INTEGRASI MACHINE LEARNING DAN ANALISIS SPASIAL UNTUK PREDIKSI WILAYAH RAWAN TUBERKULOSIS DI PROVINSI SUMATERA UTARA

Fanny Ramadhani; Said Iskandar Al-Idrus; Dian Septiana; Arnita Arnita; Diah Retno Wahyuningrum

doi:10.46576/djtechno.v6i2.6840

INTEGRASI MACHINE LEARNING DAN ANALISIS SPASIAL UNTUK PREDIKSI WILAYAH RAWAN TUBERKULOSIS DI PROVINSI SUMATERA UTARA

Fanny Ramadhani, Said Iskandar Al-Idrus, Dian Septiana, Arnita Arnita, Diah Retno Wahyuningrum

Abstract

Tuberkulosis (TBC) masih menjadi masalah kesehatan masyarakat yang serius di Provinsi Sumatera Utara. Prevalensi tinggi terutama di daerah padat penduduk dan terbatasnya akses layanan kesehatan menjadi tantangan utama dalam pengendalian TBC. Penelitian ini bertujuan untuk memprediksi wilayah rawan TBC dengan mengintegrasikan algoritma machine learning dan analisis spasial. Data sekunder diperoleh dari Sistem Informasi Tuberkulosis Nasional (SITB), Badan Pusat Statistik (BPS), dan shapefile administrasi wilayah kabupaten/kota di Sumatera Utara. Variabel prediktor meliputi kepadatan penduduk, status gizi, jumlah fasilitas kesehatan, tingkat kemiskinan, kualitas hunian, dan cakupan imunisasi. Model dikembangkan menggunakan algoritma Random Forest, sementara analisis spasial dilakukan menggunakan QGIS untuk menghasilkan peta risiko TBC. Hasil model menunjukkan akurasi sebesar 86,2% dengan variabel paling berpengaruh adalah kepadatan penduduk, kualitas hunian, dan akses fasilitas kesehatan. Peta risiko yang dihasilkan mengidentifikasi wilayah seperti Kota Medan, Deli Serdang, dan Labuhanbatu sebagai zona merah. Hasil penelitian ini diharapkan menjadi dasar perencanaan intervensi kesehatan yang lebih tepat sasaran di Sumatera Utara.

Keywords

Tuberkulosis; Machine Learning; Random Forest; Spasial; Sumatera Utara

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DOI: https://doi.org/10.46576/djtechno.v6i2.6840