PENINGKATAN KINERJA KLASIFIKASI DIABETES MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DENGAN KERNEL RADIAL BASIS FUNCTION (RBF)

Hasim As'ari Hasim; Kusrini

doi:10.32699/zpgtg048

Authors

Hasim As'ari Hasim Universitas Amikom Yogyakarta
Kusrini Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.32699/zpgtg048

Keywords:

Diabetes Mellitus, SVM-RBF, ADASYN, Machine Learning, Klasifikasi, Preprocessing Data

Abstract

Diabetes Mellitus merupakan salah satu penyakit kronis dengan prevalensi yang terus meningkat secara global. Deteksi dini terhadap penyakit ini sangat penting guna mengurangi risiko komplikasi yang lebih parah. Penelitian ini bertujuan untuk mengevaluasi performa algoritma Support Vector Machine (SVM) dengan kernel Radial Basis Function (RBF) dalam mengklasifikasikan penderita diabetes berdasarkan data medis. Untuk meningkatkan performa model, diterapkan berbagai tahapan preprocessing seperti normalisasi dengan StandardScaler, pembangkitan fitur non-linear dengan PolynomialFeatures, seleksi fitur dengan SelectKBest, serta penyeimbangan kelas menggunakan ADASYN. Dataset yang digunakan adalah Pima Indians Diabetes dari Kaggle, yang memiliki permasalahan ketidakseimbangan kelas. Hasil evaluasi menunjukkan bahwa model mampu mencapai nilai akurasi sebesar 76,6% dan nilai ROC AUC sebesar 0,861. Temuan ini menunjukkan bahwa pendekatan berbasis machine learning dengan pipeline yang tepat dapat menjadi solusi yang andal untuk mendukung deteksi dini Diabetes Mellitus secara otomatis.

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