[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126286-id":3,"doc-seo-126286-113":31,"detail-sidebar-cat-0-id-113":93},{"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},126286,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",54,"Penelitian & Laporan","Pemodelan Spasial Prediksi Longsor di Kecamatan Malalak dan Kecamatan IV Koto, Kabupaten Agam dengan Machine Learning Random Forest","Penelitian ini bertujuan melakukan pemodelan spasial prediksi longsor, mengidentifikasi faktor dominan penyebab longsor, serta menyusun dasar perencanaan mitigasi bencana. Pendekatan yang digunakan adalah statistik multivariat dengan machine learning Random Forest dan validasi model memakai nilai Area Under Curve (AUC) melalui bahasa R. Variabel mencakup lokasi longsor, karakteristik lereng, curah hujan, geologi, tutupan lahan, indeks vegetasi, jenis tanah, bentuklahan, serta jarak dari jalan dan sungai. Hasil memetakan tingkat bahaya ke lima kelas dengan AUC sekitar 0,91.","PEMODELAN SPASIAL PREDIKSI LONGSOR DI KECAMATAN MALALAK DAN KECAMATAN IV KOTO, KABUPATEN AGAM DENGAN MACHINE LEARNING RANDOM FOREST  \nAndika Adityawarman 1 , Triyatno2  \n1.2 Departemen Geografi, Fakultas Ilmu Sosial, Universitas Negeri Padang  \n[andikaadtywrmn@student.unp.ac.id](andikaadtywrmn@student.unp.ac.id)  \nABSTRACT  \nThe objectives of this study are: 1) spatial modeling of landslide prediction, 2) analysis of factors that contribute most to landslide occurrence, and 3) landslide prediction model as a basis for landslide disaster mitigation planning. This study uses a multivariate statistical approach with random forest machine learning, and model validation is performed by calculating the Area Under Curve (AUC) value using the R language. The variables analyzed in this study include landslide locations, slope direction, slope curvature, elevation, slope inclination, rainfall, geology, land cover, vegetation index, soil type, landform, distance from roads, and distance from rivers. The results of the study found 57 landslide locations spread across the study area. The resulting machine learning random forest produced a landslide hazard prediction map with an AUC value of 0, 91, classified into five hazard level categories based on probability values: very low with an area of 5.240, 98 hectares (27, 64%), low with an area of 4.468,25 hectares (23, 56%), moderate with an area of 4,336.05 hectares (22, 86%), high with an area of 3.048, 19 hectares (16, 07%), and very high with an area of 1.870,42 hectares (9, 86%). The largest contributing factors to landslides were slope gradient, rainfall, and distance from roads. Mitigation strategies based on the primary contributing factors to landslides include the construction of retaining walls, soil retention structures, drainage improvements, revegetation of slopes with strong root systems, and regular monitoring.  \nKeywords: Spatial Modeling, Landslide Hazard, Machine Learning, Random Forest, Landslide Mitigation  \nABSTRAK  \nTujuan dari penelitian ini untuk: 1) pemodelan spasial prediksi longsor, 2) analisis faktor yang memiliki kontribusi terbesar terjadinya longsor, dan 3) model prediksi longsor sebagai dasar perencanaan mitigasi bencana longsor. Penelitian ini menggunakan pendekatan statistik multivariat dengan machine learning random forest serta validasi model dilakukan melalui perhitungan nilai Area Under Curve (AUC) menggunakan bahasa R. Variabel yang dianalisis dalam studi ini mencakuptitik kejadian longsor, arah lereng, kelengkungan lereng, elevasi, kemiringan lereng, curah hujan, geologi, tutupan lahan, indeks vegetasi, jenis tanah, bentuklahan, jarak dari jalan, dan jarak dari sungai. Hasil penelitian, ditemukan 57 titik kejadian longsor yang tersebar di wilayah penelitian. Machine learning random forest yang dihasilkan menghasilkan peta prediksi bahaya longsor dengan nilai AUC sebesar 0,9062 yang diklasifikasikan ke dalam lima kategori tingkat bahaya berdasarkan nilai probabilitas, yaitu sangat rendah dengan luas 5.240,98 hektar (27,64%), rendah dengan luas 4.468,25 hektar (23,56%), sedang dengan luas 4.336,05 hektar (22,86%), tinggi dengan luas 3.048,19 hektar (16,07%), dan sangat tinggi dengan luas 1.870,42 hektar (9,86%) . Kontribusi faktor terbesar terjadinya longsor adalahkemiringan lereng, curah hujan, dan jarak dari jalan. Strategi mitigasi berdasarkanfaktor kontribusi terbesar terjadinya longsor meliputi pembangunan dinding penahan tanah, beronjong, peningkatan tata salir, revegetasi lereng berakar kuat, dan pemantauan berkala.  \nKata Kunci: Pemodelan Spasial, Bahaya Longsor, Machine Learning, Random Forest  \nA. Pendahuluan  \nIndonesia merupakan negara yang terletak pada pertemuan tiga lempeng tektonik utama dunia, yaitu Eurasia, Indo-Australia, dan Pasifik. Kondisi ini menyebabkan wilayah Indonesia memiliki tingkat bahaya tinggi terhadap berbagai bencana geologi, salah satunya adalah bencana longsor. Menurut Undang-Undang Nomor 24 Tahun 2007, longsor termasu","cbCair5npVq6XWj8","https://ap.wps.com/l/cbCair5npVq6XWj8","pdf",1207054,9,1,24,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang kebencanaan longsor di Indonesia\n## Gambaran wilayah penelitian: Kabupaten Agam, Malalak, dan IV Koto\n## Urgensi pemodelan prediksi longsor berbasis spasial\n## Metode Random Forest dan konsep validasi AUC","[{\"question\":\"Apa tujuan utama penelitian pemodelan spasial prediksi longsor ini?\",\"answer\":\"Penelitian ini bertujuan memodelkan prediksi longsor secara spasial, menganalisis faktor yang paling berkontribusi terhadap kejadian longsor, serta menyediakan model sebagai dasar perencanaan mitigasi.\"},{\"question\":\"Metode apa yang digunakan dan bagaimana validasi model dilakukan?\",\"answer\":\"Penelitian menggunakan statistik multivariat dengan machine learning Random Forest. Validasi dilakukan dengan menghitung nilai Area Under Curve (AUC) menggunakan bahasa R.\"},{\"question\":\"Variabel apa saja yang dianalisis untuk memprediksi bahaya longsor?\",\"answer\":\"Variabel mencakup titik kejadian longsor, arah dan kelengkungan lereng, elevasi dan kemiringan lereng, curah hujan, geologi, tutupan lahan, indeks vegetasi, jenis tanah, bentuklahan, serta jarak dari jalan dan jarak dari sungai.\"}]","Pemodelan Spasial Prediksi Longsor di Kecamatan Malalak dan Kecamatan IV Koto, Kabupaten Agam dengan Machine Learning Random Forest | PDF",1785904264,37,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"spatial-modeling-of-landslide-prediction-in-malalak-and-iv-koto-districts-agam-regency-using-random-forest-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/spatial-modeling-of-landslide-prediction-in-malalak-and-iv-koto-districts-agam-regency-using-random-forest-machine-learning/126286/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-17","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Apa tujuan utama penelitian pemodelan spasial prediksi longsor ini?","Question",{"text":77,"@type":78},"Penelitian ini bertujuan memodelkan prediksi longsor secara spasial, menganalisis faktor yang paling berkontribusi terhadap kejadian longsor, serta menyediakan model sebagai dasar perencanaan mitigasi.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Metode apa yang digunakan dan bagaimana validasi model dilakukan?",{"text":82,"@type":78},"Penelitian menggunakan statistik multivariat dengan machine learning Random Forest. Validasi dilakukan dengan menghitung nilai Area Under Curve (AUC) menggunakan bahasa R.",{"name":84,"@type":75,"acceptedAnswer":85},"Variabel apa saja yang dianalisis untuk memprediksi bahaya longsor?",{"text":86,"@type":78},"Variabel mencakup titik kejadian longsor, arah dan kelengkungan lereng, elevasi dan kemiringan lereng, curah hujan, geologi, tutupan lahan, indeks vegetasi, jenis tanah, bentuklahan, serta jarak dari jalan dan jarak dari sungai.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]