From 89ce77b63144adf3dd7010df56d87fbe88a10f40 Mon Sep 17 00:00:00 2001
From: muhamad fais aizat <muhfaisaizat@gmail.com>
Date: Wed, 7 May 2025 17:19:27 +0700
Subject: [PATCH] fix:memperbaiki predictTomat, testingModel dan landingpage

---
 .../__pycache__/predictTomat.cpython-310.pyc  | Bin 8155 -> 8826 bytes
 .../__pycache__/testingModel.cpython-310.pyc  | Bin 4289 -> 4164 bytes
 backend/routes/predictTomat.py                |  97 +++++++++++++-----
 backend/routes/testingModel.py                |  68 ++++++++----
 frontend/src/pages/LandingPage/ViewGrafik.jsx |  16 ++-
 5 files changed, 127 insertions(+), 54 deletions(-)

diff --git a/backend/routes/__pycache__/predictTomat.cpython-310.pyc b/backend/routes/__pycache__/predictTomat.cpython-310.pyc
index 326c47ffa0b9dbd1c30fea12d770f6df0c3bdd63..bf2d30a48648c39f27a34abb5e768bc8575f9f21 100644
GIT binary patch
delta 4670
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diff --git a/backend/routes/__pycache__/testingModel.cpython-310.pyc b/backend/routes/__pycache__/testingModel.cpython-310.pyc
index bbd0e70d4fbf4ace7f26bf27a472559b1d58aac3..71eefe5a236826e49636202d3bbce44cfa8e0881 100644
GIT binary patch
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diff --git a/backend/routes/predictTomat.py b/backend/routes/predictTomat.py
index 132cade7..b3e7431c 100644
--- a/backend/routes/predictTomat.py
+++ b/backend/routes/predictTomat.py
@@ -6,7 +6,7 @@ from sqlalchemy import select, insert, text, join, delete
 from sklearn.svm import SVR
 from sklearn.preprocessing import MinMaxScaler, StandardScaler
 from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_absolute_error, mean_squared_error  
+from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error  
 from config.db import get_db, conn
 from models.index import priceTomat, settingPredict, resultPredict
 from datetime import datetime, timedelta
@@ -21,13 +21,28 @@ predict_router = APIRouter(
 @predict_router.get("/date")
 async def read_data(db: Session = Depends(get_db)):
     try:
-        query = text("SELECT tanggal FROM price_tomat ORDER BY tanggal DESC LIMIT 1;")
-        result = db.execute(query).fetchone()
-        
-        if result:
-            return {"tanggal": result[0]}
-        else:
-            return {"message": "No data found"}
+            query = text("""
+                SELECT 
+                (SELECT pt.tanggal 
+                FROM predict.price_tomat AS pt 
+                JOIN predict.result_predict AS rp ON pt.id = rp.id 
+                ORDER BY rp.id ASC 
+                LIMIT 1 OFFSET 30) AS tanggal_old,
+
+                (SELECT tanggal 
+                FROM predict.price_tomat 
+                ORDER BY tanggal DESC 
+                LIMIT 1) AS tanggal_new;
+            """)
+            result = db.execute(query).fetchone()
+            
+            if result:
+                return {
+                    "tanggal_old": result[0],  
+                    "tanggal_new": result[1]
+                }
+            else:
+                return {"message": "No data found"}
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -76,6 +91,15 @@ def predict_price(db: Session = Depends(get_db)):
         df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_2Hari_Lalu', 'RataRata_Sekarang']]
     )
 
+    X = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_2Hari_Lalu']].values
+    y = df['RataRata_Sekarang'].values
+    ids = df['id'].values
+    tanggal = df['Tanggal'].values
+
+    X_train, X_test, y_train, y_test, id_train, id_test, tanggal_train, tanggal_test = train_test_split(
+        X, y, ids, tanggal, test_size=0.2, shuffle=False
+    )
+
     # Ambil parameter model dari database
     kernel = settings.nama_kernel
     C = float(settings.nilai_c) if settings.nilai_c is not None else 1.0
@@ -110,41 +134,58 @@ def predict_price(db: Session = Depends(get_db)):
     X = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'RataRata_Kemarin', 'RataRata_2Hari_Lalu']].values
     y = df['RataRata_Sekarang'].values
     
-    # Latih model dengan semua data yang tersedia
-    svr.fit(X, y)
+    # # Latih model dengan semua data yang tersedia
+    # svr.fit(X, y)
     
-    # Prediksi harga untuk semua tanggal di masa depan
-    for i in range(len(df)):
-        fitur_input = X[i]
-        prediksi = svr.predict([fitur_input])[0]
-        hasil_prediksi.append(prediksi)
+    # # Prediksi harga untuk semua tanggal di masa depan
+    # for i in range(len(df)):
+    #     fitur_input = X[i]
+    #     prediksi = svr.predict([fitur_input])[0]
+    #     hasil_prediksi.append(prediksi)
+
+    # Latih model dengan data latih
+    svr.fit(X_train, y_train)
+
+    # Prediksi untuk data uji
+    y_pred = svr.predict(X_test)
     
     # Evaluasi Model
-    mae = mean_absolute_error(y, hasil_prediksi)
-    rmse = np.sqrt(mean_squared_error(y, hasil_prediksi))
-    mape = np.mean(np.abs((y - hasil_prediksi) / y)) * 100
-    
-    # Simpan hasil ke database
-    for i in range(len(hasil_prediksi)):
-        id_tomat = df.iloc[i]['id']  # Ambil ID dari tabel price_tomat
-        prediksi_value = float(scaler.inverse_transform([[0, 0, 0, 0, 0, hasil_prediksi[i]]])[0][5])
+    mae = mean_absolute_error(y_test, y_pred)
+    rmse = np.sqrt(mean_squared_error(y_test, y_pred))
+    mape = mean_absolute_percentage_error(y_test, y_pred)  
+
+    jumlah_data_dikirim = 0
+    
+    # Gabungkan hasil prediksi ke data uji
+    for i in range(len(y_pred)):
+        id_tomat = id_test[i]
+        tanggal_pred = tanggal_test[i]
+        hasil = y_pred[i]
+        hasil_asli = y_test[i]
+
+        # Invers hasil prediksi
+        dummy_row = np.zeros((1, 6))  # [0, 0, 0, 0, 0, hasil_prediksi]
+        dummy_row[0][5] = hasil
+        prediksi_asli = float(scaler.inverse_transform(dummy_row)[0][5])
 
-        # Perbarui atau masukkan data baru
         existing = db.execute(select(resultPredict).where(resultPredict.c.id == id_tomat)).fetchone()
         if existing:
             db.execute(
                 resultPredict.update()
                 .where(resultPredict.c.id == id_tomat)
-                .values(hasil_prediksi=prediksi_value)
+                .values(hasil_prediksi=prediksi_asli)
             )
         else:
-            db.execute(insert(resultPredict).values(id=id_tomat, hasil_prediksi=prediksi_value))
+            db.execute(insert(resultPredict).values(id=id_tomat, hasil_prediksi=prediksi_asli))
+
+        jumlah_data_dikirim += 1
 
     db.commit()
     
     return {
         "Kernel": kernel,
         "Evaluasi": { "MAE": mae, "RMSE": rmse, "MAPE": mape },
+         "Jumlah_data_dikirim": jumlah_data_dikirim,
         "Pesan": "Prediksi seluruh data berhasil disimpan ke database"
     }
 
@@ -256,7 +297,7 @@ def get_price_history(
             # 10. Evaluasi Model
             mae = mean_absolute_error(y_test, y_pred)
             rmse = np.sqrt(mean_squared_error(y_test, y_pred))
-            mape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100
+            mape = mape = mean_absolute_percentage_error(y_test, y_pred)
 
 
             # 11. Prediksi 30 Hari ke Depan
@@ -311,7 +352,7 @@ def get_price_history(
 
         else:
             # Ambil data historis jika tanggal bukan yang terbaru
-            start_date = tanggal_input - timedelta(days=30)
+            start_date = tanggal_input - timedelta(days=29)
             end_date = tanggal_input
 
             query = (
diff --git a/backend/routes/testingModel.py b/backend/routes/testingModel.py
index db20b4f2..e65c473d 100644
--- a/backend/routes/testingModel.py
+++ b/backend/routes/testingModel.py
@@ -123,36 +123,60 @@ def predict_price(
     rmse = np.sqrt(mean_squared_error(y_test, y_pred))
     mape = mean_absolute_percentage_error(y_test, y_pred)  
 
-    # Kembalikan skala data
+   # Kembalikan skala data
     df_prediksi = df.iloc[len(X_train):].copy()
+    df_prediksi['Tanggal'] = df_prediksi['Tanggal'].dt.date
     df_prediksi['Harga_Prediksi'] = y_pred
-    df_prediksi[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang', 'Harga_Prediksi']] = scaler.inverse_transform(df_prediksi[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang', 'Harga_Prediksi']])
 
-    # Simpan hasil prediksi
-    hasil_prediksi = df_prediksi[['Tanggal', 'Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang', 'Harga_Prediksi']].to_dict(orient='records')
+    # Daftar kolom yang telah dinormalisasi
+    cols_scaled = ['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']
 
-    # Cek jumlah data bernilai 0 awal
-    zero_data_log = {"Sebelum Preprocessing": (df == 0).sum().to_dict()}
+    # Invers scaling satu per satu kolom berdasarkan mean dan std dari StandardScaler
+    feature_names = scaler.feature_names_in_.tolist()
 
-    # Konversi ke numerik dan hitung 0 setelah konversi
-    df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']] = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']].apply(pd.to_numeric, errors='coerce')
-    zero_data_log["Setelah Konversi Numerik"] = (df == 0).sum().to_dict()
+    # Invers Harga_Prediksi berdasarkan skala 'Harga_Sekarang'
+    index_harga_sekarang = feature_names.index('Harga_Sekarang')
+    mean_sekarang = scaler.mean_[index_harga_sekarang]
+    std_sekarang = np.sqrt(scaler.var_[index_harga_sekarang])
+    df_prediksi['Harga_Prediksi'] = df_prediksi['Harga_Prediksi'] * std_sekarang + mean_sekarang
 
-    # Hapus NaN dan hitung 0 setelah drop NaN
-    df.dropna(inplace=True)
-    zero_data_log["Setelah Drop NaN Pertama"] = (df == 0).sum().to_dict()
+    df_prediksi['Harga_Prediksi'] = df_prediksi['Harga_Prediksi'].round(0)
 
-    # Konversi tanggal
-    df['Tanggal'] = pd.to_datetime(df['Tanggal'], errors='coerce')
-    zero_data_log["Setelah Konversi Tanggal"] = (df == 0).sum().to_dict()
+    # Invers kolom lain yang tersedia di df_prediksi
+    for kolom in cols_scaled:
+        if kolom in df_prediksi.columns:
+            index = feature_names.index(kolom)
+            mean = scaler.mean_[index]
+            std = np.sqrt(scaler.var_[index])
+            df_prediksi[kolom] = df_prediksi[kolom] * std + mean
 
-    # Tambah fitur Harga_2Hari_Lalu
-    df['Harga_2Hari_Lalu'] = df['Harga_Kemarin'].shift(1)
-    zero_data_log["Setelah Tambah Harga_2Hari_Lalu"] = (df == 0).sum().to_dict()
+    # Export atau tampilkan hasil
+    hasil_prediksi = df_prediksi[['Tanggal', 'Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak',
+                                'Harga_Kemarin', 'Harga_Sekarang', 'Harga_Prediksi']].to_dict(orient='records')
 
-    # Drop NaN setelah penambahan fitur dan hitung 0
-    df.dropna(inplace=True)
-    zero_data_log["Setelah Drop NaN Kedua"] = (df == 0).sum().to_dict()
+
+    # # Cek jumlah data bernilai 0 awal
+    # zero_data_log = {"Sebelum Preprocessing": (df == 0).sum().to_dict()}
+
+    # # Konversi ke numerik dan hitung 0 setelah konversi
+    # df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']] = df[['Pasar_Bandung', 'Pasar_Ngunut', 'Pasar_Ngemplak', 'Harga_Kemarin', 'Harga_Sekarang']].apply(pd.to_numeric, errors='coerce')
+    # zero_data_log["Setelah Konversi Numerik"] = (df == 0).sum().to_dict()
+
+    # # Hapus NaN dan hitung 0 setelah drop NaN
+    # df.dropna(inplace=True)
+    # zero_data_log["Setelah Drop NaN Pertama"] = (df == 0).sum().to_dict()
+
+    # # Konversi tanggal
+    # df['Tanggal'] = pd.to_datetime(df['Tanggal'], errors='coerce')
+    # zero_data_log["Setelah Konversi Tanggal"] = (df == 0).sum().to_dict()
+
+    # # Tambah fitur Harga_2Hari_Lalu
+    # df['Harga_2Hari_Lalu'] = df['Harga_Kemarin'].shift(1)
+    # zero_data_log["Setelah Tambah Harga_2Hari_Lalu"] = (df == 0).sum().to_dict()
+
+    # # Drop NaN setelah penambahan fitur dan hitung 0
+    # df.dropna(inplace=True)
+    # zero_data_log["Setelah Drop NaN Kedua"] = (df == 0).sum().to_dict()
 
 
 
@@ -164,7 +188,7 @@ def predict_price(
         "C": C,
         "Gamma": gamma,
         "Epsilon": epsilon,
-        "Zero_Data_Log": zero_data_log,
+        # "Zero_Data_Log": zero_data_log,
         "Data_Asli": data_asli,
         "Hasil_Preprocessing": hasil_preprocessing,
         "Hasil_Normalisasi": hasil_normalisasi,
diff --git a/frontend/src/pages/LandingPage/ViewGrafik.jsx b/frontend/src/pages/LandingPage/ViewGrafik.jsx
index 9e198070..0d588b8f 100644
--- a/frontend/src/pages/LandingPage/ViewGrafik.jsx
+++ b/frontend/src/pages/LandingPage/ViewGrafik.jsx
@@ -1,5 +1,5 @@
 import React, { useState, useEffect } from "react";
-import { format, parseISO, isAfter, startOfDay } from "date-fns";
+import { format, parseISO, isBefore, isAfter, startOfDay } from "date-fns";
 import { CalendarIcon } from "lucide-react";
 import { cn } from "@/lib/utils";
 import { Button } from "@/components/ui/button";
@@ -43,6 +43,8 @@ import { useToast } from '@/hooks/use-toast';
 const ViewGrafik = ({ date, setDate, dataYAxis, setDataYAxis, priceType, setPriceType, chartData, setChartData, setTabelDataAktual, setTabelDataPredict }) => {
     const { toast } = useToast();
     const [dateTerbaru, setDateTerbaru] = useState(null);
+    const [dateTerlama, setDateTerlama] = useState(null);
+    const oldestDate = dateTerlama ? parseISO(dateTerlama) : null;
     const latestDate = dateTerbaru ? parseISO(dateTerbaru) : null;
 
     const handleDateChange = (selectedDate) => setDate(selectedDate);
@@ -52,8 +54,9 @@ const ViewGrafik = ({ date, setDate, dataYAxis, setDataYAxis, priceType, setPric
         try {
             const response = await axios.get(`${API_URL}/predict/date`);
 
-            console.log(response.data.tanggal)
-            setDateTerbaru(response.data.tanggal)
+            console.log(response.data.tanggal_old)
+            setDateTerbaru(response.data.tanggal_new)
+            setDateTerlama(response.data.tanggal_old)
         } catch (error) {
             console.error("Error fetching data", error);
         }
@@ -119,7 +122,12 @@ const ViewGrafik = ({ date, setDate, dataYAxis, setDataYAxis, priceType, setPric
                                         onSelect={handleDateChange}
                                         initialFocus
                                         defaultMonth={latestDate}
-                                        disabled={(day) => latestDate && isAfter(startOfDay(day), latestDate)}
+                                        disabled={(day) =>
+                                            // Nonaktifkan tanggal SETELAH tanggal terbaru
+                                            (latestDate && isAfter(startOfDay(day), latestDate)) ||
+                                            // Nonaktifkan tanggal SEBELUM tanggal terlama
+                                            (oldestDate && isBefore(startOfDay(day), oldestDate))
+                                        }
                                     />
                                 </PopoverContent>
                             </Popover>