SISTEM KLASIFIKASI PENYAKIT DIABETES MENGGUNAKAN ALGORITMA RANDOM FOREST BERDASARKAN DATA MEDIS

Hafist Rezallul Fikry; Rudi Kurniawan; Bani Nurhakim; Umi Hayati

Authors

Hafist Rezallul Fikry STMIK IKMI Cirebon, Indonesia
Rudi Kurniawan STMIK IKMI Cirebon, Indonesia
Bani Nurhakim STMIK IKMI Cirebon, Indonesia
Umi Hayati STMIK IKMI Cirebon, Indonesia

Keywords:

Random Forest, Deteksi Dini, Diabetes Mellitus, Data Medis, Klasifikasi, machine learning

Abstract

Penelitian ini membahas pembangunan model klasifikasi untuk deteksi dini diabetes mellitus menggunakan algoritma Random Forest. Diabetes merupakan penyakit kronis dengan prevalensi yang terus meningkat, sehingga deteksi awal menjadi krusial untuk mencegah komplikasi. Pemanfaatan data mining dan machine learning memungkinkan analisis data medis secara lebih akurat dalam mendukung proses diagnosis. Pendekatan penelitian menggunakan metode supervised learning dengan dataset berisi atribut kesehatan seperti kadar glukosa, tekanan darah, BMI, usia, dan variabel medis relevan lainnya. Data diproses melalui tahapan pre-processing, meliputi pembersihan, transformasi, normalisasi, serta pembagian data menjadi 80% data latih dan 20% data uji. Model Random Forest dibangun sebagai algoritma klasifikasi, sementara kinerjanya dievaluasi dengan metrik akurasi, presisi, recall, F1-score, dan confusion matrix. Hasil eksperimen menunjukkan bahwa model mencapai akurasi 96,76%, dengan variasi presisi dan recall antar kelas yang mengindikasikan adanya ketidakseimbangan data dan kompleksitas fitur. Meskipun akurasinya belum optimal, penelitian ini menegaskan bahwa Random Forest tetap potensial sebagai model dasar dalam sistem pendukung keputusan medis. Kinerja model masih dapat ditingkatkan melalui optimasi parameter, penyeimbangan data, serta peningkatan kualitas pre-processing. Pendekatan ini diharapkan menjadi langkah awal pengembangan analitik data untuk deteksi dini diabetes.

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