[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-127325-113":53,"doc-detail-127325-id":128},{"code":4,"msg":5,"data":6},0,"success",[7,13,17,21,25,29,33,37,41,45,49],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},55,"Document","Agama & Spiritualitas",60,"religion-spirituality",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":11,"slug":16},48,"Cerita & Novel","story-novel",{"id":18,"doc_module":4,"doc_module_name":9,"category_name":19,"show_sort_weight":11,"slug":20},56,"Gaya Hidup","lifestyle",{"id":22,"doc_module":4,"doc_module_name":9,"category_name":23,"show_sort_weight":11,"slug":24},51,"Komik","comic",{"id":26,"doc_module":4,"doc_module_name":9,"category_name":27,"show_sort_weight":11,"slug":28},53,"Layanan Kesehatan","healthcare",{"id":30,"doc_module":4,"doc_module_name":9,"category_name":31,"show_sort_weight":11,"slug":32},54,"Penelitian & Laporan","research-report",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":11,"slug":36},49,"Sastra","literature",{"id":38,"doc_module":4,"doc_module_name":9,"category_name":39,"show_sort_weight":11,"slug":40},52,"Teknologi","technology",{"id":42,"doc_module":4,"doc_module_name":9,"category_name":43,"show_sort_weight":11,"slug":44},50,"Ujian","exam",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":47,"show_sort_weight":11,"slug":48},57,"Umum","general",{"id":50,"doc_module":4,"doc_module_name":9,"category_name":51,"show_sort_weight":4,"slug":52},181,"Formulir","formulir",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":121,"head_meta":123,"extra_data":125,"updated_unix":127},113,"id","integration-of-machine-learning-and-spatial-analysis-for-predicting-tb-prone-areas-in-north-sumatra-province","Integrasi Machine Learning dan Analisis Spasial untuk Prediksi Wilayah Rawan Tuberkulosis di Provinsi Sumatera Utara","","Tuberkulosis (TBC) masih menjadi tantangan kesehatan masyarakat di Provinsi Sumatera Utara, dipengaruhi tingginya prevalensi di wilayah padat penduduk dan terbatasnya akses layanan kesehatan. Penelitian ini memprediksi wilayah rawan TBC melalui integrasi algoritma machine learning dan analisis spasial. Data sekunder berasal dari SITB, BPS, serta shapefile administratif kabupaten/kota. Prediktor mencakup kepadatan penduduk, status gizi, fasilitas kesehatan, kemiskinan, kualitas hunian, dan cakupan imunisasi dasar. Random Forest mencapai akurasi 90,2% dan menghasilkan peta risiko menggunakan QGIS. Zona merah teridentifikasi di Kota Medan, Kabupaten Deli Serdang, dan Kabupaten Labuhanbatu.",{"@graph":63,"@context":120},[64,81,103],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,72,75,78],{"item":68,"name":69,"@type":70,"position":71},"https://docshare.wps.com","Home","ListItem",1,{"item":73,"name":9,"@type":70,"position":74},"https://docshare.wps.com/id/document/",2,{"item":76,"name":31,"@type":70,"position":77},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/integration-of-machine-learning-and-spatial-analysis-for-predicting-tb-prone-areas-in-north-sumatra-province/127325/",4,{"url":79,"name":59,"@type":82,"image":83,"author":88,"headline":59,"publisher":91,"fileFormat":94,"inLanguage":57,"description":61,"dateModified":95,"datePublished":96,"encodingFormat":94,"isAccessibleForFree":97,"interactionStatistic":98},"DigitalDocument",{"url":84,"@type":85,"width":86,"height":87},"https://docshare.wps.com/thumbnails/integration-of-machine-learning-and-spatial-analysis-for-predicting-tb-prone-areas-in-north-sumatra-province/127325.png","ImageObject",300,407,{"name":89,"@type":90},"Evangeline","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-09-20","2026-08-05",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",9,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116],{"name":107,"@type":108,"acceptedAnswer":109},"Apa tujuan penelitian ini?","Question",{"text":110,"@type":111},"Memprediksi wilayah rawan tuberkulosis dengan mengintegrasikan algoritma machine learning dan analisis spasial.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Data apa saja yang digunakan untuk membangun model?",{"text":115,"@type":111},"Data sekunder dikumpulkan dari SITB, BPS, serta shapefile administratif kabupaten/kota di Provinsi Sumatera Utara.",{"name":117,"@type":108,"acceptedAnswer":118},"Bagaimana model risiko dinilai dan variabel apa yang paling berpengaruh?",{"text":119,"@type":111},"Model Random Forest dievaluasi dengan akurasi 90,2% serta metrik precision, recall, dan F1-score. Variabel paling berpengaruh adalah kepadatan penduduk, kualitas hunian, dan jumlah fasilitas kesehatan.","https://schema.org",{"og:url":79,"og:type":122,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":124,"canonical":79},"index,follow",{"doc_id":126,"site_id":56},127325,1785938297,{"code":4,"msg":5,"data":129},{"doc_id":126,"user_id":130,"nickname":89,"user_avatar":131,"doc_module":4,"category_id":30,"category_name":31,"doc_title":59,"doc_description":61,"doc_content":132,"file_id":133,"file_url":134,"file_type":135,"file_size":136,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":137,"language":138,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":139,"faqs":140,"seo_title":141,"seo_description":61,"update_tm":127,"read_time":142},962085570644,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","INTEGRASI MACHINE LEARNING DAN ANALISIS SPASIAL UNTUK PREDIKSI WILAYAH RAWAN TUBERKULOSIS DI PROVINSI SUMATERA UTARA  \nFanny Ramadhani1, Said Iskandar Al-Idrus2, Dian Septiana 3, Arnita4, Diah Retno Wahyuningrum5  \n1,2) Ilmu Komputer, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Medan Indonesia  \n3,4) Statistik, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Medan Indonesia  \n5) Gizi, Fakultas Teknik, Universitas Negeri Medan Indonesia  \nArticle Info  \nABSTRACT  \nAbstrak  \nTuberkulosis (TBC) masih menjadi masalah kesehatan masyarakat yang signifikandi Provinsi Sumatera Utara. Tingginya prevalensi TBC, terutama di daerah padat penduduk dan terbatasnya akses terhadap layanan kesehatan, menjadi tantanganutama dalam upaya pengendalian penyakit ini.Penelitian ini bertujuan memprediksi wilayah rawan TBC dengan mengintegrasikan algoritma machine learning dan analisis spasial. Data sekunder dikumpulkan dari Sistem Informasi Tuberkulosis Nasional (SITB), Badan Pusat Statistik (BPS), dan shapefile administratifkabupaten/kota di Sumatera Utara. Variabel prediktor meliputi kepadatan penduduk, status gizi, jumlah fasilitas kesehatan, tingkat kemiskinan, kualitashunian, dan cakupan imunisasi dasar lengkap. Model prediksi dikembangkan menggunakan algoritma Random Forest, sedangkan analisis spasial dilakukandengan QGIS untuk menghasilkan peta risiko TBC. Model Random Forest menunjukkan performa yang sangat baik dengan akurasi sebesar 90,2%, precision 86,7%, recall 84,5%, dan F1-score 84,1% . Variabel yang paling berpengaruh terhadap kerawanan TBC adalah kepadatan penduduk, kualitas hunian, dan jumlahfasilitas kesehatan. Peta risiko mengidentifikasi Kota Medan, Kabupaten Deli Serdang, dan Kabupaten Labuhanbatu sebagai zona merah. Analisis spasial lebih lanjut menggunakan Moran’s I (0,47; p \u003C 0,01) dan Getis-Ord Gi* menunjukkanadanya pola klaster signifikan dan hotspot TBC di wilayah tersebut. Integrasi algoritma machine learning dan analisis spasial efektifdalam memprediksi dan memetakan wilayah rawan TBC di Sumatera Utara. Pendekatan ini dapat menjadi dasar perencanaan intervensi kesehatan berbasis bukti dan lokasi, sertadireplikasi untuk penyakit menular lainnya yang memiliki karakteristik spasialserupa.  \nKata kunci: Tuberkulosis, Machine Learning, Random Forest, Spasial, Sumatera Utara  \nAbstract  \nTuberculosis (TB) remains a significant public health issue in North Sumatra Province. The high prevalence of TB, particularly in densely populated areas and regions with limited access to healthcare services, presents major challenges for disease control efforts. This study aims to predict TB-prone areas by integrating machine learning algorithms and spatial analysis. Secondary data were collected from the National Tuberculosis Information System (SITB), the Central Bureau of Statistics (BPS), and administrative shapefiles of districts/cities in North Sumatra. Predictor variables included population density, nutritional status, number of healthcare facilities, poverty rate, housing quality, and basic immunization coverage. The predictive model was developed using the Random Forest algorithm, while spatial analysis was conducted with QGIS to generate TB risk maps. The Random Forest model demonstrated strong performance with an accuracy of 90.2%, precision of 86.7%, recall of 84.5%, and an F1-score of 84.1%. The most influential variables affecting TB risk were population density, housing quality, and availability of healthcare facilities. The risk map identified Medan City, Deli Serdang District, and Labuhanbatu District as high-risk (red zone) areas. Further spatial analysis using Moran’s I (0.47; p \u003C 0.01) and Getis-Ord Gi* revealed significant clustering patterns and TB hotspots in these regions. The integration of machine learning algorithms and spatial analysis proved effective in predicting and mapping TB-prone areas in North Sumatra. This approach can serve  \nas a foundation for evidence-based, loca","cbCaicCrymPKQQxZ","https://ap.wps.com/l/cbCaicCrymPKQQxZ","pdf",563076,12,"Indonesian","# PENDAHULUAN\n## Latar Belakang\n## Urgensi Identifikasi Wilayah Rawan\n# METODOLOGI\n## Sumber Data\n## Variabel Prediktor\n## Pemodelan Random Forest dan Analisis Spasial (QGIS)","[{\"question\":\"Apa tujuan penelitian ini?\",\"answer\":\"Memprediksi wilayah rawan tuberkulosis dengan mengintegrasikan algoritma machine learning dan analisis spasial.\"},{\"question\":\"Data apa saja yang digunakan untuk membangun model?\",\"answer\":\"Data sekunder dikumpulkan dari SITB, BPS, serta shapefile administratif kabupaten/kota di Provinsi Sumatera Utara.\"},{\"question\":\"Bagaimana model risiko dinilai dan variabel apa yang paling berpengaruh?\",\"answer\":\"Model Random Forest dievaluasi dengan akurasi 90,2% serta metrik precision, recall, dan F1-score. Variabel paling berpengaruh adalah kepadatan penduduk, kualitas hunian, dan jumlah fasilitas kesehatan.\"}]","Integrasi Machine Learning dan Analisis Spasial untuk Prediksi Wilayah Rawan Tuberkulosis di Provinsi Sumatera Utara | PDF",18]