[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124271-id":3,"doc-seo-124271-113":31,"detail-sidebar-cat-0-id-113":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},124271,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",54,"Penelitian & Laporan","Aplikasi Machine Learning Method pada Pemetaan Kerawanan Tanah Longsor di Kabupaten Karanganyar","Penelitian ini menyusun peta kerawanan tanah longsor untuk wilayah Kabupaten Karanganyar melalui pendekatan machine learning berbasis Voting Classifier Ensemble Technique. Model mengklasifikasikan area menjadi lima kelas kerawanan: sangat rendah, rendah, sedang, tinggi, dan sangat tinggi. Sebagai input digunakan sembilan faktor pengondisi, meliputi jarak terhadap jalan sekunder dan tersier, kemiringan lereng, TWI, elevasi, land use, litologi, NDVI, serta curah hujan. Algoritma diambil dari modul Scikit Learn dan evaluasi menunjukkan AUC 0,9563 yang mendekati 1 sehingga performanya baik untuk memprediksi probabilitas longsor.","Aplikasi Machine Learning Method pada Pemetaan Kerawanan Tanah Longsor di Kabupaten Karanganyar  \nNada Hanifah Putri1*), Raden Harya Dananjaya2), Niken Silmi Surjandari3)  \n1, 2, 3) Program Studi Teknik Sipil, Unversitas Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta 57126, Fax  \n662118; Telp. (0271) 634524.  \n[Email: nada_hanifah_putri@student.uns.ac.id](Email: nada_hanifah_putri@student.uns.ac.id)  \nAbstrak  \nIndonesia 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.  \nKata kunci: longsor, machine learning, Voting Classifier.  \nAbstract  \nIndonesia is in a tropical climate zone which is prone to hydrometeorological disasters. Mapping landslide susceptibility is one of the countermeasures that can reduce the impact of landslides. This study aims to make Karanganyar Regency landslides susceptibility map using machine learning which is classified into five classes, namely very low, low, medium, high, and very high. The method used for making model is Voting Classifier Ensemble Technique. Nine conditioning factors used are distance to secondary and tertiary roads, slope, TWI, elevation, land use, lithology, NDVI, and rain. The machine learning algorithm is taken from Scikit Learn module. The combination of parameters used is the Random Forest method using parameters random_state = 0, n_estimators = 750, criteria = 'entropy', Support Vector Machine method using parameters random_state = 0, Probability = True, gamma = 0. 005, C = 1, K-method Nearest Neighbors uses parameters n_neighbors = 11, weights = 'distance', leaf_size = 20, and Voting Classifier method uses parameters voting = 'soft', weights = [1,1,1] for the other parameters used are set according to the default module. The model obtained has an AUC of 0.9563 which is close to 1 so can be said that model has good performance for predicting landslide stability.  \nKeywords: landslide, machine learning, Voting Classifier.  \nCopyright © 2022 The Author(s)  \nThis is an open access article under the CC-NC-SA license.  \n1. PENDAHULUAN  \nIndonesia berada dalam zona iklim tropis yang memiliki kerawanan tinggi untuk mengalami bencana hidrometeorologi. Jawa Tengah merupakan provinsi dengan kasus tanah longsorterbanyak kedua di pulau Jawa dengan total 2975 kasus dalam kurun waktu tahun 2003 sam-  \npai tahun 2022 (BNPB, 2023) . Pemetaan wilayah kerawanan longsor dapat menjadi salah satu upaya mitigasi yang dapat dilakukan. Bencana longsor dipengaruhi oleh banyak faktor baikfaktor buatan (aktivitas manusia, jarak denganjalan, dll) ataupun faktor alam non geoteknis (kerapatan vegatasi, perubahan curah ","cbCaic2f5wSPN1PN","https://ap.wps.com/l/cbCaic2f5wSPN1PN","pdf",1352348,4,1,12,"Indonesian","id",113,"# Pendahuluan\n## Tujuan penelitian dan konteks bencana hidrometeorologi\n## Kerawanan longsor dan faktor pengondisi\n## Metode ensemble machine learning (Voting Classifier)\n## Perbandingan dengan penelitian terdahulu","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Menyiapkan peta kerawanan tanah longsor Kabupaten Karanganyar menggunakan machine learning yang membagi wilayah menjadi lima tingkat kerawanan.\"},{\"question\":\"Metode machine learning apa yang digunakan dalam pemodelan?\",\"answer\":\"Model menggunakan Voting Classifier Ensemble Technique yang mengombinasikan Random Forest, Support Vector Machine, dan K-Nearest Neighbors.\"},{\"question\":\"Faktor pengondisi apa saja yang digunakan?\",\"answer\":\"Penelitian memakai sembilan faktor, yaitu jarak ke jalan sekunder dan tersier, slope, TWI, elevasi, land use, litologi, NDVI, serta curah hujan.\"}]","Aplikasi Machine Learning Method pada Pemetaan Kerawanan Tanah Longsor di Kabupaten Karanganyar | 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tujuan penelitian ini?","Question",{"text":76,"@type":77},"Menyiapkan peta kerawanan tanah longsor Kabupaten Karanganyar menggunakan machine learning yang membagi wilayah menjadi lima tingkat kerawanan.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Metode machine learning apa yang digunakan dalam pemodelan?",{"text":81,"@type":77},"Model menggunakan Voting Classifier Ensemble Technique yang mengombinasikan Random Forest, Support Vector Machine, dan K-Nearest Neighbors.",{"name":83,"@type":74,"acceptedAnswer":84},"Faktor pengondisi apa saja yang digunakan?",{"text":85,"@type":77},"Penelitian memakai sembilan faktor, yaitu jarak ke jalan sekunder dan tersier, slope, TWI, elevasi, land use, litologi, NDVI, serta curah 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