first commit

.gitignore

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+
+# Ignore log
+backend/scraping.log
+
+# Ignore all node_modules or venv
+node_modules/
+venv/
+
+# Ignore generated/temporary files
+*.log
+.env
+.DS_Store
+*.json
+
+# Exclude frontend directory from the ignore rules
+!frontend/

backend/.gitignore

@@ -0,0 +1,4 @@
+.env
+venv/
+*.dll
+*.pyd

backend/app.py

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+import os
+import numpy as np
+import tensorflow as tf
+from flask import Flask, request, jsonify, send_file
+from flask_cors import CORS
+from flask_jwt_extended import JWTManager, jwt_required, get_jwt_identity
+import joblib
+import mysql.connector
+from models import db, User
+from config import Config
+from auth import auth_bp
+from admin import admin_bp  # Impor blueprint admin yang sudah berisi semua route
+from datetime import datetime, timedelta
+import logging
+import scraping
+ 
+
+app = Flask(__name__)
+app.config.from_object(Config)
+
+# Konfigurasi JWT
+app.config['JWT_SECRET_KEY'] = 'rahasia-kunci-yang-sangat-aman'  # Ganti dengan secret key yang benar
+app.config['JWT_ACCESS_TOKEN_EXPIRES'] = timedelta(hours=1)
+
+# Inisialisasi ekstensi
+CORS(app)
+jwt = JWTManager(app)
+db.init_app(app)
+
+# JWT error handlers
+@jwt.invalid_token_loader
+def invalid_token_callback(error):
+    logging.error(f"Invalid JWT token: {error}")
+    return jsonify({"status": "error", "message": "Invalid token"}), 401
+
+@jwt.expired_token_loader
+def expired_token_callback(jwt_header, jwt_data):
+    logging.error(f"Expired JWT token: {jwt_data}")
+    return jsonify({"status": "error", "message": "Token has expired"}), 401
+
+@jwt.unauthorized_loader
+def missing_token_callback(error):
+    logging.error(f"Missing JWT token: {error}")
+    return jsonify({"status": "error", "message": "Authorization header is missing"}), 401
+
+# Konfigurasi logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler("app.log"),
+        logging.StreamHandler()
+    ]
+)
+logger = logging.getLogger()
+
+# Registrasi blueprints
+app.register_blueprint(auth_bp, url_prefix='/api/auth')
+app.register_blueprint(admin_bp, url_prefix='/api/admin')
+
+# Buat direktori uploads jika belum ada
+os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
+
+# Inisialisasi database saat aplikasi dimulai
+with app.app_context():
+    db.create_all()
+    
+    # Buat user admin jika belum ada
+    if not User.query.filter_by(username='admin').first():
+        admin = User(username='admin', is_admin=True)
+        admin.set_password('admin123')
+        db.session.add(admin)
+        
+        # Buat user biasa untuk testing
+        user = User(username='user', is_admin=False)
+        user.set_password('user123')
+        db.session.add(user)
+        
+        db.session.commit()
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+SCALER_DIR = os.path.join(BASE_DIR, "scalers")
+MODEL_DIR = os.path.join(BASE_DIR, "models")
+DATASET_DIR = os.path.join(BASE_DIR, "datasets")
+
+for directory in [SCALER_DIR, MODEL_DIR, DATASET_DIR]:
+    os.makedirs(directory, exist_ok=True)
+
+# Fungsi koneksi database
+def connect_db():
+    try:
+        return mysql.connector.connect(
+            host="localhost", 
+            user="root", 
+            password="", 
+            database="harga_komoditas", 
+            autocommit=True
+        )
+    except mysql.connector.Error as err:
+        print(f"❌ Gagal koneksi database: {err}")
+        return None
+
+# Load model dan scaler
+def load_model(komoditas):
+    # Format nama komoditas
+    komoditas_formatted = komoditas.lower().replace(" ", "_").replace("-", "_")
+    
+    # Buat nama file yang diharapkan
+    model_filename = f"{komoditas_formatted}_model.h5"
+    scaler_filename = f"{komoditas_formatted}_scaler.pkl"
+    
+    model_path = os.path.join(MODEL_DIR, model_filename)
+    scaler_path = os.path.join(SCALER_DIR, scaler_filename)
+    
+    logger.info(f"🔍 Mencari model di: {model_path}")
+    logger.info(f"🔍 Mencari scaler di: {scaler_path}")
+
+    if not os.path.exists(model_path) or not os.path.exists(scaler_path):
+        print(f"❌ Model atau scaler untuk {komoditas} tidak ditemukan.")
+        return None, None
+    
+    try:
+        model = tf.keras.models.load_model(model_path)
+        scaler = joblib.load(scaler_path)
+        print("✅ Model dan scaler berhasil dimuat.")
+        return model, scaler
+    except Exception as e:
+        print(f"❌ Gagal memuat model atau scaler: {e}")
+        return None, None
+
+# Ambil 60 harga terakhir dari database
+def get_last_60_prices(komoditas):
+    db = connect_db()
+    if not db:
+        return None
+    
+    try:
+        with db.cursor() as cursor:
+            query = """
+                SELECT harga FROM harga_komoditas
+                WHERE LOWER(REPLACE(REPLACE(komoditas, ' ', '_'), '-', '_')) = LOWER(%s)
+                ORDER BY tanggal DESC LIMIT 60
+            """
+            cursor.execute(query, (komoditas.lower().replace(" ", "_").replace("-", "_"),))
+            result = cursor.fetchall()
+
+        if not result:
+            print(f"⚠️ Tidak ada data harga untuk {komoditas}")
+            return None
+
+        prices = [row[0] for row in result]
+        
+        if len(prices) < 60:
+            avg_price = np.mean(prices)
+            prices.extend([avg_price] * (60 - len(prices)))
+
+        return list(reversed(prices))
+    except mysql.connector.Error as err:
+        print(f"❌ Gagal mengambil data harga: {err}")
+        return None
+    finally:
+        db.close()
+
+
+@app.route("/api/predict-with-filter", methods=["POST"])
+def predict_with_filter():
+    try:
+        data = request.get_json()
+        komoditas = data.get("komoditas")
+        filter_days = data.get("filter_days", 30)  # Default 30 hari
+        
+        # Validasi input
+        if not komoditas:
+            return jsonify({"status": "error", "message": "Parameter 'komoditas' dibutuhkan."}), 400
+            
+        if filter_days not in [3, 7, 30]:
+            return jsonify({"status": "error", "message": "Filter hari harus 3, 7, atau 30."}), 400
+        
+        # Ambil data historis untuk visualisasi
+        db = connect_db()
+        if not db:
+            return jsonify({"status": "error", "message": "Gagal terhubung ke database"}), 500
+            
+        try:
+            with db.cursor(dictionary=True) as cursor:
+                query = """
+                    SELECT harga, tanggal FROM harga_komoditas
+                    WHERE LOWER(REPLACE(REPLACE(komoditas, ' ', '_'), '-', '_')) = LOWER(%s)
+                    ORDER BY tanggal DESC LIMIT 60
+                """
+                cursor.execute(query, (komoditas.lower().replace(" ", "_").replace("-", "_"),))
+                historical_data = cursor.fetchall()
+        finally:
+            db.close()
+            
+        if not historical_data:
+            return jsonify({"status": "error", "message": f"Data historis tidak ditemukan untuk komoditas '{komoditas}'"}), 404
+        
+        # Siapkan data untuk prediksi
+        harga = get_last_60_prices(komoditas)
+        if harga is None:
+            return jsonify({"status": "error", "message": f"Data tidak ditemukan untuk '{komoditas}'"}), 404
+
+        # Load model dan scaler
+        model, scaler = load_model(komoditas)
+        if not model or not scaler:
+            return jsonify({"status": "error", "message": f"Model atau scaler untuk '{komoditas}' tidak tersedia."}), 404
+        
+        # Proses prediksi untuk jumlah hari yang diminta
+        predictions = []
+        harga_np = np.array(harga, dtype=np.float32).reshape(-1, 1)
+        
+        # Data yang akan digunakan untuk prediksi
+        current_data = harga_np.copy()
+        
+        # Dapatkan tanggal terakhir dari data historis untuk mulai prediksi
+        from datetime import datetime, timedelta
+        if historical_data and len(historical_data) > 0:
+            last_date = historical_data[0]["tanggal"]  # Historical data sudah diurutkan DESC
+            
+            # Log untuk debugging
+            logger.info(f"Raw last_date dari DB: {last_date}, tipe: {type(last_date)}")
+            
+            # Pastikan last_date adalah objek datetime
+            if isinstance(last_date, str):
+                last_date = datetime.strptime(last_date, "%Y-%m-%d")
+            
+            # Periksa bulan dan koreksi jika perlu
+            current_month = datetime.now().month
+            if last_date.month != current_month:
+                logger.info(f"Mengoreksi bulan dari {last_date.month} ke {current_month}")
+                # Buat tanggal baru dengan bulan yang benar (bulan saat ini)
+                last_date = datetime(last_date.year, current_month, last_date.day)
+            
+            # Tanggal awal prediksi adalah 1 hari setelah data terakhir
+            start_date = last_date + timedelta(days=1)
+            logger.info(f"Tanggal awal prediksi: {start_date.strftime('%Y-%m-%d')}")
+        else:
+            # Fallback ke tanggal saat ini jika tidak ada data historis
+            start_date = datetime.now()
+        
+        for i in range(filter_days):
+            # Ambil 60 data terbaru untuk prediksi
+            input_data = current_data[-60:].reshape(1, 60, 1)
+            
+            # Normalisasi data
+            input_scaled = scaler.transform(input_data.reshape(-1, 1)).reshape(1, 60, 1)
+            
+            # Prediksi
+            pred = model.predict(input_scaled)
+            
+            # Denormalisasi hasil
+            predicted_price = scaler.inverse_transform(pred).flatten()[0]
+            
+            # Tanggal prediksi
+            prediction_date = start_date + timedelta(days=i)
+            
+            # Tambahkan ke hasil
+            predictions.append({
+                "tanggal": prediction_date.strftime("%Y-%m-%d"),
+                "prediksi": float(predicted_price),
+                "hari_ke": i+1
+            })
+            
+            # Update data untuk prediksi selanjutnya
+            new_value = np.array([[predicted_price]], dtype=np.float32)
+            current_data = np.vstack((current_data, new_value))
+        
+        # Format data historis untuk chart
+        historical_data_formatted = []
+        for item in reversed(historical_data[:30]):  # Terbaru ke terlama, batasi 30 hari
+            tanggal_item = item["tanggal"]
+            
+            # Pastikan tanggal menggunakan bulan yang benar
+            if hasattr(tanggal_item, "strftime"):
+                current_month = datetime.now().month
+                if tanggal_item.month != current_month:
+                    # Koreksi bulan jika perlu
+                    tanggal_item = datetime(tanggal_item.year, current_month, tanggal_item.day)
+                formatted_date = tanggal_item.strftime("%Y-%m-%d")
+            else:
+                formatted_date = tanggal_item
+                
+            historical_data_formatted.append({
+                "tanggal": formatted_date,
+                "harga": float(item["harga"])
+            })
+        
+        # Simpan riwayat prediksi ke database
+        try:
+            db = connect_db()
+            if db:
+                with db.cursor() as cursor:
+                    for pred in predictions:
+                        query = """
+                            INSERT INTO prediksi_history 
+                            (komoditas, tanggal_prediksi, tanggal_dibuat, harga_prediksi, filter_days, user_id)
+                            VALUES (%s, %s, NOW(), %s, %s, %s)
+                        """
+                        # Gunakan user_id dari JWT atau null jika tidak ada
+                        user_id = None
+                        if request.headers.get('Authorization'):
+                            try:
+                                from flask_jwt_extended import get_jwt_identity
+                                user_id = get_jwt_identity()
+                            except:
+                                pass
+                                
+                        cursor.execute(
+                            query,
+                            (komoditas, pred["tanggal"], pred["prediksi"], filter_days, user_id)
+                        )
+                db.close()
+        except Exception as e:
+            logger.error(f"Error menyimpan riwayat prediksi: {e}")
+            # Lanjutkan meskipun ada error, tidak perlu return
+
+        # Format last_date ke string dengan bulan yang benar
+        if hasattr(last_date, "strftime"):
+            last_date_str = last_date.strftime("%Y-%m-%d")
+        else:
+            last_date_str = str(last_date)
+
+        return jsonify({
+            "status": "success", 
+            "komoditas": komoditas, 
+            "filter_days": filter_days,
+            "predictions": predictions,
+            "historical_data": historical_data_formatted,
+            "last_date": last_date_str
+        })
+        
+    except Exception as e:
+        logger.error(f"❌ Error di API /predict-with-filter: {e}")
+        return jsonify({"status": "error", "message": f"Gagal mendapatkan prediksi: {str(e)}"}), 500
+
+# API untuk mendapatkan riwayat prediksi (histori admin)
+@app.route("/api/admin/prediction-history", methods=["GET"])
+@jwt_required()  # Pastikan hanya user terautentikasi yang bisa mengakses
+def get_prediction_history():
+    try:
+        # Cek apakah user adalah admin
+        current_user_id = get_jwt_identity()
+        admin_check = db.session.query(User).filter_by(id=current_user_id, is_admin=True).first()
+        
+        if not admin_check:
+            return jsonify({"status": "error", "message": "Unauthorized access"}), 403
+            
+        # Parameter filter
+        komoditas = request.args.get('komoditas')
+        start_date = request.args.get('start_date')
+        end_date = request.args.get('end_date')
+        limit = request.args.get('limit', 100)
+        
+        conn = connect_db()
+        if not conn:
+            return jsonify({"status": "error", "message": "Gagal terhubung ke database"}), 500
+            
+        try:
+            with conn.cursor(dictionary=True) as cursor:
+                query = """
+                    SELECT ph.id, ph.komoditas, ph.tanggal_prediksi, ph.tanggal_dibuat, 
+                           ph.harga_prediksi, ph.filter_days,
+                           u.username as requested_by
+                    FROM prediksi_history ph
+                    LEFT JOIN users u ON ph.user_id = u.id
+                    WHERE 1=1
+                """
+                
+                params = []
+                
+                # Tambahkan filter jika ada
+                if komoditas:
+                    query += " AND LOWER(REPLACE(REPLACE(ph.komoditas, ' ', '_'), '-', '_')) = LOWER(%s)"
+                    params.append(komoditas.lower().replace(" ", "_").replace("-", "_"))
+                    
+                if start_date:
+                    query += " AND ph.tanggal_dibuat >= %s"
+                    params.append(start_date)
+                    
+                if end_date:
+                    query += " AND ph.tanggal_dibuat <= %s"
+                    params.append(end_date)
+                
+                # Tambahkan sorting dan limit
+                query += " ORDER BY ph.tanggal_dibuat DESC LIMIT %s"
+                params.append(int(limit))
+                
+                cursor.execute(query, params)
+                history = cursor.fetchall()
+                
+                # Format tanggal untuk respon JSON
+                for item in history:
+                    if hasattr(item["tanggal_prediksi"], "strftime"):
+                        item["tanggal_prediksi"] = item["tanggal_prediksi"].strftime("%Y-%m-%d")
+                    if hasattr(item["tanggal_dibuat"], "strftime"):
+                        item["tanggal_dibuat"] = item["tanggal_dibuat"].strftime("%Y-%m-%d %H:%M:%S")
+            
+            return jsonify({
+                "status": "success",
+                "history": history
+            })
+            
+        finally:
+            conn.close()
+            
+    except Exception as e:
+        logger.error(f"❌ Error di API /admin/prediction-history: {e}")
+        return jsonify({"status": "error", "message": f"Gagal mendapatkan riwayat prediksi: {str(e)}"}), 500
+
+
+# API prediksi harga
+@app.route("/api/predict", methods=["POST"])
+def predict():
+    try:
+        data = request.get_json()
+        komoditas = data.get("komoditas")
+
+        if not komoditas:
+            return jsonify({"status": "error", "message": "Parameter 'komoditas' dibutuhkan."}), 400
+        
+        harga = get_last_60_prices(komoditas)
+        if harga is None:
+            return jsonify({"status": "error", "message": f"Data tidak ditemukan untuk '{komoditas}'"}), 404
+
+        model, scaler = load_model(komoditas)
+        if not model or not scaler:
+            return jsonify({"status": "error", "message": f"Model atau scaler untuk '{komoditas}' tidak tersedia."}), 404
+        
+        harga_np = np.array(harga, dtype=np.float32).reshape(-1, 1)
+        
+        if harga_np.shape[0] < 60:
+            return jsonify({"status": "error", "message": "Data harga kurang dari 60 hari."}), 400
+
+        harga_scaled = scaler.transform(harga_np).reshape(1, 60, 1)
+        pred = model.predict(harga_scaled)
+        harga_prediksi = scaler.inverse_transform(pred).flatten()[0]
+
+        return jsonify({"status": "success", "komoditas": komoditas, "predicted_price": round(float(harga_prediksi), 2)})
+    except Exception as e:
+        logger.error(f"❌ Error di API /predict: {e}")
+        return jsonify({"status": "error", "message": f"Gagal mendapatkan prediksi: {str(e)}"}), 500
+
+
+# API untuk mendapatkan harga terbaru
+@app.route('/api/get_latest_prices', methods=['GET'])
+def get_latest_prices():
+    commodity = request.args.get('commodity')
+    
+    if not commodity:
+        return jsonify({'status': 'error', 'message': 'Parameter commodity tidak ditemukan'}), 400
+
+    try:
+        db = connect_db()
+        if not db:
+            return jsonify({'status': 'error', 'message': 'Gagal terhubung ke database'}), 500
+
+        with db.cursor(dictionary=True) as cursor:
+            query = """
+                SELECT harga, tanggal FROM harga_komoditas
+                WHERE LOWER(REPLACE(REPLACE(komoditas, ' ', '_'), '-', '_')) = LOWER(%s)
+                ORDER BY tanggal DESC LIMIT 60
+            """
+            cursor.execute(query, (commodity.lower().replace(" ", "_").replace("-", "_"),))
+            data = cursor.fetchall()
+
+        if not data:
+            return jsonify({'status': 'error', 'message': f'Tidak ada data harga untuk {commodity}'}), 404
+
+        return jsonify({'status': 'success', 'latest_prices': data})
+    except Exception as e:
+        logger.error(f"❌ Error saat mengambil data: {e}")
+        return jsonify({'status': 'error', 'message': f'Gagal mengambil data harga: {str(e)}'}), 500
+    finally:
+        if db:
+            db.close()
+            
+
+@app.route('/api/scrape', methods=['POST'])
+def scrape_data():
+    """
+    Endpoint untuk scraping data
+    """
+    
+    try:
+        data = request.json or {}
+        days_back = data.get('days_back', 70)
+        
+        if not isinstance(days_back, int) or days_back <= 0 or days_back > 365:
+            return jsonify({
+                'status': 'error',
+                'message': 'parameter days_back harus berupa angka antara 1-365'
+            }), 400
+            
+        logger.info(f"menjalankan scraping untuk {days_back} hari terakhir")
+        result = scraping.scrape_and_store(days_back)
+        
+        return jsonify(result)
+    
+    except Exception as e:
+        logger.error(f"error pada endpoint /api/scrape: {str(e)}")
+        return jsonify({
+            'status': 'error',
+            'message': str(e)
+        }), 500
+
+@app.route('/api/check-mapping', methods=['GET'])
+def check_mapping():
+    """Endpoint untuk memeriksa integritas pemetaan komoditas"""
+    try:
+        result = scraping.check_komoditas_mapping_integrity()
+        return jsonify(result)
+    
+    except Exception as e:
+        logger.error(f"Error pada endpoint /api/check-mapping: {str(e)}")
+        return jsonify({
+            'status': 'error',
+            'message': str(e)
+        }), 500
+
+@app.route('/api/data-status', methods=['GET'])
+def data_status():
+   
+    try:
+        days = request.args.get('days', 70, type=int)
+        result = scraping.get_data_status(days)
+        return jsonify(result)
+    
+    except Exception as e:
+        logger.error(f"Error pada endpoint /api/data-status: {str(e)}")
+        return jsonify({
+            'status': 'error',
+            'message': str(e)
+        }), 500
+
+@app.route('/api/komoditas', methods=['GET'])
+def get_komoditas():
+    """
+    Endpoint untuk mendapatkan daftar komoditas yang tersedia
+    """
+    try:
+        return jsonify({
+            'status': 'success',
+            'komoditas': list(scraping.KOMODITAS_DIPERLUKAN),
+            'mapping': scraping.KOMODITAS_MAPPING
+        })
+    
+    except Exception as e:
+        logger.error(f"Error pada endpoint /api/komoditas: {str(e)}")
+        return jsonify({
+            'status': 'error',
+            'message': str(e)
+        }), 500
+    
+if __name__ == "__main__":
+    app.run(debug=True)

backend/auth.py

@@ -0,0 +1,73 @@
+from flask import Blueprint, request, jsonify
+from flask_jwt_extended import create_access_token, jwt_required, get_jwt_identity
+from models import User, db
+
+auth_bp = Blueprint('auth', __name__)
+
+@auth_bp.route('/login', methods=['POST'])
+def login():
+    data = request.get_json()
+    
+    if not data or not data.get('username') or not data.get('password'):
+        return jsonify({'status': 'error', 'message': 'Username dan password diperlukan'}), 400
+    
+    user = User.query.filter_by(username=data['username']).first()
+    
+    if not user or not user.check_password(data['password']):
+        return jsonify({'status': 'error', 'message': 'Username atau password salah'}), 401
+    
+    # Gunakan ID user sebagai identity (bukan username)
+    access_token = create_access_token(identity=user.id)
+    
+    return jsonify({
+        'status': 'success',
+        'token': access_token,  # Gunakan 'token' untuk konsistensi dengan frontend
+        'user': user.to_dict()
+    })
+
+@auth_bp.route('/register', methods=['POST'])
+def register():
+    data = request.get_json()
+    
+    if not data or not data.get('username') or not data.get('password'):
+        return jsonify({'status': 'error', 'message': 'Username dan password diperlukan'}), 400
+    
+    if User.query.filter_by(username=data['username']).first():
+        return jsonify({'status': 'error', 'message': 'Username sudah digunakan'}), 400
+    
+    user = User(username=data['username'], is_admin=data.get('is_admin', False))
+    user.set_password(data['password'])
+    
+    db.session.add(user)
+    db.session.commit()
+    
+    return jsonify({'status': 'success', 'message': 'User berhasil dibuat'})
+
+@auth_bp.route('/me', methods=['GET'])
+@jwt_required()
+def get_user():
+    user_id = get_jwt_identity()  # Mendapatkan ID user dari token
+    user = User.query.get(user_id)
+    
+    if not user:
+        return jsonify({'status': 'error', 'message': 'User tidak ditemukan'}), 404
+    
+    return jsonify({'status': 'success', 'user': user.to_dict()})
+
+@auth_bp.route('/verify', methods=['GET'])
+@jwt_required()
+def verify_token():
+    """
+    Endpoint untuk verifikasi token JWT
+    """
+    user_id = get_jwt_identity()
+    user = User.query.get(user_id)
+    
+    if not user:
+        return jsonify({"status": "error", "message": "User tidak ditemukan"}), 404
+    
+    return jsonify({
+        "status": "success",
+        "message": "Token valid",
+        "user": user.to_dict()
+    })

backend/client/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

backend/client/README.md

@@ -0,0 +1,8 @@
+# React + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react/README.md) uses [Babel](https://babeljs.io/) for Fast Refresh
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh

backend/client/eslint.config.js

@@ -0,0 +1,38 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import react from 'eslint-plugin-react'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+
+export default [
+  { ignores: ['dist'] },
+  {
+    files: ['**/*.{js,jsx}'],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+      parserOptions: {
+        ecmaVersion: 'latest',
+        ecmaFeatures: { jsx: true },
+        sourceType: 'module',
+      },
+    },
+    settings: { react: { version: '18.3' } },
+    plugins: {
+      react,
+      'react-hooks': reactHooks,
+      'react-refresh': reactRefresh,
+    },
+    rules: {
+      ...js.configs.recommended.rules,
+      ...react.configs.recommended.rules,
+      ...react.configs['jsx-runtime'].rules,
+      ...reactHooks.configs.recommended.rules,
+      'react/jsx-no-target-blank': 'off',
+      'react-refresh/only-export-components': [
+        'warn',
+        { allowConstantExport: true },
+      ],
+    },
+  },
+]

backend/client/index.html

@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/vite.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Vite + React</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.jsx"></script>
+  </body>
+</html>

backend/client/public/vite.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>

backend/client/src/App.css

@@ -0,0 +1,42 @@
+#root {
+  max-width: 1280px;
+  margin: 0 auto;
+  padding: 2rem;
+  text-align: center;
+}
+
+.logo {
+  height: 6em;
+  padding: 1.5em;
+  will-change: filter;
+  transition: filter 300ms;
+}
+.logo:hover {
+  filter: drop-shadow(0 0 2em #646cffaa);
+}
+.logo.react:hover {
+  filter: drop-shadow(0 0 2em #61dafbaa);
+}
+
+@keyframes logo-spin {
+  from {
+    transform: rotate(0deg);
+  }
+  to {
+    transform: rotate(360deg);
+  }
+}
+
+@media (prefers-reduced-motion: no-preference) {
+  a:nth-of-type(2) .logo {
+    animation: logo-spin infinite 20s linear;
+  }
+}
+
+.card {
+  padding: 2em;
+}
+
+.read-the-docs {
+  color: #888;
+}

backend/client/src/App.jsx

@@ -0,0 +1,35 @@
+import { useState } from 'react'
+import reactLogo from './assets/react.svg'
+import viteLogo from '/vite.svg'
+import './App.css'
+
+function App() {
+  const [count, setCount] = useState(0)
+
+  return (
+    <>
+      <div>
+        <a href="https://vite.dev" target="_blank">
+          <img src={viteLogo} className="logo" alt="Vite logo" />
+        </a>
+        <a href="https://react.dev" target="_blank">
+          <img src={reactLogo} className="logo react" alt="React logo" />
+        </a>
+      </div>
+      <h1>Vite + React</h1>
+      <div className="card">
+        <button onClick={() => setCount((count) => count + 1)}>
+          count is {count}
+        </button>
+        <p>
+          Edit <code>src/App.jsx</code> and save to test HMR
+        </p>
+      </div>
+      <p className="read-the-docs">
+        Click on the Vite and React logos to learn more
+      </p>
+    </>
+  )
+}
+
+export default App

backend/client/src/assets/react.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="35.93" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 228"><path fill="#00D8FF" d="M210.483 73.824a171.49 171.49 0 0 0-8.24-2.597c.465-1.9.893-3.777 1.273-5.621c6.238-30.281 2.16-54.676-11.769-62.708c-13.355-7.7-35.196.329-57.254 19.526a171.23 171.23 0 0 0-6.375 5.848a155.866 155.866 0 0 0-4.241-3.917C100.759 3.829 77.587-4.822 63.673 3.233C50.33 10.957 46.379 33.89 51.995 62.588a170.974 170.974 0 0 0 1.892 8.48c-3.28.932-6.445 1.924-9.474 2.98C17.309 83.498 0 98.307 0 113.668c0 15.865 18.582 31.778 46.812 41.427a145.52 145.52 0 0 0 6.921 2.165a167.467 167.467 0 0 0-2.01 9.138c-5.354 28.2-1.173 50.591 12.134 58.266c13.744 7.926 36.812-.22 59.273-19.855a145.567 145.567 0 0 0 5.342-4.923a168.064 168.064 0 0 0 6.92 6.314c21.758 18.722 43.246 26.282 56.54 18.586c13.731-7.949 18.194-32.003 12.4-61.268a145.016 145.016 0 0 0-1.535-6.842c1.62-.48 3.21-.974 4.76-1.488c29.348-9.723 48.443-25.443 48.443-41.52c0-15.417-17.868-30.326-45.517-39.844Zm-6.365 70.984c-1.4.463-2.836.91-4.3 1.345c-3.24-10.257-7.612-21.163-12.963-32.432c5.106-11 9.31-21.767 12.459-31.957c2.619.758 5.16 1.557 7.61 2.4c23.69 8.156 38.14 20.213 38.14 29.504c0 9.896-15.606 22.743-40.946 31.14Zm-10.514 20.834c2.562 12.94 2.927 24.64 1.23 33.787c-1.524 8.219-4.59 13.698-8.382 15.893c-8.067 4.67-25.32-1.4-43.927-17.412a156.726 156.726 0 0 1-6.437-5.87c7.214-7.889 14.423-17.06 21.459-27.246c12.376-1.098 24.068-2.894 34.671-5.345a134.17 134.17 0 0 1 1.386 6.193ZM87.276 214.515c-7.882 2.783-14.16 2.863-17.955.675c-8.075-4.657-11.432-22.636-6.853-46.752a156.923 156.923 0 0 1 1.869-8.499c10.486 2.32 22.093 3.988 34.498 4.994c7.084 9.967 14.501 19.128 21.976 27.15a134.668 134.668 0 0 1-4.877 4.492c-9.933 8.682-19.886 14.842-28.658 17.94ZM50.35 144.747c-12.483-4.267-22.792-9.812-29.858-15.863c-6.35-5.437-9.555-10.836-9.555-15.216c0-9.322 13.897-21.212 37.076-29.293c2.813-.98 5.757-1.905 8.812-2.773c3.204 10.42 7.406 21.315 12.477 32.332c-5.137 11.18-9.399 22.249-12.634 32.792a134.718 134.718 0 0 1-6.318-1.979Zm12.378-84.26c-4.811-24.587-1.616-43.134 6.425-47.789c8.564-4.958 27.502 2.111 47.463 19.835a144.318 144.318 0 0 1 3.841 3.545c-7.438 7.987-14.787 17.08-21.808 26.988c-12.04 1.116-23.565 2.908-34.161 5.309a160.342 160.342 0 0 1-1.76-7.887Zm110.427 27.268a347.8 347.8 0 0 0-7.785-12.803c8.168 1.033 15.994 2.404 23.343 4.08c-2.206 7.072-4.956 14.465-8.193 22.045a381.151 381.151 0 0 0-7.365-13.322Zm-45.032-43.861c5.044 5.465 10.096 11.566 15.065 18.186a322.04 322.04 0 0 0-30.257-.006c4.974-6.559 10.069-12.652 15.192-18.18ZM82.802 87.83a323.167 323.167 0 0 0-7.227 13.238c-3.184-7.553-5.909-14.98-8.134-22.152c7.304-1.634 15.093-2.97 23.209-3.984a321.524 321.524 0 0 0-7.848 12.897Zm8.081 65.352c-8.385-.936-16.291-2.203-23.593-3.793c2.26-7.3 5.045-14.885 8.298-22.6a321.187 321.187 0 0 0 7.257 13.246c2.594 4.48 5.28 8.868 8.038 13.147Zm37.542 31.03c-5.184-5.592-10.354-11.779-15.403-18.433c4.902.192 9.899.29 14.978.29c5.218 0 10.376-.117 15.453-.343c-4.985 6.774-10.018 12.97-15.028 18.486Zm52.198-57.817c3.422 7.8 6.306 15.345 8.596 22.52c-7.422 1.694-15.436 3.058-23.88 4.071a382.417 382.417 0 0 0 7.859-13.026a347.403 347.403 0 0 0 7.425-13.565Zm-16.898 8.101a358.557 358.557 0 0 1-12.281 19.815a329.4 329.4 0 0 1-23.444.823c-7.967 0-15.716-.248-23.178-.732a310.202 310.202 0 0 1-12.513-19.846h.001a307.41 307.41 0 0 1-10.923-20.627a310.278 310.278 0 0 1 10.89-20.637l-.001.001a307.318 307.318 0 0 1 12.413-19.761c7.613-.576 15.42-.876 23.31-.876H128c7.926 0 15.743.303 23.354.883a329.357 329.357 0 0 1 12.335 19.695a358.489 358.489 0 0 1 11.036 20.54a329.472 329.472 0 0 1-11 20.722Zm22.56-122.124c8.572 4.944 11.906 24.881 6.52 51.026c-.344 1.668-.73 3.367-1.15 5.09c-10.622-2.452-22.155-4.275-34.23-5.408c-7.034-10.017-14.323-19.124-21.64-27.008a160.789 160.789 0 0 1 5.888-5.4c18.9-16.447 36.564-22.941 44.612-18.3ZM128 90.808c12.625 0 22.86 10.235 22.86 22.86s-10.235 22.86-22.86 22.86s-22.86-10.235-22.86-22.86s10.235-22.86 22.86-22.86Z"></path></svg>

backend/client/src/index.css

@@ -0,0 +1,68 @@
+:root {
+  font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
+  line-height: 1.5;
+  font-weight: 400;
+
+  color-scheme: light dark;
+  color: rgba(255, 255, 255, 0.87);
+  background-color: #242424;
+
+  font-synthesis: none;
+  text-rendering: optimizeLegibility;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+a {
+  font-weight: 500;
+  color: #646cff;
+  text-decoration: inherit;
+}
+a:hover {
+  color: #535bf2;
+}
+
+body {
+  margin: 0;
+  display: flex;
+  place-items: center;
+  min-width: 320px;
+  min-height: 100vh;
+}
+
+h1 {
+  font-size: 3.2em;
+  line-height: 1.1;
+}
+
+button {
+  border-radius: 8px;
+  border: 1px solid transparent;
+  padding: 0.6em 1.2em;
+  font-size: 1em;
+  font-weight: 500;
+  font-family: inherit;
+  background-color: #1a1a1a;
+  cursor: pointer;
+  transition: border-color 0.25s;
+}
+button:hover {
+  border-color: #646cff;
+}
+button:focus,
+button:focus-visible {
+  outline: 4px auto -webkit-focus-ring-color;
+}
+
+@media (prefers-color-scheme: light) {
+  :root {
+    color: #213547;
+    background-color: #ffffff;
+  }
+  a:hover {
+    color: #747bff;
+  }
+  button {
+    background-color: #f9f9f9;
+  }
+}

backend/client/src/main.jsx

@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.jsx'
+
+createRoot(document.getElementById('root')).render(
+  <StrictMode>
+    <App />
+  </StrictMode>,
+)

backend/client/vite.config.js

@@ -0,0 +1,7 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [react()],
+})

backend/config.py

@@ -0,0 +1,11 @@
+import os
+from datetime import timedelta
+
+class Config:
+    SECRET_KEY = 'your-secret-key-change-in-production'
+    SQLALCHEMY_DATABASE_URI = 'mysql+pymysql://root:@localhost/harga_komoditas'
+    SQLALCHEMY_TRACK_MODIFICATIONS = False
+    JWT_SECRET_KEY = 'jwt-secret-key-change-in-production'
+    JWT_ACCESS_TOKEN_EXPIRES = timedelta(hours=1)
+    UPLOAD_FOLDER = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'uploads')
+    ALLOWED_EXTENSIONS = {'csv'}

backend/lstm_prediction_80.ipynb

@@ -0,0 +1,417 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Impor pustaka yang dibutuhkan\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "from tensorflow.keras.models import load_model\n",
+    "from tensorflow.keras.callbacks import EarlyStopping\n",
+    "import glob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setel seed acak untuk reproduktibilitas\n",
+    "np.random.seed(42)\n",
+    "import tensorflow as tf\n",
+    "tf.random.set_seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Tentukan jalur ke data dan ambil semua file CSV\n",
+    "data_path = \"C:/D/projects/BPP PROJECT/bpp-prediction/backend/datasets/\"\n",
+    "csv_files = glob.glob(data_path + \"*.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fungsi untuk memuat dan membersihkan data (menangani nilai yang hilang)\n",
+    "def load_and_clean_data(file_path):\n",
+    "    df = pd.read_csv(file_path, delimiter=';')\n",
+    "    df['Tanggal'] = pd.to_datetime(df['Tanggal'], format='%d/%m/%Y')\n",
+    "    df.set_index('Tanggal', inplace=True)\n",
+    "    \n",
+    "    # Menangani data yang hilang\n",
+    "    df = df.fillna(method='ffill')  # Menggunakan pengisian maju untuk menangani data yang hilang\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "time data \"2021-04-01\" doesn't match format \"%d/%m/%Y\", at position 0. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Memuat semua dataset\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m datasets \u001b[38;5;241m=\u001b[39m {file\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]: \u001b[43mload_and_clean_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m csv_files}\n",
+      "Cell \u001b[1;32mIn[4], line 4\u001b[0m, in \u001b[0;36mload_and_clean_data\u001b[1;34m(file_path)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_and_clean_data\u001b[39m(file_path):\n\u001b[0;32m      3\u001b[0m     df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(file_path, delimiter\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m     df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTanggal\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTanggal\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mm/\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mY\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m     df\u001b[38;5;241m.\u001b[39mset_index(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTanggal\u001b[39m\u001b[38;5;124m'\u001b[39m, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;66;03m# Menangani data yang hilang\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\core\\tools\\datetimes.py:1067\u001b[0m, in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m   1065\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n\u001b[0;32m   1066\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1067\u001b[0m         values \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1068\u001b[0m         result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_constructor(values, index\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mindex, name\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mname)\n\u001b[0;32m   1069\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, (ABCDataFrame, abc\u001b[38;5;241m.\u001b[39mMutableMapping)):\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\core\\tools\\datetimes.py:433\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    431\u001b[0m \u001b[38;5;66;03m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[39;00m\n\u001b[0;32m    432\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 433\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_array_strptime_with_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    435\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m objects_to_datetime64(\n\u001b[0;32m    436\u001b[0m     arg,\n\u001b[0;32m    437\u001b[0m     dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    441\u001b[0m     allow_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    442\u001b[0m )\n\u001b[0;32m    444\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    445\u001b[0m     \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[0;32m    446\u001b[0m     \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python312\\site-packages\\pandas\\core\\tools\\datetimes.py:467\u001b[0m, in \u001b[0;36m_array_strptime_with_fallback\u001b[1;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[0;32m    456\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_array_strptime_with_fallback\u001b[39m(\n\u001b[0;32m    457\u001b[0m     arg,\n\u001b[0;32m    458\u001b[0m     name,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    462\u001b[0m     errors: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m    463\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[0;32m    464\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    465\u001b[0m \u001b[38;5;124;03m    Call array_strptime, with fallback behavior depending on 'errors'.\u001b[39;00m\n\u001b[0;32m    466\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 467\u001b[0m     result, tz_out \u001b[38;5;241m=\u001b[39m \u001b[43marray_strptime\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    468\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m tz_out \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    469\u001b[0m         unit \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mdatetime_data(result\u001b[38;5;241m.\u001b[39mdtype)[\u001b[38;5;241m0\u001b[39m]\n",
+      "File \u001b[1;32mstrptime.pyx:501\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mstrptime.pyx:451\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mstrptime.pyx:583\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime._parse_with_format\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: time data \"2021-04-01\" doesn't match format \"%d/%m/%Y\", at position 0. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this."
+     ]
+    }
+   ],
+   "source": [
+    "# Memuat semua dataset\n",
+    "datasets = {file.split(\"\\\\\")[-1]: load_and_clean_data(file) for file in csv_files}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'datasets' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[6], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Menampilkan informasi dataset\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, df \u001b[38;5;129;01min\u001b[39;00m \u001b[43mdatasets\u001b[49m\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m      3\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mDataset: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      4\u001b[0m     display(df\u001b[38;5;241m.\u001b[39mhead())\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'datasets' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Menampilkan informasi dataset\n",
+    "for name, df in datasets.items():\n",
+    "    print(f\"\\nDataset: {name}\")\n",
+    "    display(df.head())\n",
+    "# Kamus untuk menyimpan scaler untuk setiap dataset\n",
+    "scalers = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fungsi untuk menormalkan data\n",
+    "def normalize_data(df, dataset_name):\n",
+    "    \"\"\"Menormalkan data dan menyimpan scaler untuk digunakan nanti\"\"\"\n",
+    "    scalers[dataset_name] = MinMaxScaler(feature_range=(0, 1))\n",
+    "    df_scaled = scalers[dataset_name].fit_transform(df[['Harga']].values)\n",
+    "    return df_scaled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Menormalkan semua dataset\n",
+    "scaled_datasets = {}\n",
+    "for file, df in datasets.items():\n",
+    "    print(f\"\\nMenormalkan dataset: {file}\")\n",
+    "    scaled_datasets[file] = normalize_data(df, file)\n",
+    "    \n",
+    "    # Menampilkan sampel nilai asli dan nilai terormalisasi\n",
+    "    print(\"Nilai asli:\", df['Harga'].head().values)\n",
+    "    print(\"Nilai terormalisasi:\", scaled_datasets[file][:5].flatten())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "# Simpan semua scaler ke dalam file\n",
+    "scaler_path = \"C:/D/projects/BPP PROJECT/bpp-prediction/backend/scalers_100/\"\n",
+    "os.makedirs(scaler_path, exist_ok=True)\n",
+    "\n",
+    "for dataset_name, scaler in scalers.items():\n",
+    "    joblib.dump(scaler, f\"{scaler_path}{dataset_name}_scaler.pkl\")\n",
+    "\n",
+    "print(\"Semua scaler telah disimpan.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fungsi untuk membuat urutan data untuk LSTM\n",
+    "def create_dataset(data, time_step=60):\n",
+    "    X, y = [], []\n",
+    "    for i in range(len(data) - time_step - 1):\n",
+    "        X.append(data[i:(i + time_step), 0])\n",
+    "        y.append(data[i + time_step, 0])\n",
+    "    return np.array(X), np.array(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Membuat urutan untuk setiap dataset\n",
+    "datasets_X_y = {}\n",
+    "for file, scaled_data in scaled_datasets.items():\n",
+    "    print(f\"\\nMembuat dataset untuk {file}\")\n",
+    "    X, y = create_dataset(scaled_data)\n",
+    "    datasets_X_y[file] = (X, y)\n",
+    "    print(f\"X shape: {X.shape}, y shape: {y.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fungsi untuk membagi data\n",
+    "def split_data(X, y, train_size=0.8):\n",
+    "    train_len = int(len(X) * train_size)\n",
+    "    X_train, X_test = X[:train_len], X[train_len:]\n",
+    "    y_train, y_test = y[:train_len], y[train_len:]\n",
+    "    return X_train, X_test, y_train, y_test\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Membagi data untuk setiap dataset\n",
+    "split_datasets = {}\n",
+    "for file, (X, y) in datasets_X_y.items():\n",
+    "    print(f\"\\nMembagi data untuk {file}\")\n",
+    "    X_train, X_test, y_train, y_test = split_data(X, y)\n",
+    "    split_datasets[file] = (X_train, X_test, y_train, y_test)\n",
+    "    print(f\"Bentuk set pelatihan: {X_train.shape}\")\n",
+    "    print(f\"Bentuk set pengujian: {X_test.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fungsi untuk membangun model LSTM\n",
+    "def build_model(input_shape):\n",
+    "    model = Sequential([\n",
+    "        LSTM(units=50, return_sequences=True, input_shape=input_shape),\n",
+    "        Dropout(0.2),\n",
+    "        LSTM(units=50, return_sequences=False),\n",
+    "        Dropout(0.2),\n",
+    "        Dense(units=1)\n",
+    "    ])\n",
+    "    model.compile(optimizer=Adam(), loss='mean_squared_error')\n",
+    "    return model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Melatih model untuk setiap dataset\n",
+    "model_results = {}\n",
+    "for file, (X_train, X_test, y_train, y_test) in split_datasets.items():\n",
+    "    print(f\"\\nMelatih model untuk {file}\")\n",
+    "    X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
+    "    \n",
+    "    model = build_model((X_train.shape[1], 1))\n",
+    "\n",
+    "    # Penghentian dini untuk mencegah overfitting\n",
+    "    early_stopping = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)\n",
+    "\n",
+    "    history = model.fit(\n",
+    "        X_train, y_train,\n",
+    "        epochs=80,\n",
+    "        batch_size=32,\n",
+    "        validation_split=0.1,\n",
+    "        callbacks=[early_stopping],\n",
+    "        verbose=1\n",
+    "    )\n",
+    "    \n",
+    "    # Menyimpan model\n",
+    "    model.save(f\"C:/D/projects/BPP PROJECT/bpp-prediction/backend/models_100/{file}_model.h5\")\n",
+    "    model_results[file] = model\n",
+    "    \n",
+    "    #  # Menampilkan Hidden State dan Cell State pada timestep terakhir\n",
+    "    # print(\"\\nEvaluasi pada data uji (X_test):\")\n",
+    "    # output, state_h, state_c = model.predict(X_test)\n",
+    "    # print(\"Hidden State terakhir (state_h):\", state_h)\n",
+    "    # print(\"Cell State terakhir (state_c):\", state_c)\n",
+    "    \n",
+    "    # Plot riwayat pelatihan\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.plot(history.history['loss'], label='Loss Pelatihan')\n",
+    "    plt.plot(history.history['val_loss'], label='Loss Validasi')\n",
+    "    plt.title(f'Loss Model untuk {file}')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')\n",
+    "    plt.legend()\n",
+    "    plt.grid(True)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fungsi untuk evaluasi\n",
+    "def evaluate_model(model, X_test, y_test):\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
+    "    mae = mean_absolute_error(y_test, y_pred)\n",
+    "    return rmse, mae\n",
+    "\n",
+    "def denormalize_data(scaled_data, dataset_name):\n",
+    "    return scalers[dataset_name].inverse_transform(scaled_data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Evaluasi model dan membuat prediksi\n",
+    "evaluations = {}\n",
+    "predictions = {}\n",
+    "\n",
+    "for file, model in model_results.items():\n",
+    "    X_test = split_datasets[file][1].reshape(-1, 60, 1)\n",
+    "    y_test = split_datasets[file][3]\n",
+    "    \n",
+    "    # Membuat prediksi\n",
+    "    y_pred_scaled = model.predict(X_test)\n",
+    "    \n",
+    "    # Denormalisasi\n",
+    "    y_pred_rescaled = denormalize_data(y_pred_scaled, file)\n",
+    "    y_test_rescaled = denormalize_data(y_test.reshape(-1, 1), file)\n",
+    "    \n",
+    "    # Menghitung metrik\n",
+    "    rmse, mae = evaluate_model(model, X_test, y_test)\n",
+    "    evaluations[file] = {'RMSE': rmse, 'MAE': mae}\n",
+    "    \n",
+    "    predictions[file] = {\n",
+    "        'y_pred': y_pred_rescaled,\n",
+    "        'y_test': y_test_rescaled\n",
+    "    }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fungsi untuk memplot prediksi\n",
+    "def plot_predictions(file, predictions, evaluations):\n",
+    "    y_pred = predictions[file]['y_pred']\n",
+    "    y_test = predictions[file]['y_test']\n",
+    "    \n",
+    "    plt.figure(figsize=(12, 6))\n",
+    "    plt.plot(y_test, label='Harga Aktual', linewidth=2)\n",
+    "    plt.plot(y_pred, label='Harga Prediksi', linewidth=2)\n",
+    "    plt.title(f'Prediksi Harga BPP untuk {file}\\nRMSE: {evaluations[file][\"RMSE\"]:.2f}, MAE: {evaluations[file][\"MAE\"]:.2f}')\n",
+    "    plt.xlabel('Waktu')\n",
+    "    plt.ylabel('Harga (Rupiah)')\n",
+    "    plt.legend()\n",
+    "    plt.grid(True)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot hasil untuk setiap dataset\n",
+    "for file in predictions.keys():\n",
+    "    plot_predictions(file, predictions, evaluations)\n",
+    "\n",
+    "# Menampilkan hasil evaluasi akhir\n",
+    "print(\"\\nHasil Evaluasi Akhir:\")\n",
+    "for name, metrics in evaluations.items():\n",
+    "    print(f\"\\nMetrik untuk {name}:\")\n",
+    "    print(f\"RMSE: {metrics['RMSE']:.4f}\")\n",
+    "    print(f\"MAE: {metrics['MAE']:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "model.summary()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

backend/models.py

@@ -0,0 +1,44 @@
+from flask_sqlalchemy import SQLAlchemy
+from werkzeug.security import generate_password_hash, check_password_hash
+
+db = SQLAlchemy()
+
+class User(db.Model):
+    __tablename__ = 'users'
+    
+    id = db.Column(db.Integer, primary_key=True)
+    username = db.Column(db.String(80), unique=True, nullable=False)
+    password_hash = db.Column(db.String(255), nullable=False)
+    is_admin = db.Column(db.Boolean, default=False)
+    
+    def set_password(self, password):
+        self.password_hash = generate_password_hash(password)
+        
+    def check_password(self, password):
+        return check_password_hash(self.password_hash, password)
+    
+    def to_dict(self):
+        return {
+            'id': self.id,
+            'username': self.username,
+            'is_admin': self.is_admin
+        }
+
+class DatasetUpload(db.Model):
+    __tablename__ = 'dataset_uploads'
+    
+    id = db.Column(db.Integer, primary_key=True)
+    filename = db.Column(db.String(255), nullable=False)
+    uploaded_by = db.Column(db.Integer, db.ForeignKey('users.id'))
+    upload_date = db.Column(db.DateTime, default=db.func.current_timestamp())
+    commodity_name = db.Column(db.String(100), nullable=False)
+    status = db.Column(db.String(50), default='pending')  # pending, processing, completed, error
+    
+    def to_dict(self):
+        return {
+            'id': self.id,
+            'filename': self.filename,
+            'upload_date': self.upload_date.strftime('%Y-%m-%d %H:%M:%S'),
+            'commodity_name': self.commodity_name,
+            'status': self.status
+        }