[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-185074-113":3,"detail-sidebar-cat-0-id-113":81,"doc-detail-185074-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","data-mining-for-classification-of-new-student-admissions-using-the-c45-algorithm","Data Mining Untuk Klasifikasi Penerimaan Peserta Didik Baru Dengan Menerapkan Algoritma C4.5","","Dokumen ini menjelaskan penerapan algoritma C4.5 dalam proses klasifikasi penerimaan peserta didik baru. Tabel data yang disajikan mencakup informasi seperti nomor urut, nama, alamat, prestasi, zonasi, dan keterangan status penerimaan (DITERIMA/DITOLAK). Berdasarkan data tersebut, algoritma C4.5 digunakan untuk membangun sebuah pohon keputusan yang akan membantu dalam menentukan kriteria penerimaan. Analisis ini memungkinkan identifikasi faktor-faktor kunci yang mempengaruhi keputusan penerimaan, seperti jarak zonasi dan prestasi akademik. Pohon keputusan yang dihasilkan menunjukkan bahwa kriteria 'Zonasi' dan 'Prestasi' adalah prediktor penting dalam klasifikasi penerimaan siswa. Ditemukan pula bahwa jarak zonasi yang lebih dekat dengan sekolah dan adanya sertifikat prestasi akademik meningkatkan peluang siswa untuk diterima. Dokumen ini memberikan contoh konkret bagaimana data mining dapat diaplikasikan untuk meningkatkan efisiensi dan objektivitas dalam proses seleksi akademik.",{"@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 & 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tujuan utama dari penerapan algoritma C4.5 dalam dokumen ini?","Question",{"text":63,"@type":64},"Tujuan utama penerapan algoritma C4.5 adalah untuk melakukan klasifikasi penerimaan peserta didik baru berdasarkan kriteria tertentu seperti zonasi dan prestasi.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Faktor apa saja yang diidentifikasi sebagai prediktor penting dalam klasifikasi penerimaan siswa?",{"text":68,"@type":64},"Berdasarkan analisis pohon keputusan, faktor 'Zonasi' (jarak tempat tinggal ke sekolah) dan 'Prestasi' (sertifikat akademik/non-akademik) diidentifikasi sebagai prediktor penting dalam menentukan status penerimaan siswa.",{"name":70,"@type":61,"acceptedAnswer":71},"Apa kriteria utama yang membuat seorang siswa ditolak?",{"text":72,"@type":64},"Berdasarkan data yang disajikan, siswa dengan jarak zonasi yang jauh (misalnya 247km dan 41km) dan tidak memiliki prestasi relevan atau tidak aktif dalam kontes, cenderung ditolak.","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},185074,1788365646,{"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 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NO | NAMA | ALAMAT | PRESTASI | ZONASI | KETERANGAN |\n| --- | --- | --- | --- | --- | --- |\n| 1 | ABDURRAHMAN HARAHAP | Bekasi | sertifikat\u003Cbr>akademik 2024 | 59km | DITERIMA |\n| 2 | ABI HUDAER | Tangerang | - | 2km | DITERIMA |\n| 3 | ALFARIZA\u003Cbr>FAHRIYANSYAH | Tangerang | - | 2,3km | DITERIMA |\n| 4 | AHMAD ASSUBKI FERDIANSYAH | Tangerang | - | 2km | DITERIMA |\n| 6 | AHMAD HUMAEDI | Tangerang | - | 1,2km | DITERIMA |\n| 7 | AHMAD HUSEN | Tangerang | - | 1,3km | DITERIMA |\n| 9 | ARYA RAMDANI HANAFI | Tangerang | - | 2km | DITERIMA |\n| 10 | AZKA IBNU\u003Cbr>WIJAYA | Tangerang | - | 2km | DITERIMA |\n| 11 | FAIZ FAHMI AL HAKIM | Tangerang | - | 1,6km | DITERIMA |\n| 12 | INDRA BHAKTI MULYANA | Tangerang | - | 2km | DITERIMA |\n| 13 | ISRON EFENDY TANJUNG | Tangerang | - | 2km | DITERIMA |\n| 14 | JULIAN LEVY | Tangerang | - | 2km | DITERIMA |\n| 15 | MUHAMAD AKBARUDIN | Subang | sertifikat nonakademik 2024 | 143km | DITERIMA |\n| 16 | MUHAMAD BINTAN SAMUDRA | Tangerang | - | 2km | DITERIMA |\n| 17 | MUHAMMAD FAJAR SETIAWAN | Tangerang | - | 3km | DITERIMA |\n\n\n| 18 | RAFLY AUFA\u003Cbr>RAMANDA | Tangerang | - | 2km | DITERIMA |\n| --- | --- | --- | --- | --- | --- |\n| 19 | RENDIKA | Lebak | sertifikat nonakademik 2023 | 64km | DITERIMA |\n| 20 | ROBBY RAYHAN RAMADHAN | Tangerang | - | 2km | DITERIMA |\n| 21 | TAUFIQ KAMIL | Brebes | sertifikat\u003Cbr>akademik 2024 | 343km | DITERIMA |\n| 22 | ZACKY AL\u003Cbr>MUBAROK | Tangerang | - | 2km | DITERIMA |\n| 5 | AHMAD BAGUSAPRIYANSYAH | Umbul Baru | sertifikat\u003Cbr>akadaemik 2021 | 247km | DITOLAK |\n| 8 | AHMAD SYAWABIL FALLAH | Jakarta | sertifikat nonakademik 2020 | 41km | DITOLAK |","cbCaijGnjZwJDd2Z","https://ap.wps.com/l/cbCaijGnjZwJDd2Z","pdf",450646,"Indonesian","# Data Peserta Didik\n## Pohon Keputusan","[{\"question\":\"Apa tujuan utama dari penerapan algoritma C4.5 dalam dokumen ini?\",\"answer\":\"Tujuan utama penerapan algoritma C4.5 adalah untuk melakukan klasifikasi penerimaan peserta didik baru berdasarkan kriteria tertentu seperti zonasi dan prestasi.\"},{\"question\":\"Faktor apa saja yang diidentifikasi sebagai prediktor penting dalam klasifikasi penerimaan siswa?\",\"answer\":\"Berdasarkan analisis pohon keputusan, faktor 'Zonasi' (jarak tempat tinggal ke sekolah) dan 'Prestasi' (sertifikat akademik/non-akademik) diidentifikasi sebagai prediktor penting dalam menentukan status penerimaan siswa.\"},{\"question\":\"Apa kriteria utama yang membuat seorang siswa ditolak?\",\"answer\":\"Berdasarkan data yang disajikan, siswa dengan jarak zonasi yang jauh (misalnya 247km dan 41km) dan tidak memiliki prestasi relevan atau tidak aktif dalam kontes, cenderung ditolak.\"}]","Data Mining Untuk Klasifikasi Penerimaan Peserta Didik Baru Dengan Menerapkan Algoritma C4.5 | PDF",8]