[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121523-id":3,"doc-seo-121523-113":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},121523,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",54,"Penelitian & Laporan","Model Deteksi Tutupan Lahan di Kecamatan Gunungsitoli Menggunakan Algoritma Decision Tree Berbasis Machine Learning","Perkembangan teknologi penginderaan jauh mendorong integrasi data penginderaan jauh dengan machine learning untuk deteksi tutupan lahan yang lebih efisien. Penelitian ini bertujuan membangun model algoritma decision tree untuk klasifikasi tutupan lahan menggunakan citra PlanetScope NICFI Level 1 yang diturunkan menjadi indeks spektral NDVI, VARI, SAVI, NDWI, dan GRVI. Pemilihan variabel dinilai melalui Information Gain, Gini Index, serta Gain Ratio. Hasil menunjukkan SAVI dan NDVI menjadi variabel paling informatif, sementara tutupan hutan mendominasi Kecamatan Gunungsitoli.","Model Deteksi Tutupan Lahan di Kecamatan Gunungsitoli Menggunakan Algoritma Decision Tree Berbasis  \nMachine Learning  \nLand Cover Detection Model in Gunungsitoli District Using Decision Tree Algorithm  \nBased on Machine Learning  \nAmati Eltriman Hulu*1, Mizero Alexis2  \n1,2Program Studi Pascasarjana Ilmu Pengelolaan Hutan, Departemen Manajeman Hutan, Fakultas Kehutanan dan Lingkungan, IPB University, Dramaga, Bogor, 16880, Indonesia 2Department of Forestry and Nature Conservation, College of Agriculture, University of Rwanda, P.O. Box 210 Musanze, Rwanda  \n[E-mail : amatieltrimanhulu.com](E-mail : amatieltrimanhulu.com)*1, [mizeroalexis2023@gmail.com](mizeroalexis2023@gmail.com2)[2](mizeroalexis2023@gmail.com2)[ ](mizeroalexis2023@gmail.com2)*Corresponding author  \nReceived 31 May 2025; Revised 9 June 2025; Accepted 19 June 2025  \nAbstrak – Perkembangan teknologi penginderaan jauh semakin berkembang, integrasi data penginderan jauh dan artificial intelligence-machine learning menjadi pendekatan yang sangatefisien dalam mendeteksi tutupan lahan. Penelitian ini bertujuan untuk untuk membangun model algoritma tutupan lahan menggunakan algoritma decision tree. Data yang digunakan yakni Citra PlanetScope NICFI Level 1 yang diturunkan menjadi beberapa indeks spektral yang terdiri atas Normalized Difference Vegetation Index (NDVI), Visible Atmospherically Resistant Index (VARI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), dan Green-Red Vegetation Index (GRVI) . Untuk mengukur setia variabel digunakan Information Gain, Gini Index, dan Gain Ratio. Hasil penelitian menunjukan bahwa SAVI dan NDVI merupakan variabel yang informatif dalam membangun model. Distribusi tutupan lahan di Kecamatan Gunungsitoli didominasi oleh tutupan hutan.  \nKata Kunci – Decision Tree, Machine Learning, Tutupan Lahan, Gunungsitoli  \nAbstract - The development of remote sensing technology is increasingly developing, the integration of remote sensing data and artificial intelligence-machine learning be a very efficient approach in detecting land cover. This study aims to build a land cover algorithm model using the algorithm decision tree. The data used is PlanetScope NICFI Level 1 imagery which is broken down into several spectral indices consisting of Normalized Difference Vegetation Index (NDVI), Visible Atmospherically Resistant Index (VARI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), and Green-Red Vegetation Index (GRVI). Information Gain, Gini Index, and Gain Ratio are used to measure each variable. The results of the study indicate that SAVI and NDVI are informative variables in building the model. The distribution of land cover in Gunungsitoli District is dominated by forest cover.  \nKeywords-Decision Tree, Machine Learning, Land Cover, Gunungsitoli District  \n1. PENDAHULUAN  \nKompleksitas landscape Indonesia yang terdiri dari berbagai tipe tutupan lahan seperti hutan, area pertanian, kawasan terbangun, dan badan air memerlukan pendekatan yang sistematis dan advanced dalam proses deteksinya [1], [2] . Ketersediaan informasi tutupan lahan yang akurat menjadi sangat penting mengingat Indonesia memiliki wilayah yang luas dengan karakteristik geografis yang beragam [3] . Keakuratan dalam deteksi tutupan lahan memiliki implikasi  \nsignifikan terhadap berbagai aspek pembangunan, termasuk perencanaan tata ruang, manajemensumber daya alam, hingga implementasi kebijakan berbasis spatial planning [4] .  \nKepulauan Nias merupakan sebuah gugusan kepulauan yang terletak di lepas pantai barat Provinsi Sumatera Utara, Indonesia, dengan Kota Gunungsitoli sebagai pusat pemerintahan dan kegiatan ekonomi utamanya. Keterisolasian geografis Kepulauan Nias sebagai entitas yang terpisah dari daratan utama Sumatera sering kali berdampak pada tingginya kerentanan terhadapperubahan lingkungan. Sebagai wilayah kepulauan terpencil, Nias sangat bergantung padasumber daya alam lokal yang terbatas dan rentan t","cbCailQ9zbVLZskd","https://ap.wps.com/l/cbCailQ9zbVLZskd","pdf",570029,4,1,11,"Indonesian","id",113,"# Abstrak\n# Kata Kunci\n# Pendahuluan\n## Latar belakang kebutuhan deteksi tutupan lahan\n## Konteks Kepulauan Nias dan kerentanan lingkungan\n## Perkembangan penginderaan jauh dan machine learning\n## Alasan penggunaan algoritma decision tree","[{\"question\":\"Penelitian ini bertujuan membangun model apa dan menggunakan algoritma apa?\",\"answer\":\"Penelitian membangun model deteksi tutupan lahan menggunakan algoritma decision tree berbasis machine learning.\"},{\"question\":\"Data dan indeks spektral apa yang digunakan untuk membentuk model?\",\"answer\":\"Data menggunakan Citra PlanetScope NICFI Level 1, dengan turunan indeks spektral NDVI, VARI, SAVI, NDWI, dan GRVI.\"},{\"question\":\"Bagaimana variabel dalam model dievaluasi dan variabel apa yang paling informatif?\",\"answer\":\"Variabel dievaluasi menggunakan Information Gain, Gini Index, dan Gain Ratio. SAVI dan NDVI dinyatakan sebagai variabel yang paling informatif.\"}]","Model Deteksi Tutupan Lahan di Kecamatan Gunungsitoli Menggunakan Algoritma Decision Tree Berbasis Machine Learning | PDF",1785736079,17,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"land-cover-detection-model-in-gunungsitoli-district-using-a-decision-tree-algorithm-based-on-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@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":20},"https://docshare.wps.com/id/document/land-cover-detection-model-in-gunungsitoli-district-using-a-decision-tree-algorithm-based-on-machine-learning/121523/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-16","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Penelitian ini bertujuan membangun model apa dan menggunakan algoritma apa?","Question",{"text":76,"@type":77},"Penelitian membangun model deteksi tutupan lahan menggunakan algoritma decision tree berbasis machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Data dan indeks spektral apa yang digunakan untuk membentuk model?",{"text":81,"@type":77},"Data menggunakan Citra PlanetScope NICFI Level 1, dengan turunan indeks spektral NDVI, VARI, SAVI, NDWI, dan GRVI.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana variabel dalam model dievaluasi dan variabel apa yang paling informatif?",{"text":85,"@type":77},"Variabel dievaluasi menggunakan Information Gain, Gini Index, dan Gain Ratio. 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