[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-id-113":3,"doc-seo-185645-113":53,"doc-detail-185645-id":132},{"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":125,"head_meta":127,"extra_data":129,"updated_unix":131},113,"id","school-admission-zoning-for-ppdb-using-hybrid-dbscan-and-k-means-clustering-to-map-students-socio-economic-profiles","PPDB Zonasi Berbasis Hybrid Clustering DBSCAN dan K-Means untuk Pemetaan Profil Sosial-Ekonomi Siswa","","Penelitian ini merancang pendekatan hybrid untuk penentuan batas wilayah pada sistem Penerimaan Peserta Didik Baru (PPDB) berbasis zonasi dengan mempertimbangkan kedekatan geografis serta kondisi sosial-ekonomi keluarga. Data 1.091 siswa SMAN 1 Alalak dengan 10 atribut sosial-ekonomi dan koordinat GPS dianalisis menggunakan DBSCAN untuk klasterisasi spasial adaptif dan K-Means untuk pemetaan profil sosial-ekonomi, diperkaya reduksi dimensi PCA. Hasil optimal menghasilkan 3 zona bermakna dan 5 profil sosial-ekonomi; integrasi hybrid membentuk 15 segmen unik untuk pemetaan kebutuhan siswa secara menyeluruh.",{"@graph":63,"@context":124},[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":43,"@type":70,"position":77},"https://docshare.wps.com/id/document/ujian/",3,{"item":79,"name":59,"@type":70,"position":80},"https://docshare.wps.com/id/document/school-admission-zoning-for-ppdb-using-hybrid-dbscan-and-k-means-clustering-to-map-students-socio-economic-profiles/185645/",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/school-admission-zoning-for-ppdb-using-hybrid-dbscan-and-k-means-clustering-to-map-students-socio-economic-profiles/185645.png","ImageObject",300,407,{"name":89,"@type":90},"Đào","Person",{"url":68,"name":92,"@type":93},"DocShare","Organization","application/pdf","2026-10-01","2026-09-02",true,{"@type":99,"interactionType":100,"userInteractionCount":102},"InteractionCounter",{"@type":101},"ViewAction",6,{"@type":104,"mainEntity":105},"FAQPage",[106,112,116,120],{"name":107,"@type":108,"acceptedAnswer":109},"Apa tujuan utama penelitian ini dalam konteks PPDB zonasi?","Question",{"text":110,"@type":111},"Menentukan batas wilayah PPDB berbasis zonasi dengan mempertimbangkan dua dimensi sekaligus: kedekatan geografis dan kondisi sosial-ekonomi keluarga siswa.","Answer",{"name":113,"@type":108,"acceptedAnswer":114},"Bagaimana DBSCAN digunakan untuk membentuk zona geografis?",{"text":115,"@type":111},"DBSCAN digunakan untuk klasterisasi spasial adaptif berbasis kedekatan koordinat GPS, dengan parameter ε=3,0 km dan min_samples=3 menghasilkan tiga zona bermakna serta noise.",{"name":117,"@type":108,"acceptedAnswer":118},"Bagaimana K-Means dan PCA digunakan untuk memetakan profil sosial-ekonomi siswa?",{"text":119,"@type":111},"K-Means dengan K=5 klaster dan reduksi dimensi PCA menjadi 3 komponen menghasilkan lima profil sosial-ekonomi dari Sangat Rendah hingga Tinggi.",{"name":121,"@type":108,"acceptedAnswer":122},"Apa hasil integrasi hybrid DBSCAN dan K-Means yang paling penting?",{"text":123,"@type":111},"Integrasi menghasilkan 15 segmen unik untuk pemetaan kebutuhan siswa secara menyeluruh, dengan segmen prioritas intervensi yaitu 58 siswa di Zona-1 dengan profil Sangat Rendah.","https://schema.org",{"og:url":79,"og:type":126,"og:title":59,"og:site_name":92,"og:description":61},"article",{"robots":128,"canonical":79},"index,follow",{"doc_id":130,"site_id":56},185645,1788368395,{"code":4,"msg":5,"data":133},{"doc_id":130,"user_id":134,"nickname":89,"user_avatar":135,"doc_module":4,"category_id":42,"category_name":43,"doc_title":59,"doc_description":61,"doc_content":136,"file_id":137,"file_url":138,"file_type":139,"file_size":140,"view_count":102,"is_deleted":4,"is_public":71,"is_downloadable":71,"audit_status":71,"page_count":141,"language":142,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":143,"faqs":144,"seo_title":145,"seo_description":61,"update_tm":131,"read_time":146},1374402968488,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","| Info Artikel | Abstrak |\n| --- | --- |\n| Kata Kunci:\u003Cbr>DBSCAN\u003Cbr>Hybrid Clustering K-Means Klasterisasi Spasial Sosial-Ekonomi Zonasi Sekolah | Penentuan batas wilayah dalam sistem Penerimaan Peserta Didik Baru (PPDB) berbasis zonasi menuntut mekanisme yang mampu mengakomodasi dua dimensi sekaligus: kedekatan geografis antara tempat tinggal siswa dengan sekolah, serta kondisi sosialekonomi keluarga yang melatarbelakangi kebutuhan mereka. Penelitian ini merancang pendekatan gabungan antara algoritma DBSCAN (Density-Based Spatial Clustering of Applications with Noise) guna membentuk zona geografis secara adaptif, dan K-Meansuntuk memetakan profil sosial-ekonomi siswa. Data yang digunakan mencakup 1.091 siswa SMAN 1 Alalak dengan 10 atribut sosial-ekonomi beserta koordinat GPS. Hasil optimasi menunjukkan bahwa DBSCAN dengan parameter ε=3,0 km dan min samples=3 menghasilkan tiga zona bermakna dengan 54 titik terpencil (4,9%), Silhouette Score 0,7685, dan Davies-Bouldin Index 0,1447 . K-Means dengan K=5 klaster dan reduksi dimensi PCA 3 komponen menghasilkan lima profil sosial-ekonomi mulai dari Sangat Rendah (62 siswa) hingga Tinggi (459 siswa) dengan Silhouette Score 0,8824 dan Davies-Bouldin Index 0,1866 . Perpaduan hybrid dari kedua algoritma menghasilkan 15 segmen unik yang memungkinkan pemetaan kebutuhan siswa secara menyeluruh. Segmen yang paling memerlukan intervensi adalah 58 siswa di Zona-1 dengan profil Sangat Rendah. Kebaruan penelitian ini terletak pada penyatuanklasterisasi densitas spasial berbasis metrik Haversine dengan segmentasi sosialekonomi berbasis PCA dalam satu kerangka hybrid, sebuah pendekatan yang sebelumnya belum diterapkan di wilayah sungai dan rawa seperti Kalimantan Selatan. |\n| Abstract |  |\n| Keywords:\u003Cbr>DBSCAN Hybrid Clustering K-Means School Zoning Socio-Economic Spatial Clustering | School admission zone delineation (PPDB) requires mechanisms that simultaneously consider two dimensions: geographic proximity between students' residences and schools, as well as the socio-economic background of families. This study designs a hybrid approach combining DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for adaptive geographic zone formation and K-Means for mapping student socio-economic profiles. Data covering 1,091 students of SMAN 1 Alalak with 10 socio-economic attributes and GPS coordinates were used. Optimization results show DBSCAN with parameters ε=3.0 km and min_samples=3 produced three meaningful geographic zones with 54 remote points (4.9%), Silhouette Score 0.7685, and Davies-Bouldin Index 0.1447. K-Means with K=5 clusters and PCA dimensionality reduction to 3 components yielded five socio-economic profiles from Very Low (62 students) to High (459 students) with Silhouette Score 0.8824 and Davies-Bouldin Index 0.1866. The hybrid integration produces 15 unique segments enabling comprehensive student needs mapping. The most critical segment requiring intervention is 58 students in Zone-1 with Very Low profiles. The novelty of this research lies in unifying Haversine-based spatial density clustering with PCA-based socio-economic segmentation in a single hybrid framework an approach not previously implemented in river and swamp geographic contexts such as South Kalimantan.\u003Cbr>JuKSITis licensed under a Creative Commons Attribution-Share Alike 4.0 International License\u003Cbr>|\n\n| Profil Sosial-Ekonomi | Cluster | Jumlah Siswa | Skor Kemiskinan | Karakteristik Utama |\n| --- | --- | --- | --- | --- |\n| Sangat Rendah | C2 | 62 (5,7%) | 7,82 | Penerima KPS/KIP/PIP, penghasilan sangat rendah |\n| Rendah | C3 | 110 (10,1%) | 5,85 | Sebagian penerima bantuan, penghasilan rendah |\n| Menengah | C4 | 30 (2,7%) | 5,80 | Tidak ada bantuan, penghasilanbawah-menengah |\n| Menengah-Atas | C1 | 430 (39,4%) | 3,44 | Non-penerima bantuan, penghasilan menengah |\n| Tinggi | C0 | 459 (42,1%) | 1,56 | Penghasilan tinggi, tanpa bantuan sosial |\n\n| Metode / Konfigurasi | Silhouette\u003Cbr>Score | Davies-Bou","cbCaieHts3wFLkf7","https://ap.wps.com/l/cbCaieHts3wFLkf7","pdf",828787,11,"Indonesian","# Pendahuluan\n## Latar belakang kebutuhan zonasi PPDB\n# Metode Penelitian\n## Hybrid DBSCAN dan K-Means\n## Konfigurasi parameter DBSCAN dan K-Means\n## Reduksi dimensi PCA\n# Data Penelitian\n## Sampel dan atribut sosial-ekonomi serta GPS\n# Hasil dan Evaluasi\n## Klasterisasi geografis dan noise\n## Profil sosial-ekonomi berbasis K-Means dan PCA\n## Integrasi hybrid menghasilkan segmen\n# Pembahasan\n## Segmen prioritas intervensi\n## Kebaruan kerangka hybrid berbasis Haversine dan PCA","[{\"question\":\"Apa tujuan utama penelitian ini dalam konteks PPDB zonasi?\",\"answer\":\"Menentukan batas wilayah PPDB berbasis zonasi dengan mempertimbangkan dua dimensi sekaligus: kedekatan geografis dan kondisi sosial-ekonomi keluarga siswa.\"},{\"question\":\"Bagaimana DBSCAN digunakan untuk membentuk zona geografis?\",\"answer\":\"DBSCAN digunakan untuk klasterisasi spasial adaptif berbasis kedekatan koordinat GPS, dengan parameter ε=3,0 km dan min_samples=3 menghasilkan tiga zona bermakna serta noise.\"},{\"question\":\"Bagaimana K-Means dan PCA digunakan untuk memetakan profil sosial-ekonomi siswa?\",\"answer\":\"K-Means dengan K=5 klaster dan reduksi dimensi PCA menjadi 3 komponen menghasilkan lima profil sosial-ekonomi dari Sangat Rendah hingga Tinggi.\"},{\"question\":\"Apa hasil integrasi hybrid DBSCAN dan K-Means yang paling penting?\",\"answer\":\"Integrasi menghasilkan 15 segmen unik untuk pemetaan kebutuhan siswa secara menyeluruh, dengan segmen prioritas intervensi yaitu 58 siswa di Zona-1 dengan profil Sangat Rendah.\"}]","PPDB Zonasi Berbasis Hybrid Clustering DBSCAN dan K-Means untuk Pemetaan Profil Sosial-Ekonomi Siswa | PDF",17]