Description

📦 1. Pilih Dataset dari Kaggle

Contoh dataset:
"Iris Dataset":
https://www.kaggle.com/datasets/arshid/iris-flower-dataset

File: Iris.csv

💡 2. Siapkan Google Colab (atau Jupyter Notebook)

Gunakan Google Colab karena bisa langsung konek ke Kaggle dan tidak perlu install lokal.

🔑 3. Akses Dataset dari Kaggle

Langkah:

Kunjungi: https://www.kaggle.com → klik foto profil → "Account"

Scroll ke bawah → klik "Create New API Token"

Akan terunduh file: kaggle.json

from tensorflow import keras
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical

# Muat data MNIST
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Preproses data
x_train = x_train.reshape(60000, 784).astype('float32') / 255
x_test = x_test.reshape(10000, 784).astype('float32') / 255
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)

# Definisikan model
model = keras.Sequential([
   Flatten(input_shape=(784,)),
   Dense(128, activation='relu'),
   Dense(10, activation='softmax')
])

# Kompilasi model
model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])

# Latih model
model.fit(x_train, y_train, epochs=2, batch_size=32)

# Evaluasi model
loss, accuracy = model.evaluate(x_test, y_test)
print('Loss:', loss)
print('Accuracy:', accuracy)

# Import libraries import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from sklearn.preprocessing import StandardScaler

# Load dataset df = pd.read_csv("Iris.csv") df.head()

# Drop kolom yang tidak perlu df.drop(['Id'], axis=1, inplace=True) # Pisahkan fitur dan label X = df.drop('Species', axis=1) y = df['Species'] # Encode label le = LabelEncoder() y_encoded = le.fit_transform(y) # Normalisasi fitur scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Split data X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_encoded, test_size=0.2, random_state=

model = Sequential()
model.add(Dense(8, input_shape=(4,), activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(3, activation='softmax'))  # 3 kelas (Iris-setosa, versicolor, virginica)

# Compile model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
 

import matplotlib.pyplot as plt

# Akurasi
plt.plot(history.history['accuracy'], label='Training Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.title('Model Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

# Loss
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Model Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show()
 

history = model.fit(X_train, y_train, epochs=100, batch_size=5, verbose=1, validation_split=0.2)
loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
print(f"Test Accuracy: {accuracy*100:.2f}%")