Pemilihan Fitur Berbasis Algoritma Metaheuristic untuk Meningkatkan Klasifikasi Tingkat Kesehatan Masyarakat di Pulau Jawa

Khoirunnisa Afandi; M. Habibullah Arief; Muhammad Andryan Wahyu Saputra; Hikmatul Kamila

doi:10.58602/jaiti.v4i2.251

Khoirunnisa Afandi ^{(Corresponding Author)} Universitas Jember
M. Habibullah Arief Universitas Jember
Muhammad Andryan Wahyu Saputra Universitas Jember
Hikmatul Kamila Universitas Jember

DOI: https://doi.org/10.58602/jaiti.v4i2.251

Keywords: seleksi fitur, Metaheuristik, Prediksi, Algoritma, Kesehatan

Abstract

Kompleksitas data kesehatan masyarakat di Pulau Jawa menuntut pemilihan fitur yang tepat agar model prediksi lebih akurat dan efisien. Penelitian ini bertujuan mengembangkan model prediksi tingkat kesehatan masyarakat dengan mengoptimalkan seleksi fitur menggunakan dua algoritma metaheuristik, yaitu Particle Swarm Optimization (PSO) dan Genetic Algorithm (GA). Dataset yang digunakan mencakup 16 indikator sosial, ekonomi, dan kesehatan dari 38 wilayah (29 kabupaten dan 9 kota) di Pulau Jawa tahun 2020. Model Random Forest diterapkan untuk mengevaluasi setiap subset fitur. Hasil menunjukkan bahwa PSO menghasilkan akurasi pengujian tertinggi sebesar 79,1% dengan memilih 7 fitur, mengungguli GA yang mencapai 78% dengan 10 fitur. Fitur-fitur yang secara konsisten terpilih dan berpengaruh signifikan adalah angka harapan hidup, rasio puskesmas, akses air minum layak, pemberian ASI eksklusif, balita gizi kurang, dan angka kesembuhan Covid-19. Temuan ini membuktikan bahwa PSO lebih efektif dalam seleksi fitur dan mampu memberikan dasar pengambilan kebijakan kesehatan berbasis data.

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