Praktikum 3

Praktikum 3 kali ini kita akan mencoba menggunakan Keras untuk Regresi, khususnya pada kasus Prediksi Harga Rumah.

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import tensorflow as tf

# Contoh dataset (buat dummy data)
data = pd.DataFrame({
    'luas': [50, 60, 70, 80, 90],
    'harga': [500, 600, 700, 800, 900]
})

X = data[['luas']]
y = data[['harga']]

# Normalisasi
scaler = StandardScaler()
X = scaler.fit_transform(X)
y = scaler.fit_transform(y)

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(1,)),
    tf.keras.layers.Dense(1)
])

model.compile(optimizer='adam', loss='mse')
model.fit(X_train, y_train, epochs=100)

# Evaluasi
print("Prediksi:", model.predict(X_test))

Tugas 4:

• Ubah learning rate.

• Bandingkan hasil loss.

Praktikum berikut akan menggunakan data Boston untuk memprediksi harga rumah.

# MLP regresi (Keras)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam

# 1. Load
data = load_boston()
X = data.data; y = data.target

# 2. Preprocess
scaler = StandardScaler(); Xs = scaler.fit_transform(X)
X_train, X_val, y_train, y_val = train_test_split(Xs, y, test_size=0.2, random_state=42)

# 3. Build model
model = Sequential([
Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
Dense(32, activation='relu'),
Dense(1)
])
model.compile(optimizer=Adam(1e-3), loss='mse', metrics=['mae'])

# 4. Train
h = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=200, batch_size=32, verbose=0)

# 5. Plot
plt.figure(figsize=(10,4))
plt.subplot(1,2,1); plt.plot(h.history['loss'], label='train_loss'); plt.plot(h.history['val_loss'], label='val_loss'); plt.legend(); plt.title('MSE')
plt.subplot(1,2,2); plt.plot(h.history['mae'], label='train_mae'); plt.plot(h.history['val_mae'], label='val_mae'); plt.legend(); plt.title('MAE')
plt.show()

from sklearn.metrics import mean_squared_error
pred = model.predict(X_val)
print('RMSE:', np.sqrt(mean_squared_error(y_val, pred)))