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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Psikostudia : Jurnal Psikologi JSR : Jaringan Sistem Informasi Robotik TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pengabdian kepada Masyarakat Nusantara Brilliance: Research of Artificial Intelligence Jurnal Teknologi Sistem Informasi Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Akademika Jurnal TAM (Technology Acceptance Model)
Abidin, Zainil
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Journal : Jurnal Teknologi Informasi dan Ilmu Komputer

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Penerapan Random Oversampling dan Principal Component Analysis untuk Meningkatkan Akurasi Prediksi Kebangkrutan Perusahaan di Indonesia dengan Model Machine Learning Abidin, Zainil; Suratno, Tri; Fadhila Putri, Mutia
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Prediksi 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 meningkatkan akurasi 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 data kebangkrutan 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.   Abstract Bankruptcy 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.
Co-Authors Adrian, Rahmad Apresto, Muhammad Haikal Ariansah, Bimo Arsa, Daniel Aryani, Reni aSaputra, Zaqib Benedika Ferdian Hutabarat Dedy Setiawan EDI SAPUTRA Listiani Safitri, Devi Mh. Khathamy Fhadlullah Haq Syahlevy Mohamad Ilham Muhammad Razi A mutia fadhila putri, mutia fadhila Noverina, Yolla Putri Z., Khairin Nisa Rabiula, Andre Rachmawati, Arini Rahman, Reyvi Saputra, Dandi Juan Saputri, Yosika Dian susilawati rahman, ledisma Tri Suratno Ulfa Khaira, Ulfa Utomo, Pradita Eko Prasetyo