[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125421-id":3,"doc-seo-125421-113":31,"detail-sidebar-cat-0-id-113":92},{"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},125421,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",54,"Penelitian & Laporan","Penerapan Random Oversampling dan Principal Component Analysis untuk Meningkatkan Akurasi Prediksi Kebangkrutan Perusahaan dengan Model Machine Learning","Prediksi kebangkrutan menjadi kebutuhan penting untuk memberi peringatan dini kepada manajemen dan pemangku kepentingan agar tindakan preventif dapat dilakukan lebih cepat. Penelitian ini menguji Random Oversampling dan Principal Component Analysis (PCA) dalam model machine learning menggunakan dua dataset, yaitu Taiwanese Bankruptcy Prediction (6.891 data) dan data kebangkrutan perusahaan Indonesia dari BEI tahun 2021–2023 (2.703 data), sehingga total 9.594 data. Empat algoritma klasifikasi diuji sebelum dan sesudah penerapan metode; hasil menunjukkan peningkatan recall kelas minoritas serta SVM sebagai performa terbaik.","PENERAPAN RANDOM OVERSAMPLING DAN PRINCIPAL COMPONENT ANALYSIS UNTUK MENINGKATKAN AKURASI PREDIKSI KEBANGKRUTAN PERUSAHAAN DI INDONESIA DENGAN MODEL MACHINE LEARNING  \nZainil Abidin*1, Tri Suratno2 , Mutia Fadhila Putri3  \n1,2,3 Universitas Jambi, Kabupaten Muaro Jambi  \n[Email:](Email:1zainil.abidin@unja.ac.id)[1](Email:1zainil.abidin@unja.ac.id)[zainil.abidin@unja.ac.id](Email:1zainil.abidin@unja.ac.id), [2](2tri@unja.ac.id)[tri@unja.ac.id](2tri@unja.ac.id), [3](3mutia.fadhila@unja.ac.id)[mutia.fadhila@unja.ac.id](3mutia.fadhila@unja.ac.id)  \n*Penulis Korespondensi  \n(Naskah masuk: 17 Maret 2025, diterima untuk diterbitkan: 30 April 2025)  \nAbstrak  \nPrediksi kebangkrutan menjadi penting untuk memberikan peringatan dini bagi manajemen dan pemangku kepentingan agar dapat mengambil tindakan preventif. Penelitian ini menguji penerapan metode Random Oversampling dan Principal Component Analysis (PCA) dalam model machine learning untuk meningkatkanakurasi prediksi kebangkrutan perusahaan. Penelitian ini menggunakan dua dataset yaitu data Taiwanese Bankruptcy Prediction dari UCI Machine Learning Repository sebanyak 6.891 data dan data primer berupa datakebangkrutan perusahaan Indonesia dari Bursa Efek Indonesia (BEI) dari tahun 2021-2023 sebanyak 2.703 data. Total keseluruhan dataset yang digunakan sebanyak 9.594 data. Empat algoritma klasifikasi—KNN, Naïve Bayes, SVM, dan Decision Tree—diuji sebelum dan sesudah penerapan metode tersebut. Hasil menunjukkan bahwa kombinasi PCA dan Random Oversampling meningkatkan recall kelas minoritas (kebangkrutan) secara signifikan. SVM menjadi algoritma terbaik dengan precision 0,86, recall 0,76, dan F1-score 0,80, sementara Decision Tree mengalami overfitting setelah oversampling. PCA berhasil mereduksi dimensi dataset hingga 98,87% varian tetap terjaga, dan Random Oversampling menyeimbangkan distribusi kelas.  \nKata kunci: prediksi kebangkrutan, machine learning, random oversampling, PCA.  \nAPPLICATION OF RANDOM OVERSAMPLING AND PRINCIPAL COMPONENT ANALYSIS TO ENHANCE THE ACCURACY OF BANKRUPTCY PREDICTION FOR COMPANIES IN INDONESIA USING MACHINE LEARNING MODELS  \nAbstract  \nBankruptcy prediction is crucial for providing early warnings to management and stakeholders to take preventive actions. This study examines the application of Random Oversampling and Principal Component Analysis (PCA) in machine learning models to improve the accuracy of corporate bankruptcy prediction. The study uses two datasets: the Taiwanese Bankruptcy Prediction data from the UCI Machine Learning Repository (6,891 data points) and primary data on Indonesian company bankruptcies from the Indonesia Stock Exchange (IDX) for 2021–2023 (2,703 data points), totaling 9,594 data points. Four classification algorithms—K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machine (SVM), and Decision Tree—were tested before and after applying these methods. The results show that the combination of PCA and Random Oversampling significantly improved the recall of the minority class (bankruptcy). SVM emerged as the best-performing algorithm with a precision of 0.86, recall of 0. 76, and F1-score of 0.80, while the Decision Tree experienced overfitting after oversampling. PCA successfully reduced the dataset’s dimensions while retaining 98.87% of the variance, and Random Oversampling balanced the class distribution.  \nKeywords: bankruptcy prediction, machine learning, random oversampling, PCA  \n1. PENDAHULUAN  \nIstilah kebangkrutan dapat didefinisikansebagai kondisi dimana suatu perusahaan tidak mampu lagi menjalankan operasional yangumumnya disebabkan oleh masalah kesulitankeuangan (Rahayu and Usmansyah, 2021a) . Kebangkrutan perusahaan akan berdampak kepada  \nstakeholder seperti para investor, karyawan dan pihak lain yang memiliki keterkaian dengan dengan perusahaan. Kebangkrutan juga akan menyebabkanterjadinya efek domino seperti menganggukestabilan pasar dan meningkatnya gelombang PHK (Diana and Hidayat, 2023) . Permasalahan k","cbCaiami6wRJJqH7","https://ap.wps.com/l/cbCaiami6wRJJqH7","pdf",785482,4,1,12,"Indonesian","id",113,"# Pendahuluan\n## Pentingnya prediksi kebangkrutan\n## Metode berbasis rasio keuangan\n## Alternatif machine learning\n## Konsep kerja machine learning","[{\"question\":\"Mengapa prediksi kebangkrutan perusahaan penting?\",\"answer\":\"Prediksi kebangkrutan memberikan peringatan dini bagi manajemen dan pemangku kepentingan untuk mengambil tindakan preventif sebelum kondisi memburuk.\"},{\"question\":\"Data apa saja yang digunakan dalam penelitian ini?\",\"answer\":\"Penelitian memakai dua dataset: Taiwanese Bankruptcy Prediction dari UCI (6.891 data) dan data kebangkrutan perusahaan Indonesia dari BEI tahun 2021–2023 (2.703 data), total 9.594 data.\"},{\"question\":\"Bagaimana pengaruh Random Oversampling dan PCA terhadap hasil model?\",\"answer\":\"Kombinasi PCA dan Random Oversampling meningkatkan recall kelas minoritas (kebangkrutan) secara signifikan. PCA mereduksi dimensi dengan mempertahankan 98,87% varian dan Random Oversampling menyeimbangkan distribusi kelas.\"}]","Penerapan Random Oversampling dan Principal Component Analysis untuk Meningkatkan Akurasi Prediksi Kebangkrutan Perusahaan dengan Model Machine Learning | PDF",1785898828,18,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":29},"application-of-random-oversampling-and-principal-component-analysis-to-improve-bankruptcy-prediction-accuracy-using-machine-learning-models","",{"@graph":37,"@context":86},[38,54,69],{"@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":20},"https://docshare.wps.com/id/document/application-of-random-oversampling-and-principal-component-analysis-to-improve-bankruptcy-prediction-accuracy-using-machine-learning-models/125421/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-15","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Mengapa prediksi kebangkrutan perusahaan penting?","Question",{"text":76,"@type":77},"Prediksi kebangkrutan memberikan peringatan dini bagi manajemen dan pemangku kepentingan untuk mengambil tindakan preventif sebelum kondisi memburuk.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Data apa saja yang digunakan dalam penelitian ini?",{"text":81,"@type":77},"Penelitian memakai dua dataset: Taiwanese Bankruptcy Prediction dari UCI (6.891 data) dan data kebangkrutan perusahaan Indonesia dari BEI tahun 2021–2023 (2.703 data), total 9.594 data.",{"name":83,"@type":74,"acceptedAnswer":84},"Bagaimana pengaruh Random Oversampling dan PCA terhadap hasil model?",{"text":85,"@type":77},"Kombinasi PCA dan Random Oversampling meningkatkan recall kelas minoritas (kebangkrutan) secara signifikan. PCA mereduksi dimensi dengan mempertahankan 98,87% varian dan Random Oversampling menyeimbangkan distribusi kelas.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,99,103,107,111,115,117,121,125,129,133],{"id":95,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},55,"Agama & Spiritualitas",60,"religion-spirituality",{"id":100,"doc_module":4,"doc_module_name":47,"category_name":101,"show_sort_weight":97,"slug":102},48,"Cerita & Novel","story-novel",{"id":104,"doc_module":4,"doc_module_name":47,"category_name":105,"show_sort_weight":97,"slug":106},56,"Gaya Hidup","lifestyle",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":97,"slug":110},51,"Komik","comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":97,"slug":114},53,"Layanan Kesehatan","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":97,"slug":116},"research-report",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":97,"slug":120},49,"Sastra","literature",{"id":122,"doc_module":4,"doc_module_name":47,"category_name":123,"show_sort_weight":97,"slug":124},52,"Teknologi","technology",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":97,"slug":128},50,"Ujian","exam",{"id":130,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":97,"slug":132},57,"Umum","general",{"id":134,"doc_module":4,"doc_module_name":47,"category_name":135,"show_sort_weight":4,"slug":136},181,"Formulir","formulir"]