[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-291065-113":3,"detail-sidebar-cat-0-id-113":80,"doc-detail-291065-id":127},{"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":73,"head_meta":75,"extra_data":77,"updated_unix":79},113,"id","development-of-a-credit-card-customer-default-prediction-website-using-machine-learning-algorithm","Pengembangan Website Prediksi Gagal Bayar Nasabah Kartu Kredit menggunakan Algoritma Machine Learning","","Manajemen risiko gagal bayar kartu kredit menjadi tantangan utama bagi lembaga keuangan karena metode konvensional kurang mampu menangkap pola perilaku nasabah yang kompleks. Penelitian ini mengembangkan sistem prediksi berbasis web yang dapat digunakan oleh non-teknis dengan mengintegrasikan algoritma Extreme Gradient Boosting (XGBoost). Metode meliputi pembuatan model dari dataset Kaggle (30.000 entri, 24 atribut), pengembangan API Flask untuk integrasi real-time, dan implementasi antarmuka web. Sistem menerima input JSON dan menghasilkan klasifikasi berisiko/tidak berisiko, dengan akurasi 79,13%, presisi 52,99%, serta recall 50,11%. Integrasi web meningkatkan efisiensi evaluasi kredit tanpa keahlian machine learning.",{"@graph":14,"@context":72},[15,34,55],{"@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 & Laporan",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/id/document/development-of-a-credit-card-customer-default-prediction-website-using-machine-learning-algorithm/291065/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/development-of-a-credit-card-customer-default-prediction-website-using-machine-learning-algorithm/291065.png","ImageObject",300,407,{"name":42,"@type":43},"Theodora","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-21","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":30},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"Mengapa risiko gagal bayar kartu kredit perlu diprediksi lebih akurat?","Question",{"text":62,"@type":63},"Karena risiko gagal bayar berpotensi menimbulkan kerugian finansial dan mengganggu stabilitas sistem keuangan. Metode konvensional dinilai kurang mampu menangkap pola kompleks pada data nasabah.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Apa algoritma yang digunakan dalam sistem prediksi ini?",{"text":67,"@type":63},"Penelitian menggunakan Extreme Gradient Boosting (XGBoost) untuk memprediksi risiko gagal bayar berdasarkan klasifikasi data historis.",{"name":69,"@type":60,"acceptedAnswer":70},"Bagaimana sistem menerima input dan menghasilkan output?",{"text":71,"@type":63},"Sistem menerima input dalam format JavaScript Object Notation (JSON) dan menghasilkan klasifikasi risiko menjadi dua kategori: berisiko dan tidak berisiko.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},291065,1789643352,{"code":4,"msg":81,"data":82},"success",[83,88,92,96,100,104,107,111,115,119,123],{"id":84,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":89,"doc_module":4,"doc_module_name":25,"category_name":90,"show_sort_weight":86,"slug":91},48,"Cerita & Novel","story-novel",{"id":93,"doc_module":4,"doc_module_name":25,"category_name":94,"show_sort_weight":86,"slug":95},56,"Gaya Hidup","lifestyle",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":86,"slug":99},51,"Komik","comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":86,"slug":103},53,"Layanan Kesehatan","healthcare",{"id":105,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":86,"slug":106},54,"research-report",{"id":108,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":86,"slug":110},49,"Sastra","literature",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":113,"show_sort_weight":86,"slug":114},52,"Teknologi","technology",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":86,"slug":118},50,"Ujian","exam",{"id":120,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":86,"slug":122},57,"Umum","general",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":4,"slug":126},181,"Formulir","formulir",{"code":4,"msg":81,"data":128},{"doc_id":78,"user_id":129,"nickname":42,"user_avatar":130,"doc_module":4,"category_id":105,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":30,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":136,"language":137,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":12,"update_tm":79,"read_time":141},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS  \nVol. 10, No. 1, Juni 2025, 73-82 E-ISSN: 2548-3331  \n73  \nPengembangan Website Prediksi Gagal Bayar Nasabah Kartu Kredit menggunakan Algoritma Machine Learning  \nAmin Nur Rais1*, Warjiyono 1  \n1 Informatika; Universitas Bina Sarana Informatika; Jl. Kramat Raya No.98, RT.2/RW.9, Kwitang, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta 10450 (021)  \n21231170; e-mail: [amin.arv@bsi.ac.id](amin.arv@bsi.ac.id)  \n2 Sistem Informasi Akuntansi Kampus Kota Tegal; Universitas Bina Sarana Informatika; Jl. Kramat Raya No.98, RT.2/RW.9, Kwitang, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta 10450 (021) 21231170; [e-mail:](e-mail: warjiyono.wrj@bsi.ac.id)[ ](e-mail: warjiyono.wrj@bsi.ac.id)[warjiyono.wrj@bsi.ac.id](e-mail: warjiyono.wrj@bsi.ac.id)  \n* Korespondensi: e-mail: [amin.arv@bsi.ac.id](amin.arv@bsi.ac.id)  \nDiterima: 03 Juni 2025; Review: 05 Juni 2025; Disetujui:18 Juni 2025  \nCara sitasi: Rais AR, Warjiyono. 2025. Pengembangan Website Prediksi Gagal Bayar Nasabah Kartu Kredit menggunakan Algoritma Machine Learning. Information Management for Educatorsand Professionals. Vol 10(1): 73-82   \nAbstrak: Manajemen risiko gagal bayar kartu kredit masih menjadi tantangan utama bagilembaga keuangan, terutama karena metode konvensional seperti statistik sederhana kurang mampu menangkap pola perilaku nasabah secara kompleks. Penelitian ini bertujuan untuk mengembangkan sistem prediksi risiko gagal bayar berbasis web yang mudah digunakan oleh pengguna non teknis dengan mengintegrasikan algoritma Extreme Gradient Boosting (XGBoost) sebagai pendekatan machine learning yang unggul dalam klasifikasi data historis. Metodologi penelitian meliputi tiga tahap utama: (1) pembangunan model prediktif menggunakan dataset publik “Default of Credit Card Clients” dari Kaggle dengan 30.000 entri dan 24 atribut, (2) pengembangan Application Programming Interface (API) dengan framework Flask untuk integrasi real-time, dan (3) implementasi antarmuka web untuk memudahkan penggunaan oleh pihak nonteknis. Model yang digunakan dalam pengembangan sistem ini menunjukkan akurasi 79,13%, presisi 52,99%, dan recall 50,11% . Sistem ini menerima input dalam format JavaScript Object Notation (JSON) dan menghasilkan klasifikasi risiko menjadi dua kategori: berisiko dan tidakberisiko. Integrasi model ke dalam platform web memungkinkan proses evaluasi risiko kredit dilakukan secara lebih efisien, akurat, dan dapat diakses tanpa keahlian teknis machine learning. Penelitian ini membuka peluang pengembangan sistem serupa untuk produk keuangan lainnyadengan penekanan pada peningkatan transparansi dan penanganan distribusi data yang tidakseimbang.  \nKata kunci: prediksi gagal bayar, XGBoost, machine learning, risiko kredit, aplikasi web  \nAbstract: Managing credit card default risk remains a significant challenge for financial institutions, particularly because conventional methods such as basic statistical analysis often fail to capture complex patterns in customer behavior. This study aims to develop a web-based credit default prediction system that is accessible to non-technical users by integrating the Extreme Gradient Boosting (XGBoost) algorithm, a machine learning approach known for its strong performance in historical data classification. The research methodology consists of three main stages: (1) developing a predictive model using the public \"Default of Credit Card Clients\" dataset from Kaggle, which includes 30,000 entries and 24 attributes; (2) developing an Application Programming Interface (API) using the Flask framework for real-time integration; and (3) implementing a web interface to support user-friendly access. The model employed in this system achieved an accuracy of 79. 13%, a precision of 52. 99%, and a recall of 50. 11%. The system accepts input in JavaScript Object Notation (JSON) format and provides binary classification results indicating whether a useris at risk or not.","cbCaid8zUSncdh2s","https://ap.wps.com/l/cbCaid8zUSncdh2s","pdf",331029,10,"Indonesian","# Pendahuluan\n## Tujuan dan latar belakang masalah\n## Kebutuhan pendekatan baru credit scoring\n# Metodologi dan pengembangan sistem\n## Model XGBoost untuk klasifikasi\n## Pengembangan API untuk integrasi real-time\n## Implementasi antarmuka web\n# Evaluasi kinerja sistem\n## Metrik akurasi, presisi, dan recall\n## Output klasifikasi risiko\n# Implikasi dan pengembangan lanjutan\n## Potensi aplikasi pada produk keuangan lain","[{\"question\":\"Mengapa risiko gagal bayar kartu kredit perlu diprediksi lebih akurat?\",\"answer\":\"Karena risiko gagal bayar berpotensi menimbulkan kerugian finansial dan mengganggu stabilitas sistem keuangan. Metode konvensional dinilai kurang mampu menangkap pola kompleks pada data nasabah.\"},{\"question\":\"Apa algoritma yang digunakan dalam sistem prediksi ini?\",\"answer\":\"Penelitian menggunakan Extreme Gradient Boosting (XGBoost) untuk memprediksi risiko gagal bayar berdasarkan klasifikasi data historis.\"},{\"question\":\"Bagaimana sistem menerima input dan menghasilkan output?\",\"answer\":\"Sistem menerima input dalam format JavaScript Object Notation (JSON) dan menghasilkan klasifikasi risiko menjadi dua kategori: berisiko dan tidak berisiko.\"}]","Pengembangan Website Prediksi Gagal Bayar Nasabah Kartu Kredit menggunakan Algoritma Machine Learning | PDF",15]