Penggunaan Metode Artificial Neural Network dalam Pembuatan Peta Kerentanan Longsor Wilayah Kabupaten Karanganyar
DOI:
https://doi.org/10.32585/modulus.v6i1.4493Kata Kunci:
Artificial Neural Network (ANN), Peta Kerentanan Longsor, Ten-folds cross validationAbstrak
Tanah longsor menjadi bencana alam yang marak terjadi di Indonesia. Selama sepuluh tahun terakhir terdapat 2975 kejadian tanah longsor yang terjadi di Jawa Tengah, di mana 101 kejadian tanah longsor berada di Kabupaten Karanganyar. Penelitian ini bertujuan untuk membuat peta kerentanan longsor pada wilayah Kabupaten Karanganyar. Peta kerentanan akan dibagi menjadi lima kelas, yaitu sangat rendah, rendah, sedang, tinggi dan sangat tinggi dengan menggunakan metode natural breaks (jenk’s). Penelitian ini menggunakan 9 faktor pengondisi longsor yaitu jarak terhadap jalan sekunder, jarak terhadap jalan tersier, slope, topographic wetness index (TWI), elevasi, tata guna lahan (landuse), litologi, normalized difference vegetation index (NDVI), dan hujan. Pembuatan peta dilakukan dengan menggunakan Artificial Neural Network dengan bantuan modul scikit learn dan metode ten-folds cross validation digunakan sebagai metode validasi model yang dihasilkan. Nilai landslide density dihitung pada penelitian ini untuk evaluasi performa dari hasil klasifikasi kerentanan longsor. Parameter machine learning yang digunakan pada penelitian ini adalah hidden layer sizes, activation, maximum iteration dan random state. Performa model Artificial Neural Network yang dihasilkan menggunakan parameter tersebut menunjukkan hasil yang excellent. Nilai AUC yang didapat pada penelitian ini sebesar 0,9140 dengan nilai ten-folds cross validation 0,7444.
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