Klasifikasi Wilayah WHO Berdasarkan Data HIV/AIDS Global Menggunakan Pendekatan Machine Learning
DOI:
https://doi.org/10.62712/juktisi.v5i1.1140Keywords:
HIV/AIDS Global, Klasifikasi Wilayah WHO, Logistic Regression, Support Vector Machine, GridSearchCVAbstract
Pemetaan epidemiologi HIV/AIDS secara global berdasarkan wilayah World Health Organization (WHO) menjadi salah satu langkah strategis dalam mendukung distribusi bantuan medis yang tepat sasaran. Namun, perbedaan karakteristik data antar-wilayah yang sangat bervariasi membuat proses klasifikasi menjadi tantangan tersendiri. Penelitian ini membandingkan tiga model machine learning, yaitu Logistic Regression, Support Vector Machine (SVM) dasar, dan SVM teroptimasi, untuk mengklasifikasikan data HIV/AIDS ke dalam enam wilayah WHO: Africa, Americas, Eastern Mediterranean, Europe, South-East Asia, dan Western Pacific. Data yang digunakan mencakup estimasi jumlah penderita (nilai median, minimum, dan maksimum) serta persentase cakupan pengobatan antiretroviral (ART coverage %). Proses pra-pemrosesan meliputi analisis data eksploratif, imputasi median untuk menangani nilai kosong, dan normalisasi menggunakan StandardScaler. Evaluasi model dilakukan melalui validasi silang 5-fold dan matriks konfusi. Hasil menunjukkan bahwa data memiliki tingkat tumpang tindih antar-kelas yang cukup tinggi. Logistic Regression menghasilkan akurasi pengujian 39,29% dengan CV Mean 44,05%, sementara SVM teroptimasi dengan parameter C=10, gamma='scale', dan kernel RBF mencapai CV Mean tertinggi sebesar 49,05%. Analisis lebih lanjut mengungkapkan bahwa ART coverage % merupakan fitur paling dominan dalam membedakan karakteristik beban epidemiologi antar-wilayah.
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Copyright (c) 2026 Muhamad Agry Sugiarto, Hafiyyan Putra Pratama
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