Desain dan Implementasi Algoritma Decision Tree untuk Klasifikasi Kategori Obesitas
Keywords:
Decision Tree, Klasifikasi, Obesitas, BMI, Machine LearningAbstract
Perkembangan teknologi machine learning memungkinkan pengklasifikasian data kesehatan secara cepat dan akurat. Penelitian ini bertujuan menerapkan algoritma Decision Tree untuk klasifikasi kategori obesitas berdasarkan variabel tinggi badan, berat badan, dan Indeks Massa Tubuh (BMI). Dataset terdiri dari 200 sampel individu yang telah dibersihkan dan diberi label kategori obesitas: Underweight, Normal weight, Overweight, dan Obese. Model Decision Tree dioptimasi dengan pembatasan kedalaman (max_depth=5) untuk mengurangi overfitting dan meningkatkan kemampuan generalisasi. Evaluasi menggunakan data testing menghasilkan akurasi 99,5%, sementara rata-rata cross-validation 5-fold mencapai 0,995. Analisis feature importance menunjukkan bahwa BMI merupakan variabel dominan dalam klasifikasi, sedangkan tinggi badan dan berat badan tidak memberikan kontribusi tambahan. Visualisasi pohon keputusan memperlihatkan jalur klasifikasi yang jelas dan mudah diinterpretasikan, mendukung implementasi model sebagai Decision Support System (DSS) untuk identifikasi awal risiko obesitas. Penelitian ini menegaskan efektivitas Decision Tree dalam klasifikasi kategori obesitas dan konsistensinya dengan literatur sebelumnya, sekaligus memberikan dasar untuk pengembangan model yang lebih kompleks dengan menambahkan fitur pola makan, aktivitas fisik, atau algoritma ensemble.
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