Evaluasi Kinerja Algoritma Machine Learning SVM dan KNN pada Klasifikasi Penyakit Ginjal

Dedi Triyanto; Deny Kurniawan; Lita Sari Marita; Ade Christian; Sumanto Sumanto

doi:10.33365/jimasia.v5i2.1477

Dedi Triyanto Universitas Bina Sarana Informatika
Deny Kurniawan Universitas Bina Sarana Informatika
Lita Sari Marita Universitas Bina Sarana Informatika
Ade Christian Universitas Bina Sarana Informatika
Sumanto Sumanto Universitas Bina Sarana Informatika

DOI: https://doi.org/10.33365/jimasia.v5i2.1477

Keywords: Penyakit Ginjal, machine learning, citra ginjal, SVM, KNN

Abstract

Penyakit ginjal, mulai dari penyakit ginjal kronis hingga kondisi yang lebih serius seperti kista, batu ginjal, dan tumor, merupakan masalah kesehatan global yang memerlukan deteksi dini untuk mencegah komplikasi lebih lanjut. Metode diagnosis konvensional masih bergantung pada interpretasi subjektif tenaga medis, sehingga berpotensi menimbulkan ketidakkonsistenan dan keterlambatan penanganan. Oleh karena itu, penelitian ini bertujuan untuk mengevaluasi dan membandingkan kinerja algoritma machine learning Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) dalam mendeteksi penyakit ginjal secara otomatis. Penelitian ini menggunakan dataset penyakit ginjal yang terdiri dari 4.000 data pasien yang terbagi secara seimbang ke dalam empat kelas, yaitu normal, kista, batu ginjal, dan tumor, dengan masing-masing kelas berjumlah 1.000 data. Dataset dibagi menjadi data training dan data testing dengan rasio 80:20. Proses pelatihan dan pengujian model dilakukan menggunakan algoritma SVM dan KNN, dengan evaluasi kinerja berdasarkan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kedua algoritma menghasilkan performa yang sangat tinggi, dengan SVM mencapai akurasi sebesar 99,6% dan KNN mencapai akurasi sebesar 99,8%. Hasil ini menunjukkan bahwa metode machine learning efektif digunakan dalam mendukung deteksi penyakit ginjal. Namun demikian, penelitian lanjutan dengan dataset yang lebih beragam dan data klinis nyata masih diperlukan untuk meningkatkan robustnes dan kemampuan generalisasi model.

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