98 lines
4.6 KiB
Python
98 lines
4.6 KiB
Python
import time
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import numpy as np
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
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# ======================= RULES DEFINISI ==========================
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rules = [
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({"G001", "G002", "G003", "G004", "G011", "G012"}, "Maag Ringan"),
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({"G001", "G002", "G003", "G004", "G006", "G007", "G010", "G011", "G012"}, "Maag Sedang"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G006"}, "Maag Sedang"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G007"}, "Maag Sedang"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G010"}, "Maag Sedang"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007"}, "Maag Sedang"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G010"}, "Maag Sedang"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G007", "G010"}, "Maag Sedang"),
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({"G001", "G002", "G003", "G004", "G005", "G006", "G007", "G008", "G009", "G010", "G011d", "G012"}, "Maag Kronis"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G005"}, "Maag Kronis"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G008"}, "Maag Kronis"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G009"}, "Maag Kronis"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G005", "G008"}, "Maag Kronis"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G005", "G009"}, "Maag Kronis"),
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({"G001", "G002", "G003", "G004", "G011", "G012", "G006", "G007", "G010", "G008", "G009"}, "Maag Kronis")
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]
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# =================== FUNGSI FORWARD CHAINING =====================
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def forward_chaining(rules, initial_facts):
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facts = set(initial_facts)
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diagnosis = None
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new_facts = True
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while new_facts:
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new_facts = False
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for conditions, result in rules:
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if conditions.issubset(facts) and result not in facts:
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facts.add(result)
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diagnosis = result
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new_facts = True
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return diagnosis
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# ======================= MENU ============================
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mode = input("PILIH MENU:\n1. Diagnosa \n2. Evaluasi Performa \nPilihan (1/2): ").strip()
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if mode == "1":
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print("\n=== PREDIKSI ===")
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print("Masukkan kode gejala (pisahkan dengan koma), contoh: G001,G002,G003")
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input_gejala = input("Gejala: ")
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initial_facts = set(g.strip().upper() for g in input_gejala.split(","))
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start_time = time.time()
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hasil_diagnosa = forward_chaining(rules, initial_facts)
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end_time = time.time()
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print("\n=== Hasil Diagnosa ===")
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print(f"Gejala yang dimasukkan: {initial_facts}")
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print(f"Diagnosa: {hasil_diagnosa if hasil_diagnosa else 'Bukan Maag'}")
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print(f"Waktu Eksekusi: {end_time - start_time:.6f} detik")
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else:
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print("\n=== PERFORMA ===")
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# ================== DATA EVALUASI ==========================
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y_true = np.array([1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 0, 0, 0, 0, 0, 0])
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y_pred = np.array([1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 0, 0, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2, 1, 2, 0, 0, 0, 0, 0, 2])
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# ================== CONFUSION MATRIX =======================
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cm = confusion_matrix(y_true, y_pred)
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plt.figure(figsize=(6, 5))
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sns.heatmap(cm, annot=True, fmt="d", cmap="Blues",
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xticklabels=["Bukan Maag", "Maag Ringan", "Maag Sedang"],
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yticklabels=["Bukan Maag", "Maag Ringan", "Maag Sedang"])
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plt.xlabel("Predicted Label")
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plt.ylabel("True Label")
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plt.title("Confusion Matrix - Diagnosa Maag")
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plt.show()
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# ================== KONVERSI BINER ==========================
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y_true_binary = np.array([1 if x in [1, 2] else 0 for x in y_true])
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y_pred_binary = np.array([1 if x in [1, 2] else 0 for x in y_pred])
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cm_binary = confusion_matrix(y_true_binary, y_pred_binary)
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TN, FP, FN, TP = cm_binary.ravel()
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print("\nConfusion Matrix (Biner):\n", cm_binary)
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print(f"True Positive (TP): {TP}")
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print(f"False Positive (FP): {FP}")
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print(f"True Negative (TN): {TN}")
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print(f"False Negative (FN): {FN}")
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accuracy_binary = accuracy_score(y_true_binary, y_pred_binary)
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precision_binary = precision_score(y_true_binary, y_pred_binary)
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recall_binary = recall_score(y_true_binary, y_pred_binary)
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f1_binary = f1_score(y_true_binary, y_pred_binary)
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print("\n=== Metrik Evaluasi ===")
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print(f"Akurasi : {accuracy_binary:.2f}")
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print(f"Presisi : {precision_binary:.2f}")
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print(f"Recall : {recall_binary:.2f}")
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print(f"F1-Score : {f1_binary:.2f}")
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