Penggunaan Metode Machine Learning Random Forest untuk Prediksi Longsor pada Kabupaten Karanganyar
DOI:
https://doi.org/10.32585/modulus.v5i2.4489Kata Kunci:
longsor, Random Forest, ten-folds cross validationAbstrak
Tanah longsor adalah salah satu bencana alam yang banyak terjadi di Indonesia terutama Provinsi Jawa Tengah, dengan salah satu daerah yang memiliki kerawanan longsor yang cukup tinggi adalah Kabupaten Karanganyar. Penelitian ini dilakukan untuk menyediakan informasi mengenai kerawanan longsor wilayah Kabupaten Karanganyar dalam suatu bentuk peta yang nantinya dapat dijadikan sebagai sumber tinjauan informasi yang detail dalam upaya mitigasi bencana. Penelitian ini akan memertimbangkan sembilan faktor pengondisi longsor, yaitu jarak terhadap jalan sekunder dan tersier, elevasi, slope, Topographic Wetness Index (TWI), tataguna lahan, litologi, Normalized Difference Vegetation Index (NDVI), dan hujan. Penyusunan peta kerawanan longsor dilakukan menggunakan machine learning dengan metode Random Forest pada pengaturan parameter default dengan bantuan modul Scikit Learn. Validasi model dilakukan menggunakan metode ten-folds cross validation. Hasil prediksi longsor selanjutnya diklasifikasikan men-jadi lima kelas kerawanan longsor menggunakan metode Natural Breaks (Jenk’s) yang performanya akan dievaluasi dengan nilai landslide density. Hasil penelitian ini menunjukkan bahwa metode machine learn-ing Random Forest dapat digunakan untuk memetakan wilayah kerawanan longsor pada Kabupaten Ka-ranganyar. Model yang dihasilkan mampu mengklasifikasikan seluruh wilayah kerawanan longsor dengan nilai AUC mencapai 0,9678, serta menunjukkan hasil klasifikasi yang baik ditandai dengan semakin meningkatnya nilai landslide density pada kelas kerawanan yang semakin tinggi.Unduhan
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