Aplikasi Machine Learning Method pada Pemetaan Kerawanan Tanah Longsor di Kabupaten Karanganyar
DOI:
https://doi.org/10.32585/modulus.v6i1.4490Keywords:
longsor, machine learning, Voting ClassifierAbstract
Indonesia berada dalam zona iklim tropis yang rawan untuk mengalami bencana hidrometeorologi. Pemetaan kerawanan longsor merupakan salah satu upaya mitigasi yang dapat dilakukan untuk mengurangi dampak dari bencana tanah longsor. Penelitian ini bertujuan untuk membuat peta kerawanan longsor wilayah Kabupaten Karanganyar menggunakan machine learning yang diklasifikasikan menjadi lima kelas yaitu sangat rendah, rendah, sedang, tinggi, dan sangat tinggi. Metode yang digunakan untuk pembuatan model adalah Voting Classifier Ensemble Technique. Sembilan faktor pengondisi yang digunakan yaitu jarak terhadap jalan sekunder dan tersier, slope, TWI, elevasi, land use, litologi, NDVI, serta curah hujan. Algoritma machine learning didapatkan dari modul Scikit Learn. Kombinasi parameter yang digunakan yaitu pada metode Random Forest menggunakan parameter random_state = 0, n_estimators = 750, criterion = 'entropy', metode Support Vector Machine menggunakan parameter random_state = 0, Probability = True, gamma = 0.005, C = 1, metode K-Nearest Neighbors menggunakan parameter n_neighbors = 11, weights = 'distance', leaf_size = 20, dan metode Voting Classifier menggunakan parameter voting = 'soft', weights = [1,1,1] untuk parameter lain yang digunakan diatur sesuai dengan default modul. Model yang didapatkan memiliki AUC sebesar 0,9563 yang mendekati 1 sehingga dapat dikatakan bahwa model yang dimiliki performa yang baik untuk melakukan prediksi probabilitas longsor.
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Copyright (c) 2024 Nada Hanifah Putri, Raden Harya Dananjaya, Niken Silmi Surjandari
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
