[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120178-id":3,"doc-seo-120178-113":31,"detail-sidebar-cat-0-id-113":93},{"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},120178,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",54,"Penelitian & Laporan","PENERAPAN HYPERPARAMETER MACHINE LEARNING DALAM PREDIKSI GAGAL PINJAM","Pinjaman atau kredit berperan penting dalam mendorong pertumbuhan ekonomi, namun risiko gagal bayar perlu ditekan melalui penilaian kemampuan peminjam. Penelitian ini menerapkan machine learning untuk memprediksi hasil pinjaman dengan mengoptimalkan parameter pada algoritma ML. Model yang ditinjau meliputi Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, dan XGBoost. Hiperparameter disetel menggunakan Grid Search Cross Validation, dan hasil menunjukkan peningkatan performa dibanding pendekatan sebelumnya melalui kenaikan akurasi pada tiap algoritma, sehingga membantu lembaga pembiayaan menentukan skenario pinjaman yang lebih tepat.","PENERAPAN HYPERPARAMETER MACHINE LEARNING DALAM PREDIKSI  \nGAGAL PINJAM  \nDinar Ismunandar1*; Muhammad Rifqi Firdaus2; YurisAlkhalifi3  \nTeknologi Informasi1, Sistem Informasi2, Teknologi Komputer3  \nUniversitas Bina Sarana Informatika, Jakarta, Indonesia 1,2,3  \n[bsi.ac.id](bsi.ac.id1)[1](bsi.ac.id1),2,3  \n[dinar.dim@bsi.ac.id](dinar.dim@bsi.ac.id1)[1](dinar.dim@bsi.ac.id1)* ; [muhammad.mku@bsi.ac.id](muhammad.mku@bsi.ac.id2)[2](muhammad.mku@bsi.ac.id2); [yuris.yak@bsi.ac.id](yuris.yak@bsi.ac.id3)[3](yuris.yak@bsi.ac.id3)[ ](yuris.yak@bsi.ac.id3)(*) Corresponding Author  \nCiptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.  \nAbstract—Loans or credit are one of the key factors in advancing the economy. One of them is encouraging business expansion which will have a direct impact on a country's economic growth. Banks and other financing institutions must be able to evaluate the borrower's ability to pay their debts based on the inherent risks to reduce the possibility of default. To this end, machine learning (ML) has emerged as a revolutionary tool in using advanced prediction methods to examine historical data based on customer behavior. This research investigates the application of ML in predicting loan outcomes by optimizing parameters in the Machine Learning algorithm. The ML algorithms examined in this research are Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), and XGBoost (XGB). Meanwhile, the technique used in hyperparameter tuning is Grid Search Cross Validation (CV). The results show that the algorithm's performance is more optimal than before, it can be seen that the LR algorithm experienced an increase inaccuracy of 5%, KNN by 4%, RF by 3%, DT by 3%, and XGB by 2%. By including a default dataset based on customer behavior and optimized algorithm parameters, apart from being able to answer the alignment in previous literature in providing a deeper understanding of loan estimation, this research can also provide an understanding that hyperparameter techniques are worth trying to improve the performance of ML algorithms. So, it will be easier for financing institutions to determine the right loan scenario.  \nKeywords: gridsearchCV, hyperparameters, loan default prediction, machine learning.  \nAbstrak—Pinjaman atau kredit merupakan salah satu faktor kunci dalam memajukan perekonomian. Salah satunya adalah mendorong ekspansi bisnis yang akan berdampak langsung pada pertumbuhan ekonomisuatu negara. Bank dan lembaga pembiayaan lainnya harus dapat mengevaluasi kemampuan peminjam dalam membayar utangnya berdasarkan risiko yang melekat untuk mengurangi kemungkinan gagal bayar. Untuk itu, Machine Learning (ML) hadir sebagai alat revolusioner dalam menggunakan metode prediksi yang canggih untuk memeriksa data historis berdasarkan perilaku nasabah. Penelitian ini menginvestigasipenerapan ML dalam memprediksi hasil pinjaman dengan mengoptimalkan parameter dalam algoritma Machine Learning. Algoritma ML yang diteliti dalam penelitian ini adalah Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), dan XGBoost (XGB) . Sementara itu, teknik yang digunakan dalam tuning hyperparameter adalah Grid Search Cross Validation (CV) . Hasil penelitian menunjukkan bahwa kinerja algoritma lebih optimal dibandingkan sebelumnya, terlihat bahwa algoritma LR mengalami peningkatan akurasi sebesar 5%, KNN sebesar 4%, RF sebesar 3%, DT sebesar 3%, dan XGBsebesar 2%. Dengan menyertakan dataset default berdasarkan perilaku nasabah dan parameter algoritmayang telah dioptimasi, selain dapat menjawab keselarasan pada literatur terdahulu dalam memberikanpemahaman yang lebih mendalam mengenai estimasi kredit, penelitian ini juga dapat memberikanpemahaman bahwa teknik hiperparameter layak dicoba untuk meningkatkan performa algoritma ML. Sehingga, lembaga pembiayaan akan lebih mudah menentukan skenario pinjaman yang tepat.  \nKata kunc","cbCaip7fuhMmzSZE","https://ap.wps.com/l/cbCaip7fuhMmzSZE","pdf",1057256,5,1,9,"Indonesian","id",113,"# Pendahuluan\n## Latar belakang pinjaman dan perannya bagi ekonomi\n## Kredit macet dan kebutuhan evaluasi risiko\n## Peran machine learning dalam pengambilan keputusan","[{\"question\":\"Penelitian ini berfokus pada apa?\",\"answer\":\"Penelitian ini berfokus pada penerapan machine learning untuk memprediksi gagal pinjam dengan mengoptimalkan hyperparameter pada algoritma ML.\"},{\"question\":\"Algoritma machine learning apa saja yang digunakan?\",\"answer\":\"Algoritma yang digunakan adalah Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, dan XGBoost.\"},{\"question\":\"Bagaimana tuning hyperparameter dilakukan dan apa dampaknya?\",\"answer\":\"Tuning hyperparameter dilakukan dengan Grid Search Cross Validation. Dampaknya adalah performa model menjadi lebih optimal, terlihat dari peningkatan akurasi pada masing-masing algoritma.\"}]","PENERAPAN HYPERPARAMETER MACHINE LEARNING DALAM PREDIKSI GAGAL PINJAM | PDF",1785728570,14,{"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":88,"head_meta":90,"extra_data":92,"updated_unix":29},"application-of-hyperparameter-machine-learning-in-loan-default-prediction","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/id/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/id/document/penelitian-laporan/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/id/document/application-of-hyperparameter-machine-learning-in-loan-default-prediction/120178/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-18","2026-08-03",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Penelitian ini berfokus pada apa?","Question",{"text":77,"@type":78},"Penelitian ini berfokus pada penerapan machine learning untuk memprediksi gagal pinjam dengan mengoptimalkan hyperparameter pada algoritma ML.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Algoritma machine learning apa saja yang digunakan?",{"text":82,"@type":78},"Algoritma yang digunakan adalah Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, dan XGBoost.",{"name":84,"@type":75,"acceptedAnswer":85},"Bagaimana tuning hyperparameter dilakukan dan apa dampaknya?",{"text":86,"@type":78},"Tuning hyperparameter dilakukan dengan Grid Search Cross Validation. Dampaknya adalah performa model menjadi lebih optimal, terlihat dari peningkatan akurasi pada masing-masing algoritma.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,100,104,108,112,116,118,122,126,130,134],{"id":96,"doc_module":4,"doc_module_name":47,"category_name":97,"show_sort_weight":98,"slug":99},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":101,"doc_module":4,"doc_module_name":47,"category_name":102,"show_sort_weight":98,"slug":103},48,"Cerita & Novel","story-novel",{"id":105,"doc_module":4,"doc_module_name":47,"category_name":106,"show_sort_weight":98,"slug":107},56,"Gaya Hidup","lifestyle",{"id":109,"doc_module":4,"doc_module_name":47,"category_name":110,"show_sort_weight":98,"slug":111},51,"Komik","comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":98,"slug":115},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":98,"slug":117},"research-report",{"id":119,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":98,"slug":121},49,"Sastra","literature",{"id":123,"doc_module":4,"doc_module_name":47,"category_name":124,"show_sort_weight":98,"slug":125},52,"Teknologi","technology",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":98,"slug":129},50,"Ujian","exam",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":98,"slug":133},57,"Umum","general",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":4,"slug":137},181,"Formulir","formulir"]