From e57fd2c1256b4c66d9f7213a16a838a837f97f1e Mon Sep 17 00:00:00 2001
From: Kapten boneng <iwak01bogo@gmail.com>
Date: Tue, 25 Mar 2025 13:15:36 +0700
Subject: [PATCH] Create financial_ai_model.py

Fitur utama dari kode ini:

Mendeteksi anomali transaksi menggunakan model Isolation Forest

Memprediksi tren keuangan menggunakan model Long Short-Term Memory (LSTM)

Memberikan rekomendasi strategi transaksi berdasarkan analisis data


1. Memuat Data Transaksi

Menghasilkan data transaksi dengan jumlah acak untuk mensimulasikan perilaku pengguna.



2. Deteksi Anomali Transaksi dengan Isolation Forest

Model ini mendeteksi transaksi mencurigakan berdasarkan pola outlier.



3. Prediksi Tren Keuangan dengan LSTM

Model LSTM mempelajari pola saldo dan memprediksi saldo masa depan.



4. Strategi Optimalisasi Transaksi

Memberikan rekomendasi transaksi berdasarkan saldo dan biaya rata-rata.
---
 financial_ai_model.py | 76 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 financial_ai_model.py

diff --git a/financial_ai_model.py b/financial_ai_model.py
new file mode 100644
index 0000000..8844469
--- /dev/null
+++ b/financial_ai_model.py
@@ -0,0 +1,76 @@
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from sklearn.ensemble import IsolationForest
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+
+# Load data transaksi (dummy data)
+def load_transaction_data():
+    data = {
+        "timestamp": pd.date_range(start="2025-01-01", periods=500, freq="H"),
+        "amount": np.random.uniform(1, 1000, 500),
+        "fee": np.random.uniform(0.1, 5, 500),
+        "balance": np.cumsum(np.random.uniform(-50, 50, 500))
+    }
+    df = pd.DataFrame(data)
+    return df
+
+# **1. Deteksi Anomali Transaksi dengan Isolation Forest**
+def detect_anomalies(df):
+    model = IsolationForest(n_estimators=100, contamination=0.05, random_state=42)
+    df["anomaly"] = model.fit_predict(df[["amount", "fee", "balance"]])
+    return df
+
+# **2. Prediksi Tren Keuangan dengan LSTM**
+def prepare_lstm_data(df):
+    scaler = MinMaxScaler()
+    df_scaled = scaler.fit_transform(df[["balance"]])
+    
+    X, y = [], []
+    for i in range(len(df_scaled) - 10):
+        X.append(df_scaled[i:i+10])
+        y.append(df_scaled[i+10])
+    
+    return np.array(X), np.array(y), scaler
+
+def build_lstm_model():
+    model = Sequential([
+        LSTM(50, activation='relu', return_sequences=True, input_shape=(10, 1)),
+        Dropout(0.2),
+        LSTM(50, activation='relu'),
+        Dropout(0.2),
+        Dense(1)
+    ])
+    model.compile(optimizer='adam', loss='mse')
+    return model
+
+# **3. Rekomendasi Strategi Transaksi**
+def transaction_recommendation(df):
+    avg_fee = df["fee"].mean()
+    if df["balance"].iloc[-1] < 100:
+        return "Saldo rendah! Kurangi transaksi besar."
+    elif avg_fee > 2:
+        return "Biaya transaksi tinggi. Gunakan strategi batch processing."
+    else:
+        return "Transaksi dalam batas normal."
+
+# **Menjalankan Model**
+df = load_transaction_data()
+df = detect_anomalies(df)
+
+X, y, scaler = prepare_lstm_data(df)
+model = build_lstm_model()
+
+# **Latih Model**
+model.fit(X, y, epochs=10, batch_size=16, verbose=1)
+
+# **Prediksi Tren Masa Depan**
+future_balance = model.predict(X[-1].reshape(1, 10, 1))
+future_balance = scaler.inverse_transform(future_balance.reshape(-1, 1))
+
+# **Output Analisis**
+print("\n🔍 Anomali yang terdeteksi:", df[df["anomaly"] == -1][["timestamp", "amount", "fee"]])
+print("\n📈 Prediksi saldo keuangan selanjutnya:", future_balance[0][0])
+print("\n✅ Rekomendasi transaksi:", transaction_recommendation(df))