[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-128209-113":3,"detail-sidebar-cat-0-id-113":81,"doc-detail-128209-id":128},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},113,"id","evaluation-of-land-use-algorithms-machine-learning-for-classification-and-prediction","Evaluasi Algoritma Penggunaan Lahan - Machine Learning untuk Klasifikasi dan Prediksi","","Pemantauan, perencanaan, dan pengelolaan sumber daya lahan memerlukan data penggunaan lahan yang akurat. Penelitian ini bertujuan mengevaluasi algoritma machine learning untuk klasifikasi dan prediksi penggunaan lahan sekaligus menganalisis perubahan 2002–2032. Studi dilakukan pada Sub DAS Tanralili: klasifikasi memakai Dzetsaka dengan kNN, GMM, RF, dan SVM; prediksi memakai MOLUSCE (CA dikombinasi ANN, LR, WoE, MCE). Penilaian menggunakan overall accuracy dan kappa; kNN, SVM, dan GMM mencapai akurasi tertinggi berturut-turut pada 2002, 2012, dan 2022, serta tren perubahan lahan diamati pada periode 2002–2022 dan 2022–2032.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/id/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/id/document/penelitian-laporan/","Penelitian & Laporan",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/id/document/evaluation-of-land-use-algorithms-machine-learning-for-classification-and-prediction/128209/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/evaluation-of-land-use-algorithms-machine-learning-for-classification-and-prediction/128209.png","ImageObject",300,407,{"name":42,"@type":43},"Skyler","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-19","2026-08-05",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Penelitian ini bertujuan mengevaluasi apa?","Question",{"text":63,"@type":64},"Mengevaluasi algoritma machine learning untuk klasifikasi dan prediksi penggunaan lahan, sekaligus menganalisis perubahan penggunaan lahan tahun 2002–2032.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Bagaimana metode klasifikasi dan prediksi yang digunakan?",{"text":68,"@type":64},"Klasifikasi menggunakan Dzetsaka dengan algoritma kNN, GMM, RF, dan SVM; prediksi menggunakan MOLUSCE dengan model CA yang dikombinasi ANN, LR, WoE, dan MCE.",{"name":70,"@type":61,"acceptedAnswer":71},"Bagaimana model dievaluasi dan algoritma mana yang paling tinggi?",{"text":72,"@type":64},"Evaluasi memakai overall accuracy dan kappa; kNN tertinggi pada 2002, SVM pada 2012, dan GMM pada 2022. SVM memiliki kappa rata-rata tertinggi untuk klasifikasi, sedangkan CA-ANN tertinggi untuk prediksi.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},128209,1785945618,{"code":4,"msg":82,"data":83},"success",[84,89,93,97,101,105,108,112,116,120,124],{"id":85,"doc_module":4,"doc_module_name":25,"category_name":86,"show_sort_weight":87,"slug":88},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":90,"doc_module":4,"doc_module_name":25,"category_name":91,"show_sort_weight":87,"slug":92},48,"Cerita & Novel","story-novel",{"id":94,"doc_module":4,"doc_module_name":25,"category_name":95,"show_sort_weight":87,"slug":96},56,"Gaya Hidup","lifestyle",{"id":98,"doc_module":4,"doc_module_name":25,"category_name":99,"show_sort_weight":87,"slug":100},51,"Komik","comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":87,"slug":104},53,"Layanan Kesehatan","healthcare",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":87,"slug":107},54,"research-report",{"id":109,"doc_module":4,"doc_module_name":25,"category_name":110,"show_sort_weight":87,"slug":111},49,"Sastra","literature",{"id":113,"doc_module":4,"doc_module_name":25,"category_name":114,"show_sort_weight":87,"slug":115},52,"Teknologi","technology",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":118,"show_sort_weight":87,"slug":119},50,"Ujian","exam",{"id":121,"doc_module":4,"doc_module_name":25,"category_name":122,"show_sort_weight":87,"slug":123},57,"Umum","general",{"id":125,"doc_module":4,"doc_module_name":25,"category_name":126,"show_sort_weight":4,"slug":127},181,"Formulir","formulir",{"code":4,"msg":82,"data":129},{"doc_id":79,"user_id":130,"nickname":42,"user_avatar":131,"doc_module":4,"category_id":106,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":137,"language":138,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":12,"update_tm":80,"read_time":142},2336475104042,"https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321","ISSN 0125-1790 (print), ISSN 2540-945X (online) Majalah Geografi Indonesia Vol 39, No 1 (2025) : 19-28  \nDOI: 10.22146/mgi.99150  \n©2025 Fakultas Geografi UGM dan Ikatan Geograf Indonesia (IGI)  \n| Evaluasi Algoritma Penggunaan Lahan | Machine | Learning | untuk | Klasifikasi | \u003Cbr>ARTIKEL PENELITIAN\u003Cbr>dan Prediksi |\n| --- | --- | --- | --- | --- | --- |\n\nFajar Nugraha1*, Dwi Putro Tejo Baskoro1, Suria Darma Tarigan1  \n1 Departemen Ilmu Tanah dan Sumberdaya Lahan, Fakultas Pertanian, Institut Pertanian Bogor, Indonesia  \n* [Email koresponden:](Email koresponden: 23fnugraha@apps.ipb.ac.id)[ ](Email koresponden: 23fnugraha@apps.ipb.ac.id)[23fnugraha@apps.ipb.ac.id](Email koresponden: 23fnugraha@apps.ipb.ac.id)  \nSubmitted: 2024-08-14 Revisions: 2024-11-13 Accepted: 2025-02-17 Published: 2025-02-17 ©2025 Fakultas Geografi UGM dan Ikatan Geograf Indonesia (IGI)  \n©2025 by the authors and Majalah Geografi Indonesia.  \nThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution(CC BY SA) [https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)  \nAbstrak. Pemantauan, perencanaan, dan pengelolaan sumberdaya lahan membutuhkan data penggunaan lahan yang akurat. Banyak penelitiantelah dilakukan mengenai klasifikasi dan prediksi penggunaan lahan. Namun, penelitian terkait penentuan metode klasifikasi dan prediksi yang akurat masih sangat penting. Penelitian ini bertujuan untuk mengevaluasi algoritma machine learning dalam klasifikasi dan prediksi penggunaanlahan serta menganalisis perubahan penggunaan lahan tahun 2002- 2032. Area studi penelitian ini yaitu Sub DAS Tanralili, klasifikasi menggunakan Dzetsaka dengan algoritma seperti kNN, GMM, RF, dan SVM, dan prediksi menggunakan MOLUSCE dengan model CA yang dikombinasi dengan ANN, LR, WoE, dan MCE. Model dievaluasi menggunakan overall accuracy dan kappa, akurasi tertinggi pada tahun 2002, 2012, dan 2022 masing-masing adalah kNN (kappa 0,92), SVM (kappa 0,86), dan GMM (kappa 0,74) . Algoritma SVM memiliki kappa rata-rata tertinggi untuk klasifikasi sebesar 0,83, sedangkan model CA-ANN menunjukkan nilai kappa tertinggi untuk prediksi sebesar 0,65. Pada periode 2002-2022, terjadi penurunan hutan sekunder (4.184,0 ha), pertanian lahan kering (1.259,3 ha), dan badan air (328,0 ha), sedangkan peningkatanpada semakbelukar (5.303,3 ha), sawah (367,0 ha), padang rumput (64,5 ha), dan permukiman (36,5 ha). Pada periode 2022-2032 menunjukkan penurunan hutan sekunder (554,2 ha), sawah (332,6 ha), padang rumput (192,8 ha), dan badan air (33,4 ha), sedangkan peningkatan pada semak belukar (700,9 ha), pertanian lahan kering (401,1 ha), dan permukiman (1,1 ha) .  \nKata kunci: Dzetsaka; Kappa, Molusce; Perencanaan; Sub DAS Tanralili  \nAbstract. Monitoring, planning, and managing land resources require accurate land use data. Many studies have been conducted on land use classification and prediction. However, research related to determining accurate classification and prediction methods is still very important. This study aimed to evaluate machine learning algorithms in land use classification and prediction and analyzed land use change from 2002 to 2032. The study area of this research was the Tanralili Sub Watershed, with classification using Dzetsaka and algorithms such as kNN, GMM, RF, and SVM, and prediction using MOLUSCE with the CA model combined with ANN, LR, WoE, and MCE. The models were evaluated using overall accuracy and kappa; the highest accuracy in 2002, 2012, and 2022 were kNN (kappa 0.92), SVM (kappa 0.86), and GMM (kappa 0.74), respectively. The SVM algorithm had the highest average kappa for classification at 0.83, while the CA-ANN model showed the highest kappa value for prediction at 0.65. In the period 2002-2022, there was a decrease in secondary forests (4,184.0 ha), dry land agriculture (1,259.3 ha), and water bodies (328.0 ha), while an increase in shrubs (5,303.3 ha), rice fields (367","cbCaicolu7Jk6Rqh","https://ap.wps.com/l/cbCaicolu7Jk6Rqh","pdf",11650648,10,"Indonesian","# Pendahuluan\n## Latar belakang kebutuhan data penggunaan lahan\n## Teknologi penginderaan jauh dan SIG\n## Metode klasifikasi berbasis supervised dan unsupervised serta ML","[{\"question\":\"Penelitian ini bertujuan mengevaluasi apa?\",\"answer\":\"Mengevaluasi algoritma machine learning untuk klasifikasi dan prediksi penggunaan lahan, sekaligus menganalisis perubahan penggunaan lahan tahun 2002–2032.\"},{\"question\":\"Bagaimana metode klasifikasi dan prediksi yang digunakan?\",\"answer\":\"Klasifikasi menggunakan Dzetsaka dengan algoritma kNN, GMM, RF, dan SVM; prediksi menggunakan MOLUSCE dengan model CA yang dikombinasi ANN, LR, WoE, dan MCE.\"},{\"question\":\"Bagaimana model dievaluasi dan algoritma mana yang paling tinggi?\",\"answer\":\"Evaluasi memakai overall accuracy dan kappa; kNN tertinggi pada 2002, SVM pada 2012, dan GMM pada 2022. SVM memiliki kappa rata-rata tertinggi untuk klasifikasi, sedangkan CA-ANN tertinggi untuk prediksi.\"}]","Evaluasi Algoritma Penggunaan Lahan - Machine Learning untuk Klasifikasi dan Prediksi | PDF",15]