Pemetaan Distribusi Kerentanan Penyakit Demam Berdarah di Kota Baubau Menggunakan Algoritma Machine Learning

Rahmat Azul Mizan, Prima Widayani, Nur Mohammad Farda

Abstract


Sejak tahun 2013 hingga tahun 2017 serangan penyakit demam berdarah (DBD) di Kota Baubau terus mengalami peningkatan jumlah angka kesakitan. Laporan Dinas Kesehatan Propinsi Sulawesi Tenggara Tahun 2018 menunjukan fakta bahwa Baubau merupakan kota dengan angka kejadian DBD tertinggi ketiga dari 17 kabupaten/kota lainnya. Pemetaan distribusi tingkat kerentanan wilayah terhadap penyakit DBD merupakan langkah penting dalam mendukung penyusunan strategi penanganan penyakit DBD. Penelitian ini bertujuan memetakan dan mendeskripsikan distribusi tingkat kerentanan lokasi penelitian. Kota Baubau sebagai lokasi penelitian dengan mengambil populasi sebanyak 129 kasus kejadian sepanjang tahun 2015 hingga Februari 2016. Dalam penelitian ini, kami mensimulasikan distribusi kerentanan wilayah terhadap penyakit demam berdarah pada resolusi spasial 30x30 meter. Model dibuat menggunakan dua algoritma machine learning yang cukup kuat dan umum digunakan mencakup support vector machine (SVM) dan random forest (RF) dengan melibatkan sejumlah variabel seperti penggunaan/tutupan lahan, NDVI, BLFEI, LST, curah hujan dan kelembapan tahunan yang diturunkan dari citra Landsat 8 OLI/TIRS dan data iklim BMKG serta BWS. Kemampuan model dinilai menggunakan kurva area under curve-receiver operating characteristic (AUC-ROC). Hasil penelitian menunjukan Kecamatan Batupuaro dan Murhum merupakan kecamatan yang wilayah administrasinya didominasi oleh zona rentan sebesar 92,54% dan 41, 74% dari luas total wilayah masing-masing

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DOI: http://dx.doi.org/10.33772/jagat.v4i2.14664

DOI (PDF): http://dx.doi.org/10.33772/jagat.v4i2.14664.g10405

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