[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119119-id":3,"doc-seo-119119-113":31,"detail-sidebar-cat-0-id-113":95},{"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},119119,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",54,"Penelitian & Laporan","Implementasi Machine learning dalam Prediksi Kekeringan Berdasarkan Data Citra Landsat 8 OLI di Purbalingga - 2020-2023","Penelitian ini mengeksplorasi penerapan machine learning untuk memprediksi kekeringan di Kabupaten Purbalingga menggunakan data citra Landsat 8 OLI dari tahun 2020 hingga 2023. Metode XGBoost dan Random Forest dipilih karena mampu memproses data kompleks serta menghasilkan prediksi yang andal. Data penginderaan jauh diintegrasikan dengan indeks vegetasi seperti NDVI, SAVI, dan EVI. Hasil menunjukkan XGBoost mencapai akurasi 86,13% dengan Kappa 82,66%, sedangkan Random Forest 84,62% dengan Kappa 80,78%. NDVI menjadi variabel dominan dalam prediksi kekeringan.","Implementasi Machine learning dalam Prediksi Kekeringan Berdasarkan Data Citra Landsat 8 OLI di Purbalingga (2020-2023)  \nDisusun Oleh :  \nAnita Nilam Agatha Nauli Simamora  \n672017279  \nDibimbing Oleh :  \nSri Yulianto Joko Pasetyo, S.Si., M.Kom  \nPROGRAM STUDI S1 TEKNIK INFORMATIKA  \nFAKULTAS TEKNOLOGI INFORMASI UNIVERSITAS KRISTEN SATYA WACANASALATIGA  \nAbstract  \nThis research explores the application of machine learning to predict drought in Purbalingga Regency using Landsat 8 OLI image data from 2020 to 2023. The XGBoost and Random Forest methods were chosen for their ability to handle complex data and provide accurate predictions. Remote sensing data from Landsat 8 OLI was combined with various vegetation indices such as NDVI, SAVI, and EVI. The analysis results showed that the XGBoost method achieved 86.13% accuracy with a Kappa value of 82.66%, while Random Forest achieved 84.62% accuracy with a Kappa value of 80.78%. NDVI proved to be the dominant variable in drought prediction. The implementation of this model provides accurate and reliable estimates to identify drought conditions, important for planning and risk mitigation. This study shows that the integration of remote sensing data with machine learning algorithms can improve the efficiency of irrigation and land use management.  \nKeywords: Drought, Machine learning, XGBoost, Random Forest, Vegetation Index  \nAbstrak  \nPenelitian ini mengeksplorasi penerapan machine learning untuk memprediksi kekeringan di Kabupaten Purbalingga menggunakan data citra Landsat 8 OLI dari tahun 2020 hingga 2023. Metode XGBoost dan Random Forest dipilih karena kemampuan mereka dalam menangani data kompleks dan memberikanprediksi akurat. Data penginderaan jauh dari Landsat 8 OLI dikombinasikan dengan berbagai indeks vegetasi seperti NDVI, SAVI, dan EVI. Hasil analisis menunjukkan bahwa metode XGBoost mencapai akurasi 86,13% dengan nilai Kappa 82,66%, sedangkan Random Forest mencapai akurasi 84,62% dengan nilai Kappa 80,78% . NDVI terbukti menjadi variabel dominan dalam prediksi kekeringan. Implementasimodel ini memberikan estimasi akurat dan dapat diandalkan untuk mengidentifikasi kondisi kekeringan, penting untuk perencanaan dan mitigasi risiko. Studi ini menunjukkan bahwa integrasi data penginderaanjauh dengan algoritma machine learning dapat meningkatkan efisiensi pengelolaan irigasi dan penggunaan lahan.  \nKata kunci: Kekeringan, Machine learning, XGBoost, Random Forest, Indeks Vegetasi","cbCaigZSkJWSepdo","https://ap.wps.com/l/cbCaigZSkJWSepdo","pdf",711197,2,1,3,"Indonesian","id",113,"# Latar Belakang dan Tujuan\n# Data dan Variabel (Landsat 8 OLI, Indeks Vegetasi)\n# Metode Machine Learning (XGBoost dan Random Forest)\n# Hasil dan Akurasi (Akurasi dan Kappa)\n# Analisis Variabel Dominan (NDVI)\n# Implementasi dan Manfaat","[{\"question\":\"Penelitian ini memprediksi apa dan di mana?\",\"answer\":\"Penelitian memprediksi kekeringan di Kabupaten Purbalingga menggunakan data citra Landsat 8 OLI.\"},{\"question\":\"Metode machine learning apa yang digunakan dan kenapa dipilih?\",\"answer\":\"Metode yang digunakan adalah XGBoost dan Random Forest karena keduanya mampu menangani data kompleks dan memberikan prediksi akurat.\"},{\"question\":\"Bagaimana hasil performa model dan metrik apa yang dipakai?\",\"answer\":\"XGBoost mencapai akurasi 86,13% dengan Kappa 82,66%, sedangkan Random Forest mencapai akurasi 84,62% dengan Kappa 80,78%.\"},{\"question\":\"Indeks vegetasi apa yang paling berpengaruh dalam prediksi kekeringan?\",\"answer\":\"NDVI terbukti menjadi variabel dominan dalam prediksi kekeringan.\"}]","Implementasi Machine learning dalam Prediksi Kekeringan Berdasarkan Data Citra Landsat 8 OLI di Purbalingga - 2020-2023 | PDF",1785722478,5,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":29},"machine-learning-implementation-for-drought-prediction-using-landsat-8-oli-imagery-in-purbalingga-2020-2023","",{"@graph":37,"@context":89},[38,53,68],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,50],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/id/document/","Document",{"item":49,"name":12,"@type":44,"position":22},"https://docshare.wps.com/id/document/penelitian-laporan/",{"item":51,"name":13,"@type":44,"position":52},"https://docshare.wps.com/id/document/machine-learning-implementation-for-drought-prediction-using-landsat-8-oli-imagery-in-purbalingga-2020-2023/119119/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":42,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-17","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Penelitian ini memprediksi apa dan di mana?","Question",{"text":75,"@type":76},"Penelitian memprediksi kekeringan di Kabupaten Purbalingga menggunakan data citra Landsat 8 OLI.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Metode machine learning apa yang digunakan dan kenapa dipilih?",{"text":80,"@type":76},"Metode yang digunakan adalah XGBoost dan Random Forest karena keduanya mampu menangani data kompleks dan memberikan prediksi akurat.",{"name":82,"@type":73,"acceptedAnswer":83},"Bagaimana hasil performa model dan metrik apa yang dipakai?",{"text":84,"@type":76},"XGBoost mencapai akurasi 86,13% dengan Kappa 82,66%, sedangkan Random Forest mencapai akurasi 84,62% dengan Kappa 80,78%.",{"name":86,"@type":73,"acceptedAnswer":87},"Indeks vegetasi apa yang paling berpengaruh dalam prediksi kekeringan?",{"text":88,"@type":76},"NDVI terbukti menjadi variabel dominan dalam prediksi kekeringan.","https://schema.org",{"og:url":51,"og:type":91,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":93,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":96},[97,102,106,110,114,118,120,124,128,132,136],{"id":98,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":103,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":100,"slug":105},48,"Cerita & Novel","story-novel",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":100,"slug":109},56,"Gaya Hidup","lifestyle",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":100,"slug":113},51,"Komik","comic",{"id":115,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":100,"slug":117},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":100,"slug":119},"research-report",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":100,"slug":123},49,"Sastra","literature",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":100,"slug":127},52,"Teknologi","technology",{"id":129,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":100,"slug":131},50,"Ujian","exam",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":100,"slug":135},57,"Umum","general",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":4,"slug":139},181,"Formulir","formulir"]