KLASIFIKASI SUPPORT VECTOR MACHINE DAN RANDOM FOREST PADA DATA BIOMEDIS : APLIKASI DALAM ANALISIS DATA PENYAKIT DIABETES

Sefri Imanuel Fallo; Moch. Anjas Aprihartha; Jus Prasetya

Authors

Sefri Imanuel Fallo Program Studi Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas San Pedro
Moch. Anjas Aprihartha Program Studi PJJ Informatika Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Jus Prasetya Alumni Magister Matematika Universitas Gadjah Mada

Keywords:

Diabetes, Biomedical, Support Vector Machine, Random forest

Abstract

Penelitian ini bertujuan untuk membandingkan kinerja algoritma Support Vector Machine (SVM) dan Random Forest dalam mengklasifikasikan data biomedis terkait penyakit diabetes. Data yang digunakan mencakup informasi tentang deteksi diabetes pada pasien, dengan variabel-variabel seperti Indeks Massa Tubuh (BMI), tingkat HbA1c, dan tingkat glukosa darah. Hasil penelitian menunjukkan bahwa Random Forest memberikan tingkat akurasi yang lebih tinggi dibandingkan SVM. Hasil prediksi random forest menghasilkan pasien terdeteksi diabetes sebanyak 73 kasus dan pasien tidak terdeteksi diabetes 1127 kasus dengan akurasi 97.17%. Evaluasi model menegaskan bahwa Random Forest mencapai nilai kappa sebesar 0.7967, menandakan kemampuannya dalam memprediksi penyakit diabetes dengan lebih efektif. Hasil ini menyiratkan bahwa Random Forest dapat menjadi pilihan yang lebih optimal dalam memodelkan prediksi penyakit diabetes, terutama ketika mempertimbangkan variabel-variabel yang relevan seperti BMI, tingkat HbA1c, dan tingkat glukosa darah dalam analisisnya.

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