Metode Decision Tree C4.5 untuk Klasifikasi Berat Badan Obesitas
Abstract
Obesitas merupakan masalah kesehatan yang serius dan telah menjadi salah satu faktor utama penyebab penyakit kronis seperti diabetes, penyakit jantung, dan hipertensi. Dalam penelitian ini, metode Decision Tree digunakan untuk melakukan klasifikasi berat badan dan mengidentifikasi faktor-faktor yang berkontribusi terhadap obesitas. Decision tree C4.5 algoritma yang digunakan untuk membuat decision tree (pohon keputusan), pohon keputusan sebuah obyek yang diklasifikasikan pada pohon wajib dites nilai Entropy dan Gainnya. Nilai Entropy tersebut kemudian dihitung nilai Gain masing-masing atribut, kemudian atribut dengan Gain tertinggi dipilih menjadi test atribut dari suatu node. Proses yang dilakukan saat klasifikasi adalah dataset dibagi menjadi dua bagian, data Training dan data Testing, kemudian menghitung Entropy dan Gain, sehingga menghasilkan pohon keputusan Decision Tree C4.5. Dari proses sebelumnya menghasilkan hasil klasifikasi dari data Testing. Berdasarkan hasil klasifikasi di uji menggunakan Confusion Matrix untuk menghitung Accuracy, Precision, Recall, F1_Score. model berhasil mendeteksi sekitar 92% dari semua contoh kelas yang sebenarnya ada dalam dataset dan hasil f1-score adalah 0.89 yang berarti f1-score bernilai 89%,
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DOI: https://doi.org/10.31284/p.snestik.2025.7619
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