankan evaluasi"""
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# Load data
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try:
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df = pd.read_csv('student.csv')
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print(f"Data berhasil dimuat: {len(df)} records")
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print(f"Kolom: {list(df.columns)}")
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print()
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except FileNotFoundError:
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print("File student.csv tidak ditemukan!")
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return
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# Jalankan evaluasi
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fold_results = evaluate_model(df, n_splits=5, k_neighbors=5)
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# Analisis hasil
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summary = analyze_results(fold_results)
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print("\n" + "=" * 80)
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print("KESIMPULAN")
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print("=" * 80)
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print(f"Model Cosine Similarity dengan 5-fold CV menunjukkan:")
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print(f"• Exact Match Accuracy: {summary['mean_exact_accuracy']:.1%} ± {summary['std_exact_accuracy']:.1%}")
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print(f"• Partial Match Accuracy: {summary['mean_partial_accuracy']:.1%} ± {summary['std_partial_accuracy']:.1%}")
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print(f"• Top-3 Accuracy: {summary['mean_top3_accuracy']:.1%} ± {summary['std_top3_accuracy']:.1%}")
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print()
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print("Metode ini menggunakan kesamaan profil mahasiswa (IPK, jurusan, keterampilan, minat)")
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print("untuk merekomendasikan pekerjaan berdasarkan tetangga terdekat.")
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if __name__ == "__main__":
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main()
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@ -0,0 +1,221 @@
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import pandas as pd
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import random
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# Data jurusan dan rekomendasi (tetap sama)
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jurusan_data = {
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"teknik informatika": {
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"keterampilan": [
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"spreadsheet", "python", "analisis data", "visualisasi data",
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"pengujian perangkat lunak", "dokumentasi kasus uji", "manajemen waktu",
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"html", "css", "javascript", "java", "node.js", "sql", "devops", "aws", "azure",
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"react", "angular", "statistik", "machine learning", "cloud operation", "ruby",
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"database", "desain", "pemrograman", "keamanan digital", "troubleshooting",
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"kotlin", "swift", "objective-c", "r", "preprocessing data", "penyebaran model",
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"manajemen proyek", "pemahaman operasi bisnis", "unity", "unreal engine", "c#", "c++"
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],
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"minat": [
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"investigative (analitis)", "conventional (terstruktur)", "artistic (kreatif)",
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"realistic (praktis, teknis)", "enterprising (memimpin)", "social (sosial)"
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]
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},
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"sistem informasi": {
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"keterampilan": [
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"spreadsheet", "python", "analisis data", "visualisasi data",
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"database", "sql", "manajemen proyek", "pemahaman operasi bisnis",
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"administrasi jaringan", "troubleshooting", "dukungan teknis perangkat keras dan perangkat lunak"
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],
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"minat": ["investigative (analitis)", "conventional (terstruktur)", "realistic (praktis, teknis)", "social (sosial)"]
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},
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"matematika": {
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"keterampilan": ["spreadsheet", "python", "analisis data", "visualisasi data", "statistik", "machine learning", "r"],
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"minat": ["investigative (analitis)"]
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},
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"teknik komputer": {
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"keterampilan": [
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"html", "css", "javascript", "python", "java", "node.js", "sql", "devops", "aws", "azure",
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"cloud operation", "ruby", "pemrograman", "keamanan digital", "troubleshooting",
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"kotlin", "swift", "objective-c", "administrasi jaringan"
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],
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"minat": ["conventional (terstruktur)", "artistic (kreatif)", "realistic (praktis, teknis)", "enterprising (memimpin)"]
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},
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"desain komunikasi visual": {
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"keterampilan": ["html", "css", "javascript", "react", "angular", "desain"],
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"minat": ["artistic (kreatif)"]
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},
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"statistik": {
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"keterampilan": ["statistik", "machine learning", "python", "r"],
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"minat": ["investigative (analitis)"]
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},
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"ilmu komputer": {
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"keterampilan": ["database", "sql"],
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"minat": ["investigative (analitis)"]
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}
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}
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def get_recommendation(ipk, jurusan):
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"""Generate recommendation based on IPK and major"""
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jurusan_lower = jurusan.lower()
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if jurusan_lower in ["teknik informatika", "sistem informasi"]:
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if ipk >= 3.7:
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return ["Data Scientist", "Machine Learning Engineer", "Software Engineer"]
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elif ipk >= 3.5:
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return ["Data Analyst", "Web Developer", "Software Engineer"]
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else:
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return ["Junior Developer", "IT Support", "System Administrator"]
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elif jurusan_lower == "matematika":
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return ["Data Scientist", "Statistician", "Data Analyst"]
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elif jurusan_lower == "desain komunikasi visual":
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return ["UI/UX Designer", "Graphic Designer", "Web Designer"]
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elif jurusan_lower == "teknik komputer":
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return ["System Administrator", "Network Engineer", "DevOps Engineer"]
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elif jurusan_lower == "statistik":
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return ["Data Analyst", "Statistician", "Research Analyst"]
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else:
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return ["Database Administrator", "Data Analyst", "System Analyst"]
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def check_hit(target, recommendations):
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"""Check if target job matches any recommendation"""
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target_lower = target.lower()
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for rec in recommendations:
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rec_lower = rec.lower()
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# Check if target contains recommendation or vice versa
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if target_lower in rec_lower or rec_lower in target_lower:
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return True
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return False
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# Load CSV data
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try:
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df = pd.read_csv('student.csv')
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print(f"Data berhasil dimuat: {len(df)} records")
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except FileNotFoundError:
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print("File student.csv tidak ditemukan!")
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exit()
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# Process data
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uji_coba = []
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total = len(df)
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hit = 0
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print("=" * 120)
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print("PENGUJIAN HIT RATE SISTEM IPKMATCHER - MENGGUNAKAN DATASET CSV")
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print("=" * 120)
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print()
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print("{:<3} {:<5} {:<5} {:<20} {:<20} {:<25} {:<35} {:<18} {:<4}".format(
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"No", "NIM", "IPK", "Jurusan", "Keterampilan", "Minat", "Rekomendasi Sistem", "Target", "Hit"))
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print("-" * 120)
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for index, row in df.iterrows():
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nim = row['NIM']
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ipk = row['IPK']
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jurusan = row['Jurusan']
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target = row['dream job']
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keterampilan = row['keterampilan']
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minat = row['minat']
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||||
|
||||
# Generate recommendations
|
||||
rekomendasi = get_recommendation(ipk, jurusan)
|
||||
|
||||
# Check hit
|
||||
is_hit = check_hit(target, rekomendasi)
|
||||
if is_hit:
|
||||
hit += 1
|
||||
|
||||
# Store for analysis
|
||||
uji_coba.append({
|
||||
"nim": nim,
|
||||
"ipk": ipk,
|
||||
"jurusan": jurusan,
|
||||
"keterampilan": keterampilan,
|
||||
"minat": minat,
|
||||
"rekomendasi": rekomendasi,
|
||||
"target": target,
|
||||
"hit": is_hit
|
||||
})
|
||||
|
||||
# Format for display
|
||||
jurusan_short = (jurusan[:17] + "...") if len(jurusan) > 20 else jurusan
|
||||
keterampilan_short = (keterampilan[:17] + "...") if len(keterampilan) > 20 else keterampilan
|
||||
minat_short = (minat[:22] + "...") if len(minat) > 25 else minat
|
||||
rekom_short = (", ".join(rekomendasi)[:32] + "...") if len(", ".join(rekomendasi)) > 35 else ", ".join(rekomendasi)
|
||||
target_short = (target[:15] + "...") if len(target) > 18 else target
|
||||
|
||||
print("{:<3} {:<5} {:<5} {:<20} {:<20} {:<25} {:<35} {:<18} {:<4}".format(
|
||||
index + 1, nim, ipk, jurusan_short, keterampilan_short, minat_short,
|
||||
rekom_short, target_short, "✓" if is_hit else "✗"))
|
||||
|
||||
# Calculate hit rate
|
||||
hit_rate = hit / total
|
||||
print("-" * 120)
|
||||
print(f"HASIL PENGUJIAN:")
|
||||
print(f"Total Hit: {hit} dari {total} kasus")
|
||||
print(f"Hit Rate: {hit_rate:.3f} ({hit_rate * 100:.1f}%)")
|
||||
print()
|
||||
|
||||
# Analysis per major
|
||||
print("ANALISIS PER JURUSAN:")
|
||||
print("-" * 50)
|
||||
jurusan_stats = {}
|
||||
for case in uji_coba:
|
||||
jurusan = case["jurusan"]
|
||||
if jurusan not in jurusan_stats:
|
||||
jurusan_stats[jurusan] = {"total": 0, "hit": 0}
|
||||
|
||||
jurusan_stats[jurusan]["total"] += 1
|
||||
jurusan_stats[jurusan]["hit"] += case["hit"]
|
||||
|
||||
for jurusan, stats in jurusan_stats.items():
|
||||
rate = stats["hit"] / stats["total"]
|
||||
print(f"{jurusan:<25}: {stats['hit']}/{stats['total']} ({rate*100:.1f}%)")
|
||||
|
||||
# Analysis per IPK range
|
||||
print("\nANALISIS PER RENTANG IPK:")
|
||||
print("-" * 50)
|
||||
ipk_ranges = {
|
||||
"3.8-4.0": {"min": 3.8, "max": 4.0, "total": 0, "hit": 0},
|
||||
"3.5-3.79": {"min": 3.5, "max": 3.79, "total": 0, "hit": 0},
|
||||
"3.0-3.49": {"min": 3.0, "max": 3.49, "total": 0, "hit": 0},
|
||||
"<3.0": {"min": 0, "max": 2.99, "total": 0, "hit": 0}
|
||||
}
|
||||
|
||||
for case in uji_coba:
|
||||
ipk = case["ipk"]
|
||||
for range_name, range_info in ipk_ranges.items():
|
||||
if range_info["min"] <= ipk <= range_info["max"]:
|
||||
range_info["total"] += 1
|
||||
range_info["hit"] += case["hit"]
|
||||
break
|
||||
|
||||
for range_name, stats in ipk_ranges.items():
|
||||
if stats["total"] > 0:
|
||||
rate = stats["hit"] / stats["total"]
|
||||
print(f"IPK {range_name:<10}: {stats['hit']}/{stats['total']} ({rate*100:.1f}%)")
|
||||
|
||||
print("=" * 120)
|
||||
|
||||
# Detailed results
|
||||
# print("\nDETAIL LENGKAP HASIL PENGUJIAN:")
|
||||
# print("=" * 80)
|
||||
# for i, case in enumerate(uji_coba, 1):
|
||||
# print(f"\nTest Case {i}:")
|
||||
# print(f" NIM: {case['nim']}")
|
||||
# print(f" IPK: {case['ipk']}")
|
||||
# print(f" Jurusan: {case['jurusan']}")
|
||||
# print(f" Keterampilan: {case['keterampilan']}")
|
||||
# print(f" Minat: {case['minat']}")
|
||||
# print(f" Rekomendasi: {', '.join(case['rekomendasi'])}")
|
||||
# print(f" Target (Dream Job): {case['target']}")
|
||||
# print(f" Status: {'✓ HIT' if case['hit'] else '✗ MISS'}")
|
||||
# print(f" {'-'*40}")
|
||||
|
||||
# Summary of missed predictions
|
||||
print("\nANALISIS PREDIKSI YANG MELESET:")
|
||||
print("=" * 80)
|
||||
missed_cases = [case for case in uji_coba if not case['hit']]
|
||||
print(f"Total kasus yang meleset: {len(missed_cases)}")
|
||||
|
||||
if missed_cases:
|
||||
print("\nContoh kasus yang meleset:")
|
||||
for i, case in enumerate(missed_cases[:10], 1): # Show first 10 missed cases
|
||||
print(f"{i}. NIM {case['nim']}: Target '{case['target']}' vs Rekomendasi {case['rekomendasi']}")
|
||||
|
|
@ -0,0 +1,188 @@
|
|||
import random
|
||||
jurusan_data = {
|
||||
"teknik informatika": {
|
||||
"keterampilan": [
|
||||
"spreadsheet", "python", "analisis data", "visualisasi data",
|
||||
"pengujian perangkat lunak", "dokumentasi kasus uji", "manajemen waktu",
|
||||
"html", "css", "javascript", "java", "node.js", "sql", "devops", "aws", "azure",
|
||||
"react", "angular", "statistik", "machine learning", "cloud operation", "ruby",
|
||||
"database", "desain", "pemrograman", "keamanan digital", "troubleshooting",
|
||||
"kotlin", "swift", "objective-c", "r", "preprocessing data", "penyebaran model",
|
||||
"manajemen proyek", "pemahaman operasi bisnis", "unity", "unreal engine", "c#", "c++"
|
||||
],
|
||||
"minat": [
|
||||
"investigative (analitis)", "conventional (terstruktur)", "artistic (kreatif)",
|
||||
"realistic (praktis, teknis)", "enterprising (memimpin)", "social (sosial)"
|
||||
]
|
||||
},
|
||||
"sistem informasi": {
|
||||
"keterampilan": [
|
||||
"spreadsheet", "python", "analisis data", "visualisasi data",
|
||||
"database", "sql", "manajemen proyek", "pemahaman operasi bisnis",
|
||||
"administrasi jaringan", "troubleshooting", "dukungan teknis perangkat keras dan perangkat lunak"
|
||||
],
|
||||
"minat": ["investigative (analitis)", "conventional (terstruktur)", "realistic (praktis, teknis)", "social (sosial)"]
|
||||
},
|
||||
"matematika": {
|
||||
"keterampilan": ["spreadsheet", "python", "analisis data", "visualisasi data", "statistik", "machine learning", "r"],
|
||||
"minat": ["investigative (analitis)"]
|
||||
},
|
||||
"teknik komputer": {
|
||||
"keterampilan": [
|
||||
"html", "css", "javascript", "python", "java", "node.js", "sql", "devops", "aws", "azure",
|
||||
"cloud operation", "ruby", "pemrograman", "keamanan digital", "troubleshooting",
|
||||
"kotlin", "swift", "objective-c", "administrasi jaringan"
|
||||
],
|
||||
"minat": ["conventional (terstruktur)", "artistic (kreatif)", "realistic (praktis, teknis)", "enterprising (memimpin)"]
|
||||
},
|
||||
"desain komunikasi visual": {
|
||||
"keterampilan": ["html", "css", "javascript", "react", "angular", "desain"],
|
||||
"minat": ["artistic (kreatif)"]
|
||||
},
|
||||
"statistik": {
|
||||
"keterampilan": ["statistik", "machine learning", "python", "r"],
|
||||
"minat": ["investigative (analitis)"]
|
||||
},
|
||||
"ilmu komputer": {
|
||||
"keterampilan": ["database", "sql"],
|
||||
"minat": ["investigative (analitis)"]
|
||||
}
|
||||
}
|
||||
|
||||
def get_random_skills_interests(jurusan):
|
||||
if jurusan in jurusan_data:
|
||||
keterampilan = random.choice(jurusan_data[jurusan]["keterampilan"])
|
||||
minat = random.choice(jurusan_data[jurusan]["minat"])
|
||||
return keterampilan, minat
|
||||
return "python", "investigative (analitis)" # default
|
||||
|
||||
random.seed(42)
|
||||
|
||||
uji_coba = []
|
||||
|
||||
test_cases_data = [
|
||||
{"nama": "Ahmad", "ipk": 3.78, "jurusan": "teknik informatika", "target": "Web Developer"},
|
||||
{"nama": "Sari", "ipk": 3.50, "jurusan": "sistem informasi", "target": "Data Analyst"},
|
||||
{"nama": "Budi", "ipk": 3.83, "jurusan": "teknik informatika", "target": "Data Scientist"},
|
||||
{"nama": "Rina", "ipk": 3.91, "jurusan": "teknik informatika", "target": "Quality Assurance"},
|
||||
{"nama": "Doni", "ipk": 3.83, "jurusan": "sistem informasi", "target": "Data Analyst"},
|
||||
{"nama": "Lisa", "ipk": 3.78, "jurusan": "teknik informatika", "target": "Web Developer"},
|
||||
{"nama": "Agus", "ipk": 3.71, "jurusan": "teknik informatika", "target": "Frontend Developer"},
|
||||
{"nama": "Maya", "ipk": 3.74, "jurusan": "desain komunikasi visual", "target": "UI/UX Designer"},
|
||||
{"nama": "Rudi", "ipk": 3.92, "jurusan": "teknik informatika", "target": "Machine Learning Engineer"},
|
||||
{"nama": "Dewi", "ipk": 3.95, "jurusan": "matematika", "target": "Data Scientist"},
|
||||
{"nama": "Andi", "ipk": 3.25, "jurusan": "teknik komputer", "target": "System Administrator"},
|
||||
{"nama": "Nina", "ipk": 3.65, "jurusan": "statistik", "target": "Data Analyst"},
|
||||
{"nama": "Hadi", "ipk": 3.45, "jurusan": "ilmu komputer", "target": "Database Administrator"},
|
||||
{"nama": "Tina", "ipk": 3.88, "jurusan": "teknik informatika", "target": "Software Engineer"},
|
||||
{"nama": "Bayu", "ipk": 3.55, "jurusan": "sistem informasi", "target": "Business Analyst"},
|
||||
{"nama": "Sinta", "ipk": 3.72, "jurusan": "desain komunikasi visual", "target": "UI/UX Designer"},
|
||||
{"nama": "Dika", "ipk": 3.33, "jurusan": "teknik komputer", "target": "Network Engineer"},
|
||||
{"nama": "Lusi", "ipk": 3.67, "jurusan": "matematika", "target": "Statistician"},
|
||||
{"nama": "Yoga", "ipk": 3.81, "jurusan": "teknik informatika", "target": "DevOps Engineer"},
|
||||
{"nama": "Eka", "ipk": 3.59, "jurusan": "sistem informasi", "target": "IT Support"}
|
||||
]
|
||||
|
||||
for case_data in test_cases_data:
|
||||
keterampilan, minat = get_random_skills_interests(case_data["jurusan"])
|
||||
|
||||
if case_data["jurusan"] in ["teknik informatika", "sistem informasi"]:
|
||||
if case_data["ipk"] >= 3.7:
|
||||
rekomendasi = ["Data Scientist", "Machine Learning Engineer", "Software Engineer"]
|
||||
elif case_data["ipk"] >= 3.5:
|
||||
rekomendasi = ["Data Analyst", "Web Developer", "Software Engineer"]
|
||||
else:
|
||||
rekomendasi = ["Junior Developer", "IT Support", "System Administrator"]
|
||||
elif case_data["jurusan"] == "matematika":
|
||||
rekomendasi = ["Data Scientist", "Statistician", "Data Analyst"]
|
||||
elif case_data["jurusan"] == "desain komunikasi visual":
|
||||
rekomendasi = ["UI/UX Designer", "Graphic Designer", "Web Designer"]
|
||||
elif case_data["jurusan"] == "teknik komputer":
|
||||
rekomendasi = ["System Administrator", "Network Engineer", "DevOps Engineer"]
|
||||
elif case_data["jurusan"] == "statistik":
|
||||
rekomendasi = ["Data Analyst", "Statistician", "Research Analyst"]
|
||||
else:
|
||||
rekomendasi = ["Database Administrator", "Data Analyst", "System Analyst"]
|
||||
|
||||
uji_coba.append({
|
||||
"nama": case_data["nama"],
|
||||
"ipk": case_data["ipk"],
|
||||
"jurusan": case_data["jurusan"],
|
||||
"keterampilan": keterampilan,
|
||||
"minat": minat,
|
||||
"rekomendasi": rekomendasi,
|
||||
"target": case_data["target"]
|
||||
})
|
||||
|
||||
total = len(uji_coba)
|
||||
hit = 0
|
||||
|
||||
print("=" * 120)
|
||||
print("PENGUJIAN HIT RATE SISTEM IPKMATCHER")
|
||||
print("=" * 120)
|
||||
print()
|
||||
|
||||
print("{:<3} {:<8} {:<5} {:<20} {:<20} {:<25} {:<35} {:<18} {:<4}".format(
|
||||
"No", "Nama", "IPK", "Jurusan", "Keterampilan", "Minat", "Rekomendasi Sistem", "Target", "Hit"))
|
||||
print("-" * 120)
|
||||
|
||||
for i, case in enumerate(uji_coba, 1):
|
||||
nama = case["nama"]
|
||||
ipk = case["ipk"]
|
||||
jurusan = case["jurusan"]
|
||||
keterampilan = case["keterampilan"]
|
||||
minat = case["minat"]
|
||||
rekom = case["rekomendasi"]
|
||||
target = case["target"]
|
||||
|
||||
is_hit = 1 if any(target.lower() in rec.lower() or rec.lower() in target.lower() for rec in rekom) else 0
|
||||
hit += is_hit
|
||||
|
||||
jurusan_short = (jurusan[:17] + "...") if len(jurusan) > 20 else jurusan
|
||||
keterampilan_short = (keterampilan[:17] + "...") if len(keterampilan) > 20 else keterampilan
|
||||
minat_short = (minat[:22] + "...") if len(minat) > 25 else minat
|
||||
rekom_short = (", ".join(rekom)[:32] + "...") if len(", ".join(rekom)) > 35 else ", ".join(rekom)
|
||||
target_short = (target[:15] + "...") if len(target) > 18 else target
|
||||
|
||||
print("{:<3} {:<8} {:<5} {:<20} {:<20} {:<25} {:<35} {:<18} {:<4}".format(
|
||||
i, nama, ipk, jurusan_short, keterampilan_short, minat_short, rekom_short, target_short, "✓" if is_hit else "✗"))
|
||||
|
||||
hit_rate = hit / total
|
||||
print("-" * 120)
|
||||
print(f"HASIL PENGUJIAN:")
|
||||
print(f"Total Hit: {hit} dari {total} kasus")
|
||||
print(f"Hit Rate: {hit_rate:.3f} ({hit_rate * 100:.1f}%)")
|
||||
print()
|
||||
|
||||
print("ANALISIS PER JURUSAN:")
|
||||
print("-" * 50)
|
||||
jurusan_stats = {}
|
||||
for case in uji_coba:
|
||||
jurusan = case["jurusan"]
|
||||
if jurusan not in jurusan_stats:
|
||||
jurusan_stats[jurusan] = {"total": 0, "hit": 0}
|
||||
|
||||
jurusan_stats[jurusan]["total"] += 1
|
||||
is_hit = 1 if any(case["target"].lower() in rec.lower() or rec.lower() in case["target"].lower() for rec in case["rekomendasi"]) else 0
|
||||
jurusan_stats[jurusan]["hit"] += is_hit
|
||||
|
||||
for jurusan, stats in jurusan_stats.items():
|
||||
rate = stats["hit"] / stats["total"]
|
||||
print(f"{jurusan.title():<25}: {stats['hit']}/{stats['total']} ({rate*100:.1f}%)")
|
||||
|
||||
print("=" * 120)
|
||||
|
||||
print("\nDETAIL LENGKAP HASIL PENGUJIAN:")
|
||||
print("=" * 80)
|
||||
for i, case in enumerate(uji_coba, 1):
|
||||
print(f"\nTest Case {i}:")
|
||||
print(f" Nama: {case['nama']}")
|
||||
print(f" IPK: {case['ipk']}")
|
||||
print(f" Jurusan: {case['jurusan'].title()}")
|
||||
print(f" Keterampilan: {case['keterampilan'].title()}")
|
||||
print(f" Minat: {case['minat'].title()}")
|
||||
print(f" Rekomendasi: {', '.join(case['rekomendasi'])}")
|
||||
print(f" Target: {case['target']}")
|
||||
is_hit = 1 if any(case["target"].lower() in rec.lower() or rec.lower() in case["target"].lower() for rec in case["rekomendasi"]) else 0
|
||||
print(f" Status: {'✓ HIT' if is_hit else '✗ MISS'}")
|
||||
print(f" {'-'*40}")
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
dream job,IPK,keterampilan_Administrasi Jaringan,keterampilan_Algoritma Machine Learning,keterampilan_Analisis Data,keterampilan_Azure,keterampilan_C#,keterampilan_C++,keterampilan_CSS,keterampilan_Cloud Operation,keterampilan_Database,keterampilan_Desain,keterampilan_Dokumentasi Kasus Uji,keterampilan_Dukungan Teknis Perangkat Keras dan Perangkat Lunak,keterampilan_HTML,keterampilan_Java,keterampilan_JavaScript,keterampilan_Javascript,keterampilan_Keamanan Digital,keterampilan_Kotlin,keterampilan_Kotlin ,keterampilan_Machine Learning,keterampilan_Manajemen Penyimpanan Database,keterampilan_Manajemen Proyek,keterampilan_Manajemen Waktu,keterampilan_Node.js,keterampilan_Pemahaman Operasi Bisnis,keterampilan_Pemrograman,keterampilan_Pengujian Perangkat Lunak,keterampilan_Preprocessing Data,keterampilan_Python,keterampilan_React,keterampilan_Ruby,keterampilan_SQL,keterampilan_Spreadsheet,keterampilan_Statistik,keterampilan_Swift,keterampilan_Troubleshooting,keterampilan_Unity,keterampilan_Unreal Engine,keterampilan_Visualisasi Data,minat_Artistic (Kreatif),minat_Conventional (Terstruktur),minat_Enterprising (Memimpin),minat_Investigative (Analitis),"minat_Realistic (Praktis, Teknis)",minat_Social (Sosial)
|
||||
Backend Developer,0.5707079106380618,0.0,0.0,0.0,0.16666666666666666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6666666666666666,0.0,0.16666666666666666,0.16666666666666666,0.0
|
||||
Business Intelligence Developer,0.7181107600199566,0.0,0.0,0.16666666666666666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,1.0,0.0,0.0
|
||||
Cloud Engineer,0.6885913853317809,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.0,0.8333333333333334,0.0
|
||||
Cybersecurity Analyst,0.7101557736016407,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.16666666666666666,0.3333333333333333,0.3333333333333333,0.16666666666666666,0.0
|
||||
Data Analyst,0.735953054097075,0.0,0.0,0.42857142857142855,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.14285714285714285,0.0,0.0,0.0,0.14285714285714285,0.0,0.0,0.0,0.0,0.0,0.2857142857142857,0.0,0.0,0.0,1.0,0.0,0.0
|
||||
Data Engineer,0.7873219136315758,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.5,0.3333333333333333,0.0
|
||||
Data Scientist,0.7581905870613669,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.0,0.0,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
|
||||
Database Administrator,0.5515383336105103,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
|
||||
DevOps Engineer,0.7336049670159097,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
|
||||
Frontend Developer,0.6585730916347909,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
||||
Game Developer,0.637091856533067,0.0,0.0,0.0,0.0,0.3333333333333333,0.3333333333333333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.16666666666666666,0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
||||
IT Project Manager,0.7553356616220409,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333333333333,0.0,0.0,0.6666666666666666
|
||||
IT Support Specialist,0.7002605465934916,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0
|
||||
Machine Learning Engineer,0.7346859582016739,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.5,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0
|
||||
Mobile App Developer,0.6177726038028714,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333333333333,0.0,0.0,0.0,0.3333333333333333,0.16666666666666666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
||||
Network Administrator,0.745689894118299,0.6666666666666666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3333333333333333,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0
|
||||
Quality Assurance,0.6567104606685512,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6,0.0,0.0,0.0,0.2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0
|
||||
Software Engineer,0.7200510006097897,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5,0.5,0.0,0.0,0.0,0.0
|
||||
UI/UX Designer,0.6349021564388267,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
||||
Web Developer,0.6734297910083704,0.0,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.0,0.0,0.0,0.0,0.16666666666666666,0.0,0.0,0.6666666666666666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
|
||||
|
Binary file not shown.
|
|
@ -0,0 +1,240 @@
|
|||
from flask import Flask, render_template, request, redirect, url_for
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from sklearn.metrics.pairwise import cosine_similarity
|
||||
from sklearn.preprocessing import MinMaxScaler
|
||||
import logging
|
||||
import jinja2
|
||||
|
||||
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
def load_data(job_file):
|
||||
try:
|
||||
jobs = pd.read_excel(job_file)
|
||||
expected_job_columns = ['id', 'job', 'min_ipk', 'job_desc', 'jurusan', 'keterampilan', 'minat']
|
||||
|
||||
# Debug: tampilkan kolom yang ada
|
||||
logger.info(f"Kolom yang ditemukan di {job_file}: {list(jobs.columns)}")
|
||||
logger.info(f"Kolom yang diharapkan: {expected_job_columns}")
|
||||
|
||||
# Cek apakah semua kolom yang diperlukan ada (tidak harus urutan yang sama)
|
||||
missing_columns = [col for col in expected_job_columns if col not in jobs.columns]
|
||||
if missing_columns:
|
||||
logger.error(f"Kolom yang hilang: {missing_columns}")
|
||||
raise ValueError(f"File {job_file} harus memiliki kolom: {expected_job_columns}. Kolom yang hilang: {missing_columns}")
|
||||
|
||||
# Reorder kolom sesuai ekspektasi dan hapus kolom yang tidak diperlukan
|
||||
jobs = jobs[expected_job_columns]
|
||||
|
||||
jobs = jobs.dropna()
|
||||
jobs['jurusan'] = jobs['jurusan'].str.split(',').str[0].str.strip().str.lower().str.rstrip('.')
|
||||
jobs['keterampilan'] = jobs['keterampilan'].str.split(',').apply(lambda x: [skill.strip().lower() for skill in x])
|
||||
jobs['minat'] = jobs['minat'].str.strip().str.lower()
|
||||
|
||||
logger.info(f"Data jobs berhasil dimuat dan dibersihkan. Shape: {jobs.shape}")
|
||||
logger.info(f"Sample data:\n{jobs.head()}")
|
||||
|
||||
return jobs
|
||||
except Exception as e:
|
||||
logger.error(f"Error memuat data: {e}")
|
||||
raise
|
||||
|
||||
def preprocess_data(jobs, ipk, jurusan, keterampilan, minat):
|
||||
try:
|
||||
# Dapatkan semua nilai unik
|
||||
all_jurusan = sorted(set(jobs['jurusan']).union([jurusan.lower()]))
|
||||
all_keterampilan = sorted(set([skill for sublist in jobs['keterampilan'] for skill in sublist]).union([keterampilan.lower()]))
|
||||
all_minat = sorted(set(jobs['minat']).union([minat.lower()]))
|
||||
|
||||
logger.info(f"Total jurusan unik: {len(all_jurusan)}")
|
||||
logger.info(f"Total keterampilan unik: {len(all_keterampilan)}")
|
||||
logger.info(f"Total minat unik: {len(all_minat)}")
|
||||
|
||||
def to_one_hot(value, all_values):
|
||||
vector = np.array([1 if value.lower() == val else 0 for val in all_values])
|
||||
return vector
|
||||
|
||||
# Buat vektor untuk jobs
|
||||
job_jurusan_vectors = np.array([to_one_hot(jurusan, all_jurusan) for jurusan in jobs['jurusan']])
|
||||
job_minat_vectors = np.array([to_one_hot(minat, all_minat) for minat in jobs['minat']])
|
||||
|
||||
# Buat vektor keterampilan untuk jobs
|
||||
job_keterampilan_vectors = np.zeros((len(jobs), len(all_keterampilan)))
|
||||
for i, skills in enumerate(jobs['keterampilan']):
|
||||
for skill in skills:
|
||||
if skill in all_keterampilan:
|
||||
job_keterampilan_vectors[i, all_keterampilan.index(skill)] = 1
|
||||
|
||||
# Normalisasi IPK
|
||||
scaler = MinMaxScaler()
|
||||
job_gpa = scaler.fit_transform(jobs['min_ipk'].values.reshape(-1, 1)).flatten() # Flatten untuk 1D
|
||||
user_gpa = scaler.transform(np.array([[ipk]])).flatten() # Flatten untuk 1D
|
||||
|
||||
# Debug dimensi sebelum hstack
|
||||
logger.info(f"Dimensi job_jurusan_vectors: {job_jurusan_vectors.shape}")
|
||||
logger.info(f"Dimensi job_keterampilan_vectors: {job_keterampilan_vectors.shape}")
|
||||
logger.info(f"Dimensi job_minat_vectors: {job_minat_vectors.shape}")
|
||||
logger.info(f"Dimensi job_gpa: {job_gpa.shape}")
|
||||
|
||||
# Gabungkan semua vektor jobs
|
||||
job_vectors = np.hstack([
|
||||
job_jurusan_vectors,
|
||||
job_keterampilan_vectors,
|
||||
job_minat_vectors,
|
||||
job_gpa.reshape(-1, 1) # Reshape untuk konsistensi
|
||||
])
|
||||
|
||||
# Buat vektor user
|
||||
user_jurusan_vector = to_one_hot(jurusan, all_jurusan)
|
||||
user_keterampilan_vector = to_one_hot(keterampilan, all_keterampilan)
|
||||
user_minat_vector = to_one_hot(minat, all_minat)
|
||||
|
||||
# Debug dimensi user vectors
|
||||
logger.info(f"Dimensi user_jurusan_vector: {user_jurusan_vector.shape}")
|
||||
logger.info(f"Dimensi user_keterampilan_vector: {user_keterampilan_vector.shape}")
|
||||
logger.info(f"Dimensi user_minat_vector: {user_minat_vector.shape}")
|
||||
logger.info(f"Dimensi user_gpa: {user_gpa.shape}")
|
||||
|
||||
# Gabungkan vektor user
|
||||
user_vector = np.hstack([
|
||||
user_jurusan_vector,
|
||||
user_keterampilan_vector,
|
||||
user_minat_vector,
|
||||
user_gpa
|
||||
])
|
||||
|
||||
logger.info(f"Dimensi final job_vectors: {job_vectors.shape}")
|
||||
logger.info(f"Dimensi final user_vector: {user_vector.shape}")
|
||||
|
||||
return job_vectors, user_vector, all_jurusan, all_keterampilan, all_minat
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error dalam preprocess_data: {e}")
|
||||
import traceback
|
||||
logger.error(f"Traceback: {traceback.format_exc()}")
|
||||
raise
|
||||
|
||||
def recommend_jobs(job_vectors, user_vector, jobs, ipk, top_n=3):
|
||||
try:
|
||||
eligible_jobs = jobs[jobs['min_ipk'] <= ipk].copy()
|
||||
if eligible_jobs.empty:
|
||||
logger.info(f"Tidak ada pekerjaan yang memenuhi syarat untuk IPK {ipk}")
|
||||
return eligible_jobs
|
||||
|
||||
eligible_indices = eligible_jobs.index
|
||||
eligible_job_vectors = job_vectors[eligible_indices]
|
||||
|
||||
# Reshape user_vector untuk cosine_similarity
|
||||
user_vector_reshaped = user_vector.reshape(1, -1)
|
||||
|
||||
logger.info(f"Dimensi eligible_job_vectors: {eligible_job_vectors.shape}")
|
||||
logger.info(f"Dimensi user_vector_reshaped: {user_vector_reshaped.shape}")
|
||||
|
||||
similarity_scores = cosine_similarity(user_vector_reshaped, eligible_job_vectors)
|
||||
top_indices = np.argsort(similarity_scores[0])[::-1][:min(top_n, len(eligible_jobs))]
|
||||
|
||||
recommendations = eligible_jobs.iloc[top_indices].copy()
|
||||
recommendations['similarity_score'] = similarity_scores[0][top_indices]
|
||||
|
||||
logger.info(f"Rekomendasi untuk IPK {ipk}: {len(recommendations)} pekerjaan ditemukan")
|
||||
return recommendations
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error dalam recommend_jobs: {e}")
|
||||
import traceback
|
||||
logger.error(f"Traceback: {traceback.format_exc()}")
|
||||
raise
|
||||
|
||||
@app.route('/')
|
||||
def index():
|
||||
return render_template('index.html')
|
||||
|
||||
@app.route('/recommend', methods=['POST'])
|
||||
def recommend():
|
||||
try:
|
||||
# Debug: Print semua data yang diterima
|
||||
logger.info("=== DEBUG: Data yang diterima dari form ===")
|
||||
for key, value in request.form.items():
|
||||
logger.info(f"{key}: '{value}' (type: {type(value)}, len: {len(value)})")
|
||||
|
||||
nama = request.form['nama'].strip()
|
||||
ipk_input = request.form['ipk'].strip()
|
||||
jurusan = request.form['jurusan'].strip().lower() or 'teknik informatika'
|
||||
keterampilan = request.form['keterampilan'].strip().lower() or 'python'
|
||||
minat = request.form['minat'].strip().lower() or 'investigative (analitis)'
|
||||
|
||||
logger.info(f"=== DEBUG: Data setelah processing ===")
|
||||
logger.info(f"nama: '{nama}'")
|
||||
logger.info(f"ipk_input: '{ipk_input}'")
|
||||
logger.info(f"jurusan: '{jurusan}'")
|
||||
logger.info(f"keterampilan: '{keterampilan}'")
|
||||
logger.info(f"minat: '{minat}'")
|
||||
|
||||
# Debug: Cek apakah IPK kosong atau None
|
||||
if not ipk_input:
|
||||
logger.error("IPK input kosong!")
|
||||
return render_template('index.html', error="IPK tidak boleh kosong!")
|
||||
|
||||
# Replace koma dengan titik
|
||||
ipk_input = ipk_input.replace(',', '.')
|
||||
logger.info(f"IPK setelah replace koma: '{ipk_input}'")
|
||||
|
||||
# Coba konversi ke float
|
||||
try:
|
||||
ipk = float(ipk_input)
|
||||
logger.info(f"IPK berhasil dikonversi: {ipk} (type: {type(ipk)})")
|
||||
except ValueError as e:
|
||||
logger.error(f"Gagal konversi IPK '{ipk_input}' ke float: {e}")
|
||||
return render_template('index.html', error=f"IPK harus berupa angka desimal yang valid (contoh: 3.50)! Input: '{ipk_input}'")
|
||||
|
||||
# Validasi range IPK
|
||||
if not (0.0 <= ipk <= 4.0):
|
||||
logger.warning(f"IPK {ipk} tidak valid, harus antara 0.0 dan 4.0")
|
||||
return render_template('index.html', error=f"IPK harus antara 0.0 dan 4.0! IPK Anda: {ipk}")
|
||||
|
||||
# Validasi dropdown
|
||||
if not jurusan or not keterampilan or not minat:
|
||||
logger.warning("Salah satu dropdown tidak dipilih")
|
||||
logger.warning(f"jurusan: '{jurusan}', keterampilan: '{keterampilan}', minat: '{minat}'")
|
||||
return render_template('index.html', error="Harap pilih jurusan, keterampilan, dan minat!")
|
||||
|
||||
logger.info("Semua validasi berhasil, memproses rekomendasi...")
|
||||
logger.info("Loading jobs.xlsx")
|
||||
jobs = load_data('jobs.xlsx')
|
||||
logger.info("Preprocessing data")
|
||||
job_vectors, user_vector, _, _, _ = preprocess_data(jobs, ipk, jurusan, keterampilan, minat)
|
||||
logger.info("Generating recommendations")
|
||||
recommendations = recommend_jobs(job_vectors, user_vector, jobs, ipk)
|
||||
|
||||
logger.info(f"Recommendations generated: {len(recommendations)} jobs")
|
||||
return render_template('result.html',
|
||||
user_name=nama,
|
||||
user_ipk=ipk,
|
||||
user_jurusan=jurusan,
|
||||
user_keterampilan=keterampilan,
|
||||
user_minat=minat,
|
||||
recommendations=recommendations.to_dict(orient='records'))
|
||||
|
||||
except ValueError as e:
|
||||
logger.error(f"ValueError: {e}")
|
||||
return render_template('index.html', error=f"Terjadi kesalahan pemrosesan data: {str(e)}")
|
||||
except FileNotFoundError as e:
|
||||
logger.error(f"FileNotFoundError: {e}")
|
||||
return render_template('index.html', error="File jobs.xlsx tidak ditemukan!")
|
||||
except jinja2.exceptions.TemplateNotFound as e:
|
||||
logger.error(f"TemplateNotFound: {e}")
|
||||
return render_template('index.html', error=f"Template {e} tidak ditemukan!")
|
||||
except jinja2.exceptions.TemplateSyntaxError as e:
|
||||
logger.error(f"TemplateSyntaxError: {e}")
|
||||
return render_template('index.html', error=f"Kesalahan sintaks di template: {e}")
|
||||
except Exception as e:
|
||||
logger.error(f"Unexpected error: {e}")
|
||||
import traceback
|
||||
logger.error(f"Traceback: {traceback.format_exc()}")
|
||||
return render_template('index.html', error=f"Terjadi kesalahan: {e}")
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True)
|
||||
|
|
@ -0,0 +1,120 @@
|
|||
NIM,IPK,Jurusan,dream job,keterampilan,minat
|
||||
1,3.78,Teknik Informatika,Mobile App Developer,Java,Artistic (Kreatif)
|
||||
2,3.50,Teknik Informatika,Data Scientist,Statistik,Investigative (Analitis)
|
||||
3,3.83,Teknik Informatika,UI/UX Designer,Desain,Artistic (Kreatif)
|
||||
4,3.91,Teknik Informatika,Backend Developer,Node.js,Conventional (Terstruktur)
|
||||
5,3.83,Teknik Informatika,IT Support Specialist,Dukungan Teknis Perangkat Keras dan Perangkat Lunak,Social (Sosial)
|
||||
6,3.64,Teknik Informatika,Web Developer,Javascript,Artistic (Kreatif)
|
||||
7,3.63,Teknik Informatika,Cloud Engineer,Manajemen Penyimpanan Database,"Realistic (Praktis, Teknis)"
|
||||
8,3.88,Teknik Informatika,Data Analyst,Visualisasi Data,Investigative (Analitis)
|
||||
9,3.64,Teknik Informatika,Cybersecurity Analyst,Troubleshooting,Enterprising (Memimpin)
|
||||
10,3.72,Teknik Informatika,Software Engineer,Pemrograman,Conventional (Terstruktur)
|
||||
11,3.58,Teknik Informatika,Game Developer,C#,Artistic (Kreatif)
|
||||
12,3.8,Teknik Informatika,DevOps Engineer,Ruby,"Realistic (Praktis, Teknis)"
|
||||
13,3.52,Teknik Informatika,Database Administrator,SQL,"Realistic (Praktis, Teknis)"
|
||||
14,3.68,Teknik Informatika,IT Project Manager,Manajemen Proyek,Social (Sosial)
|
||||
15,3.56,Teknik Informatika,Machine Learning Engineer,Python,Investigative (Analitis)
|
||||
16,3.79,Teknik Informatika,Business Intelligence Developer,Analisis Data,Investigative (Analitis)
|
||||
17,3.77,Teknik Informatika,Frontend Developer,CSS,Artistic (Kreatif)
|
||||
18,3.6,Teknik Informatika,Network Administrator,Administrasi Jaringan,"Realistic (Praktis, Teknis)"
|
||||
19,3.68,Teknik Informatika,Data Engineer,Database,Investigative (Analitis)
|
||||
20,3.34,Teknik Informatika,Quality Assurance,Dokumentasi Kasus Uji,Conventional (Terstruktur)
|
||||
21,3.76,Teknik Informatika,Data Analyst,Spreadsheet,Investigative (Analitis)
|
||||
22,3.87,Teknik Informatika,Web Developer,HTML,Artistic (Kreatif)
|
||||
23,3.69,Teknik Informatika,Software Engineer,Pemrograman,Conventional (Terstruktur)
|
||||
24,3.69,Teknik Informatika,Mobile App Developer,Kotlin,Artistic (Kreatif)
|
||||
25,3.61,Teknik Informatika,Frontend Developer,JavaScript,Artistic (Kreatif)
|
||||
26,3.89,Teknik Informatika,Data Scientist,Python,Investigative (Analitis)
|
||||
27,3.08,Teknik Informatika,DevOps Engineer,Cloud Operation,"Realistic (Praktis, Teknis)"
|
||||
28,3.46,Teknik Informatika,UI/UX Designer,Desain,Artistic (Kreatif)
|
||||
29,3.5,Teknik Informatika,Cybersecurity Analyst,Keamanan Digital,Enterprising (Memimpin)
|
||||
30,3.21,Teknik Informatika,Backend Developer,Java,Conventional (Terstruktur)
|
||||
31,3.36,Teknik Informatika,Cloud Engineer,Manajemen Penyimpanan Database,"Realistic (Praktis, Teknis)"
|
||||
32,3.48,Teknik Informatika,IT Support Specialist,Dukungan Teknis Perangkat Keras dan Perangkat Lunak,Social (Sosial)
|
||||
33,3.78,Teknik Informatika,Machine Learning Engineer,Preprocessing Data,Investigative (Analitis)
|
||||
34,3.64,Teknik Informatika,Database Administrator,SQL,"Realistic (Praktis, Teknis)"
|
||||
35,3.78,Teknik Informatika,Business Intelligence Developer,Pemahaman Operasi Bisnis,Investigative (Analitis)
|
||||
36,3.2,Teknik Informatika,Game Developer,Unreal Engine,Artistic (Kreatif)
|
||||
37,3.54,Teknik Informatika,Data Engineer,Database,Investigative (Analitis)
|
||||
38,3.79,Teknik Informatika,IT Project Manager,Manajemen Proyek,Enterprising (Memimpin)
|
||||
39,3.53,Teknik Informatika,Network Administrator,Administrasi Jaringan,"Realistic (Praktis, Teknis)"
|
||||
40,3.78,Teknik Informatika,Quality Assurance,Manajemen Waktu,Conventional (Terstruktur)
|
||||
41,3.71,Teknik Informatika,Web Developer,CSS,Artistic (Kreatif)
|
||||
42,3.66,Teknik Informatika,Data Analyst,Analisis Data,Investigative (Analitis)
|
||||
43,3.58,Teknik Informatika,Software Engineer,Pemrograman,Artistic (Kreatif)
|
||||
44,3.1,Teknik Informatika,Frontend Developer,JavaScript,Artistic (Kreatif)
|
||||
45,2.93,Teknik Informatika,Mobile App Developer,Swift,Artistic (Kreatif)
|
||||
46,3.06,Teknik Informatika,Backend Developer,Azure,Conventional (Terstruktur)
|
||||
47,3.83,Teknik Informatika,DevOps Engineer,Ruby,"Realistic (Praktis, Teknis)"
|
||||
48,3.63,Teknik Informatika,UI/UX Designer,Desain,Artistic (Kreatif)
|
||||
49,3.63,Teknik Informatika,Cybersecurity Analyst,Troubleshooting,Investigative (Analitis)
|
||||
50,3.48,Teknik Informatika,Data Scientist,Statistik,Investigative (Analitis)
|
||||
51,3.86,Teknik Informatika,Cloud Engineer,Manajemen Penyimpanan Database,"Realistic (Praktis, Teknis)"
|
||||
52,3.62,Teknik Informatika,Network Administrator,Troubleshooting,"Realistic (Praktis, Teknis)"
|
||||
53,3.58,Teknik Informatika,Machine Learning Engineer,Machine Learning,Investigative (Analitis)
|
||||
54,3.5,Teknik Informatika,IT Project Manager,Manajemen Proyek,Social (Sosial)
|
||||
55,3.13,Teknik Informatika,Business Intelligence Developer,Visualisasi Data,Investigative (Analitis)
|
||||
56,3.45,Teknik Informatika,Game Developer,C++,Artistic (Kreatif)
|
||||
57,3.81,Teknik Informatika,Data Engineer,SQL,Conventional (Terstruktur)
|
||||
58,3.23,Teknik Informatika,IT Support Specialist,Dukungan Teknis Perangkat Keras dan Perangkat Lunak,Social (Sosial)
|
||||
59,2.97,Teknik Informatika,Database Administrator,Database,"Realistic (Praktis, Teknis)"
|
||||
60,3.19,Teknik Informatika,Quality Assurance,Pengujian Perangkat Lunak,Conventional (Terstruktur)
|
||||
61,3.24,Teknik Informatika,Data Analyst,Analisis Data,Investigative (Analitis)
|
||||
62,3.3,Teknik Informatika,Web Developer,Javascript,Artistic (Kreatif)
|
||||
63,3.51,Teknik Informatika,Software Engineer,Pemrograman,Artistic (Kreatif)
|
||||
64,3.77,Teknik Informatika,Data Scientist,Machine Learning,Investigative (Analitis)
|
||||
65,3.78,Teknik Informatika,Frontend Developer,HTML,Artistic (Kreatif)
|
||||
66,3.71,Teknik Informatika,Backend Developer,Java,Conventional (Terstruktur)
|
||||
67,3.74,Teknik Informatika,DevOps Engineer,Python,"Realistic (Praktis, Teknis)"
|
||||
68,3.92,Teknik Informatika,UI/UX Designer,Desain,Artistic (Kreatif)
|
||||
69,3.95,Teknik Informatika,Cybersecurity Analyst,Troubleshooting,Investigative (Analitis)
|
||||
70,3.93,Teknik Informatika,Mobile App Developer,Java,Artistic (Kreatif)
|
||||
71,3.85,Teknik Informatika,Cloud Engineer,Manajemen Penyimpanan Database,"Realistic (Praktis, Teknis)"
|
||||
72,3.56,Teknik Informatika,Network Administrator,Administrasi Jaringan,"Realistic (Praktis, Teknis)"
|
||||
73,3.63,Teknik Informatika,Machine Learning Engineer,Python,Investigative (Analitis)
|
||||
74,3.9,Teknik Informatika,IT Project Manager,Manajemen Proyek,Social (Sosial)
|
||||
75,3.81,Teknik Informatika,Business Intelligence Developer,Visualisasi Data,Investigative (Analitis)
|
||||
76,3.55,Teknik Informatika,Game Developer,Unity,Artistic (Kreatif)
|
||||
77,3.89,Teknik Informatika,Data Engineer,Database,"Realistic (Praktis, Teknis)"
|
||||
78,3.75,Teknik Informatika,IT Support Specialist,Dukungan Teknis Perangkat Keras dan Perangkat Lunak,Social (Sosial)
|
||||
79,3.53,Teknik Informatika,Database Administrator,SQL,"Realistic (Praktis, Teknis)"
|
||||
80,3.8,Teknik Informatika,Quality Assurance,Manajemen Waktu,Conventional (Terstruktur)
|
||||
81,3.9,Teknik Informatika,Data Analyst,Visualisasi Data,Investigative (Analitis)
|
||||
82,3.68,Teknik Informatika,Web Developer,Javascript,Artistic (Kreatif)
|
||||
83,3.71,Teknik Informatika,Software Engineer,Pemrograman,Artistic (Kreatif)
|
||||
84,3.76,Teknik Informatika,Data Scientist,Machine Learning,Investigative (Analitis)
|
||||
85,3.56,Teknik Informatika,Frontend Developer,React,Artistic (Kreatif)
|
||||
86,3.58,Teknik Informatika,Backend Developer,Python,Investigative (Analitis)
|
||||
87,3.93,Teknik Informatika,DevOps Engineer,Cloud Operation,"Realistic (Praktis, Teknis)"
|
||||
88,3.44,Teknik Informatika,UI/UX Designer,Desain,Artistic (Kreatif)
|
||||
89,3.92,Teknik Informatika,Cybersecurity Analyst,Keamanan Digital,"Realistic (Praktis, Teknis)"
|
||||
90,3.45,Teknik Informatika,Mobile App Developer,Kotlin,Artistic (Kreatif)
|
||||
91,3.62,Teknik Informatika,Cloud Engineer,Manajemen Penyimpanan Database,Conventional (Terstruktur)
|
||||
92,3.95,Teknik Informatika,Network Administrator,Troubleshooting,"Realistic (Praktis, Teknis)"
|
||||
93,3.76,Teknik Informatika,Machine Learning Engineer,Python,Investigative (Analitis)
|
||||
94,3.76,Teknik Informatika,IT Project Manager,Manajemen Proyek,Social (Sosial)
|
||||
95,3.76,Teknik Informatika,Business Intelligence Developer,Visualisasi Data,Investigative (Analitis)
|
||||
96,3.92,Teknik Informatika,Game Developer,C#,Artistic (Kreatif)
|
||||
97,3.65,Teknik Informatika,Data Engineer,Database,"Realistic (Praktis, Teknis)"
|
||||
98,3.86,Teknik Informatika,IT Support Specialist,Dukungan Teknis Perangkat Keras dan Perangkat Lunak,Social (Sosial)
|
||||
99,3.81,Teknik Informatika,Database Administrator,SQL,"Realistic (Praktis, Teknis)"
|
||||
100,3.89,Teknik Informatika,Quality Assurance,Manajemen Waktu,Conventional (Terstruktur)
|
||||
101,3.54,Teknik Informatika,Data Analyst,Python,Investigative (Analitis)
|
||||
102,3.5,Teknik Informatika,Web Developer,Javascript,Artistic (Kreatif)
|
||||
103,3.77,Teknik Informatika,Software Engineer,Pemrograman,Conventional (Terstruktur)
|
||||
104,3.81,Teknik Informatika,Data Scientist,Statistik,Investigative (Analitis)
|
||||
105,3.79,Teknik Informatika,Frontend Developer,JavaScript,Artistic (Kreatif)
|
||||
106,3.6,Teknik Informatika,Backend Developer,Node.js,"Realistic (Praktis, Teknis)"
|
||||
107,3.69,Teknik Informatika,DevOps Engineer,Ruby,"Realistic (Praktis, Teknis)"
|
||||
108,3.19,Teknik Informatika,UI/UX Designer,Desain,Artistic (Kreatif)
|
||||
109,3.29,Teknik Informatika,Cybersecurity Analyst,Keamanan Digital,Conventional (Terstruktur)
|
||||
110,3.58,Teknik Informatika,Mobile App Developer,Kotlin ,Artistic (Kreatif)
|
||||
111,3.47,Teknik Informatika,Cloud Engineer,Manajemen Penyimpanan Database,"Realistic (Praktis, Teknis)"
|
||||
112,3.88,Teknik Informatika,Network Administrator,Administrasi Jaringan,"Realistic (Praktis, Teknis)"
|
||||
113,3.76,Teknik Informatika,Machine Learning Engineer,Machine Learning,Investigative (Analitis)
|
||||
114,3.57,Teknik Informatika,IT Project Manager,Manajemen Proyek,Enterprising (Memimpin)
|
||||
115,3.7,Teknik Informatika,Business Intelligence Developer,Pemahaman Operasi Bisnis,Investigative (Analitis)
|
||||
116,3.78,Teknik Informatika,Game Developer,C++,Artistic (Kreatif)
|
||||
117,3.82,Teknik Informatika,Data Engineer,SQL,Investigative (Analitis)
|
||||
118,3.72,Teknik Informatika,IT Support Specialist,Dukungan Teknis Perangkat Keras dan Perangkat Lunak,Social (Sosial)
|
||||
119,3.78,Teknik Informatika,Data Analyst,Analisis Data,Investigative (Analitis)
|
||||
|
|
|
@ -0,0 +1,330 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="id">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<title>IPKMatcher</title>
|
||||
<style>
|
||||
body {
|
||||
margin: 0;
|
||||
font-family: "Helvetica Neue", sans-serif;
|
||||
background-color: #f7ecda;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
min-height: 100vh;
|
||||
padding: 20px;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
.container {
|
||||
text-align: center;
|
||||
width: 100%;
|
||||
max-width: 400px;
|
||||
padding: 20px;
|
||||
background-color: white;
|
||||
border-radius: 20px;
|
||||
box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
|
||||
}
|
||||
|
||||
h1 {
|
||||
font-size: 32px;
|
||||
color: #4c2e2e;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
.subtitle {
|
||||
font-size: 16px;
|
||||
color: #9c9c9c;
|
||||
margin-bottom: 30px;
|
||||
}
|
||||
|
||||
form {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
label {
|
||||
margin-bottom: 5px;
|
||||
font-weight: 500;
|
||||
color: #3e3e3e;
|
||||
}
|
||||
|
||||
input, select {
|
||||
padding: 12px;
|
||||
margin-bottom: 20px;
|
||||
border: none;
|
||||
border-radius: 20px;
|
||||
background-color: #c2bcb3;
|
||||
color: #333;
|
||||
font-size: 14px;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
input::placeholder {
|
||||
color: #888;
|
||||
}
|
||||
|
||||
select {
|
||||
appearance: none;
|
||||
-webkit-appearance: none;
|
||||
-moz-appearance: none;
|
||||
background-image: url("data:image/svg+xml;utf8,<svg fill='black' height='24' viewBox='0 0 24 24' width='24' xmlns='http://www.w3.org/2000/svg'><path d='M7 10l5 5 5-5z'/><path d='M0 0h24v24H0z' fill='none'/></svg>");
|
||||
background-repeat: no-repeat;
|
||||
background-position-x: 95%;
|
||||
background-position-y: 50%;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
button {
|
||||
padding: 12px;
|
||||
border: none;
|
||||
border-radius: 20px;
|
||||
background-color: #4c2e2e;
|
||||
color: white;
|
||||
font-size: 14px;
|
||||
cursor: pointer;
|
||||
transition: background-color 0.3s;
|
||||
}
|
||||
|
||||
button:hover {
|
||||
background-color: #3b2222;
|
||||
}
|
||||
|
||||
.error {
|
||||
color: red;
|
||||
margin-top: 10px;
|
||||
padding: 10px;
|
||||
background-color: #ffe6e6;
|
||||
border-radius: 10px;
|
||||
font-size: 14px;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.debug-info {
|
||||
background-color: #f0f8ff;
|
||||
border: 1px solid #ccc;
|
||||
padding: 10px;
|
||||
margin-top: 10px;
|
||||
border-radius: 5px;
|
||||
font-size: 12px;
|
||||
text-align: left;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<h1><strong>IPKMatcher</strong></h1>
|
||||
<p class="subtitle">Mulai tentukan karier Anda.</p>
|
||||
|
||||
<form id="recommendForm" action="/recommend" method="POST">
|
||||
<label for="nama">Nama:</label>
|
||||
<input type="text" id="nama" name="nama" placeholder="Masukkan Nama Anda" required value="">
|
||||
|
||||
<label for="ipk">IPK:</label>
|
||||
<input type="number" id="ipk" name="ipk" step="0.01" min="0" max="4" placeholder="Masukkan IPK Anda" required>
|
||||
|
||||
<label for="jurusan">Jurusan:</label>
|
||||
<select id="jurusan" name="jurusan" required>
|
||||
<option value="" disabled selected>Pilih Jurusan</option>
|
||||
<option value="teknik informatika">Teknik Informatika</option>
|
||||
<option value="sistem informasi">Sistem Informasi</option>
|
||||
<option value="matematika">Matematika</option>
|
||||
<option value="teknik komputer">Teknik Komputer</option>
|
||||
<option value="desain komunikasi visual">Desain Komunikasi Visual</option>
|
||||
<option value="statistik">Statistik</option>
|
||||
<option value="ilmu komputer">Ilmu Komputer</option>
|
||||
</select>
|
||||
|
||||
<label for="keterampilan">Keterampilan:</label>
|
||||
<select id="keterampilan" name="keterampilan" required>
|
||||
<option value="" disabled selected>Pilih Keterampilan</option>
|
||||
</select>
|
||||
|
||||
<label for="minat">Minat:</label>
|
||||
<select id="minat" name="minat" required>
|
||||
<option value="" disabled selected>Pilih Minat</option>
|
||||
</select>
|
||||
|
||||
<button type="submit">Cari Rekomendasi</button>
|
||||
</form>
|
||||
|
||||
{% if error %}
|
||||
<div class="error">{{ error }}</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
|
||||
<script>
|
||||
const jurusanData = {
|
||||
"teknik informatika": {
|
||||
keterampilan: [
|
||||
"Spreadsheet", "Python", "Analisis Data", "Visualisasi Data",
|
||||
"Pengujian Perangkat Lunak", "Dokumentasi Kasus Uji", "Manajemen Waktu",
|
||||
"HTML", "CSS", "Javascript", "Java", "Node.js", "SQL", "DevOps", "AWS", "Azure",
|
||||
"React", "Angular", "Statistik", "Machine Learning", "Cloud Operation", "Ruby",
|
||||
"Database", "Desain", "Pemrograman", "Keamanan Digital", "Troubleshooting",
|
||||
"Kotlin", "Swift", "Objective-C", "R", "Preprocessing Data", "Penyebaran Model",
|
||||
"Manajemen Proyek", "Pemahaman Operasi Bisnis", "Unity", "Unreal Engine", "C#", "C++"
|
||||
],
|
||||
minat: [
|
||||
"Investigative (Analitis)", "Conventional (Terstruktur)", "Artistic (Kreatif)",
|
||||
"Realistic (Praktis, Teknis)", "Enterprising (Memimpin)", "Social (Sosial)"
|
||||
]
|
||||
},
|
||||
"sistem informasi": {
|
||||
keterampilan: [
|
||||
"Spreadsheet", "Python", "Analisis Data", "Visualisasi Data",
|
||||
"Database", "SQL", "Manajemen Proyek", "Pemahaman Operasi Bisnis",
|
||||
"Administrasi Jaringan", "Troubleshooting", "Dukungan Teknis Perangkat Keras dan Perangkat Lunak"
|
||||
],
|
||||
minat: ["Investigative (Analitis)", "Conventional (Terstruktur)", "Realistic (Praktis, Teknis)", "Social (Sosial)"]
|
||||
},
|
||||
"matematika": {
|
||||
keterampilan: ["Spreadsheet", "Python", "Analisis Data", "Visualisasi Data", "Statistik", "Machine Learning", "R"],
|
||||
minat: ["Investigative (Analitis)"]
|
||||
},
|
||||
"teknik komputer": {
|
||||
keterampilan: [
|
||||
"HTML", "CSS", "Javascript", "Python", "Java", "Node.js", "SQL", "DevOps", "AWS", "Azure",
|
||||
"Cloud Operation", "Ruby", "Pemrograman", "Keamanan Digital", "Troubleshooting",
|
||||
"Kotlin", "Swift", "Objective-C", "Administrasi Jaringan"
|
||||
],
|
||||
minat: ["Conventional (Terstruktur)", "Artistic (Kreatif)", "Realistic (Praktis, Teknis)", "Enterprising (Memimpin)"]
|
||||
},
|
||||
"desain komunikasi visual": {
|
||||
keterampilan: ["HTML", "CSS", "Javascript", "React", "Angular", "Desain"],
|
||||
minat: ["Artistic (Kreatif)"]
|
||||
},
|
||||
"statistik": {
|
||||
keterampilan: ["Statistik", "Machine Learning", "Python", "R"],
|
||||
minat: ["Investigative (Analitis)"]
|
||||
},
|
||||
"ilmu komputer": {
|
||||
keterampilan: ["Database", "SQL"],
|
||||
minat: ["Investigative (Analitis)"]
|
||||
}
|
||||
};
|
||||
|
||||
function updateDropdowns() {
|
||||
const jurusanSelect = document.getElementById('jurusan');
|
||||
const keterampilanSelect = document.getElementById('keterampilan');
|
||||
const minatSelect = document.getElementById('minat');
|
||||
const selectedJurusan = jurusanSelect.value;
|
||||
|
||||
// Reset dropdowns
|
||||
keterampilanSelect.innerHTML = '';
|
||||
minatSelect.innerHTML = '';
|
||||
|
||||
// Add default options
|
||||
const defaultKeterampilanOption = document.createElement('option');
|
||||
defaultKeterampilanOption.value = '';
|
||||
defaultKeterampilanOption.text = 'Pilih Keterampilan';
|
||||
defaultKeterampilanOption.disabled = true;
|
||||
defaultKeterampilanOption.selected = true;
|
||||
keterampilanSelect.appendChild(defaultKeterampilanOption);
|
||||
|
||||
const defaultMinatOption = document.createElement('option');
|
||||
defaultMinatOption.value = '';
|
||||
defaultMinatOption.text = 'Pilih Minat';
|
||||
defaultMinatOption.disabled = true;
|
||||
defaultMinatOption.selected = true;
|
||||
minatSelect.appendChild(defaultMinatOption);
|
||||
|
||||
if (selectedJurusan && jurusanData[selectedJurusan]) {
|
||||
const { keterampilan, minat } = jurusanData[selectedJurusan];
|
||||
|
||||
// Populate keterampilan
|
||||
keterampilan.forEach(skill => {
|
||||
const option = document.createElement('option');
|
||||
option.value = skill.toLowerCase();
|
||||
option.text = skill;
|
||||
keterampilanSelect.appendChild(option);
|
||||
});
|
||||
|
||||
// Populate minat
|
||||
minat.forEach(interest => {
|
||||
const option = document.createElement('option');
|
||||
option.value = interest.toLowerCase();
|
||||
option.text = interest;
|
||||
minatSelect.appendChild(option);
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// Form validation
|
||||
document.getElementById('recommendForm').addEventListener('submit', function(event) {
|
||||
const namaInput = document.getElementById('nama').value.trim();
|
||||
const ipkInput = document.getElementById('ipk').value.trim();
|
||||
const jurusanSelect = document.getElementById('jurusan').value;
|
||||
const keterampilanSelect = document.getElementById('keterampilan').value;
|
||||
const minatSelect = document.getElementById('minat').value;
|
||||
|
||||
console.log('Form submission data:', {
|
||||
nama: namaInput,
|
||||
ipk: ipkInput,
|
||||
jurusan: jurusanSelect,
|
||||
keterampilan: keterampilanSelect,
|
||||
minat: minatSelect
|
||||
});
|
||||
|
||||
// Check if all fields are filled
|
||||
if (!namaInput) {
|
||||
event.preventDefault();
|
||||
alert('Nama harus diisi!');
|
||||
return;
|
||||
}
|
||||
|
||||
if (!ipkInput) {
|
||||
event.preventDefault();
|
||||
alert('IPK harus diisi!');
|
||||
return;
|
||||
}
|
||||
|
||||
// Validate IPK format
|
||||
const ipkValue = ipkInput.replace(',', '.');
|
||||
const ipk = parseFloat(ipkValue);
|
||||
|
||||
if (isNaN(ipk)) {
|
||||
event.preventDefault();
|
||||
alert('IPK harus berupa angka yang valid!');
|
||||
return;
|
||||
}
|
||||
|
||||
if (ipk < 0 || ipk > 4) {
|
||||
event.preventDefault();
|
||||
alert('IPK harus antara 0.0 dan 4.0!');
|
||||
return;
|
||||
}
|
||||
|
||||
if (!jurusanSelect) {
|
||||
event.preventDefault();
|
||||
alert('Harap pilih jurusan!');
|
||||
return;
|
||||
}
|
||||
|
||||
if (!keterampilanSelect) {
|
||||
event.preventDefault();
|
||||
alert('Harap pilih keterampilan!');
|
||||
return;
|
||||
}
|
||||
|
||||
if (!minatSelect) {
|
||||
event.preventDefault();
|
||||
alert('Harap pilih minat!');
|
||||
return;
|
||||
}
|
||||
|
||||
console.log('Form validation passed, submitting...');
|
||||
});
|
||||
|
||||
// Initialize dropdowns on page load
|
||||
document.addEventListener('DOMContentLoaded', function() {
|
||||
updateDropdowns();
|
||||
});
|
||||
|
||||
// Update dropdowns when jurusan changes
|
||||
document.getElementById('jurusan').addEventListener('change', updateDropdowns);
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -0,0 +1,159 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="id">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<title>Hasil Rekomendasi - IPKMatcher</title>
|
||||
<style>
|
||||
body {
|
||||
margin: 0;
|
||||
font-family: "Helvetica Neue", sans-serif;
|
||||
background-color: #f7ecda;
|
||||
padding: 20px;
|
||||
}
|
||||
|
||||
.container {
|
||||
max-width: 800px;
|
||||
margin: 0 auto;
|
||||
background-color: white;
|
||||
border-radius: 20px;
|
||||
padding: 30px;
|
||||
box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
|
||||
}
|
||||
|
||||
h1 {
|
||||
font-size: 32px;
|
||||
color: #4c2e2e;
|
||||
text-align: center;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
.user-info {
|
||||
background-color: #f8f9fa;
|
||||
padding: 20px;
|
||||
border-radius: 15px;
|
||||
margin-bottom: 30px;
|
||||
}
|
||||
|
||||
.user-info h2 {
|
||||
color: #4c2e2e;
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
.user-info p {
|
||||
margin: 5px 0;
|
||||
color: #333;
|
||||
}
|
||||
|
||||
.recommendations {
|
||||
margin-top: 30px;
|
||||
}
|
||||
|
||||
.recommendations h2 {
|
||||
color: #4c2e2e;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
.job-card {
|
||||
background-color: #f8f9fa;
|
||||
border-radius: 15px;
|
||||
padding: 20px;
|
||||
margin-bottom: 20px;
|
||||
border-left: 5px solid #4c2e2e;
|
||||
}
|
||||
|
||||
.job-title {
|
||||
font-size: 20px;
|
||||
font-weight: bold;
|
||||
color: #4c2e2e;
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
.job-desc {
|
||||
color: #555;
|
||||
margin-bottom: 10px;
|
||||
line-height: 1.5;
|
||||
}
|
||||
|
||||
.job-details {
|
||||
font-size: 14px;
|
||||
color: #777;
|
||||
}
|
||||
|
||||
.similarity-score {
|
||||
background-color: #4c2e2e;
|
||||
color: white;
|
||||
padding: 5px 10px;
|
||||
border-radius: 20px;
|
||||
font-size: 12px;
|
||||
display: inline-block;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
.back-button {
|
||||
display: inline-block;
|
||||
padding: 12px 24px;
|
||||
background-color: #4c2e2e;
|
||||
color: white;
|
||||
text-decoration: none;
|
||||
border-radius: 20px;
|
||||
margin-top: 20px;
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.back-button:hover {
|
||||
background-color: #3b2222;
|
||||
}
|
||||
|
||||
.no-results {
|
||||
text-align: center;
|
||||
color: #777;
|
||||
font-style: italic;
|
||||
padding: 40px;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<h1><strong>IPKMatcher</strong></h1>
|
||||
|
||||
<div class="user-info">
|
||||
<h2>Profil Anda</h2>
|
||||
<p><strong>Nama:</strong> {{ user_name }}</p>
|
||||
<p><strong>IPK:</strong> {{ user_ipk }}</p>
|
||||
<p><strong>Jurusan:</strong> {{ user_jurusan|title }}</p>
|
||||
<p><strong>Keterampilan:</strong> {{ user_keterampilan|title }}</p>
|
||||
<p><strong>Minat:</strong> {{ user_minat|title }}</p>
|
||||
</div>
|
||||
|
||||
<div class="recommendations">
|
||||
<h2>Rekomendasi Pekerjaan</h2>
|
||||
|
||||
{% if recommendations %}
|
||||
{% for job in recommendations %}
|
||||
<div class="job-card">
|
||||
<div class="job-title">{{ job.job }}</div>
|
||||
<div class="job-desc">{{ job.job_desc }}</div>
|
||||
<div class="job-details">
|
||||
<strong>IPK Minimum:</strong> {{ job.min_ipk }} |
|
||||
<strong>Jurusan:</strong> {{ job.jurusan|title }} |
|
||||
<strong>Keterampilan:</strong> {{ job.keterampilan|join(', ')|title }} |
|
||||
<strong>Minat:</strong> {{ job.minat|title }}
|
||||
</div>
|
||||
<div class="similarity-score">
|
||||
Kesesuaian: {{ "%.1f"|format(job.similarity_score * 100) }}%
|
||||
</div>
|
||||
</div>
|
||||
{% endfor %}
|
||||
{% else %}
|
||||
<div class="no-results">
|
||||
<p>Maaf, tidak ada pekerjaan yang sesuai dengan profil Anda.</p>
|
||||
<p>Cobalah meningkatkan IPK atau mengembangkan keterampilan lain.</p>
|
||||
</div>
|
||||
{% endif %}
|
||||
</div>
|
||||
|
||||
<a href="{{ url_for('index') }}" class="back-button">Kembali ke Beranda</a>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
Loading…
Reference in New Issue