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All Journal Jurnal Masyarakat Informatika Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Transformatika Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) ANDHARUPA Journal of Information System ITEj (Information Technology Engineering Journals) Edu Komputika Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian Kepada Masyarakat
Erika Devi Udayanti
Universitas Dian Nuswantoro, Semarang
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Optimizing Bankruptcy Prediction on Imbalanced Data using XGBoost with Random Oversampling and Chi-Square Suyatno, Revalina; Udayanti, Erika Devi; Dewi, Ika Novita
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11841

Abstract

In the midst of modern financial dynamics, the ability to predict corporate bankruptcy holds strategic significance, as it directly affects economic stability and investor confidence. However, the development of a reliable predictive model is often hindered by the complex nature of financial data, particularly the class imbalance between bankrupt and non-bankrupt companies. This imbalance causes models to become biased toward the majority class, thereby reducing their sensitivity in detecting bankruptcy cases which are, in fact, the most critical for financial decision-making. This research aims to construct a more balanced and sensitive bankruptcy prediction model by specifically addressing the issue of data imbalance. The proposed approach integrates the Random Oversampling (ROS) technique to equalize class distribution, Chi-Square feature selection to identify the most informative financial variables, and the Extreme Gradient Boosting (XGBoost) algorithm as the core predictive model. The dataset used is the UCI Taiwanese Bankruptcy Prediction dataset, consisting of 6,819 observations and 96 financial ratio variables. Experimental results show that the Chi-Square method successfully identified 20 influential variables, including Per Share Net Profit Before, Debt Ratio, and ROA(B) Before Interest and Depreciation After Tax. The proposed XGBoost model achieved an overall accuracy of 0.9648 and an F1-score of 0.4286, demonstrating superior performance. These findings confirm that the combination of ROS, Chi-Square, and XGBoost effectively enhances data balance and prediction sensitivity for the bankruptcy class. This research is expected to serve as a foundation for developing financial decision-support systems capable of providing early warnings of potential corporate bankruptcy.
English English Karimah, Sofia Rizkal; Udayanti, Erika Devi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11921

Abstract

This research aims to compare the performance of the Apriori and FP-Growth algorithms in the process of data mining association patterns in the online sales transaction data of a bookstore. The dataset used consists of 74.090 transactions resulting from data cleaning from the period January-June 2025. The analysis was conducted through the stages of data collection, followed by data preparation consisting of data cleaning and data transformation, and then continued to the modeling stage of the two algorithms. The results of the experiment show that Apriori tends to be faster on small-scale datasets with simple transaction patterns, while FP-Growth has more stable memory usage and shows more efficient processing time on parameters that analyze larger data. Both algorithms produce identical numbers and contents of association rules for each parameter variation, indicating that the significant difference lies in performance efficiency, and not in the knowledge patterns produced. Rules with the highest lift values, such as the association between the books "Rumah Kaca" and "Jejak Langkah" (lift: 183,306 & confidence 0,903) and between the books "Namaku Alam" and "Pulang" (lift: 34,062 & confidence: 0,51) indicate strong purchasing patterns between titles with the same author and theme. These findings have the potential to support cross-selling strategies and product recommendations in online sales systems. This research is still limited to a relatively small and homogeneous dataset, so further using a broader data coverage is recommended to test the algorithm's performance more comprehensively.
Co-Authors Affandy Affandy Afida, Dita Ahmad MAULANA Aisyatul Karima ALI MUQODDAS Ali Muqoddas Aloysius Soerjowardhana Alzami, Farrikh Andriana, Wiwin Anggadiva, Rifky Anwarri, Kenza Amalia Putri Arika Norma Wahyu Dorroty Aritonang, Ivana Junita Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Candra Irawan Chornelius Aneba Moza Ikratama Christiawan Yosua Hertinando Christy Atika Sari Comara, Maulana Muhammadin Dian Restu Adji Dibyo Adi Wibowo Djuniadi Djuniadi Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Erba Lutfina Erwin Yudi Hidayat Yudi Hidayat Ery Mintorini Etika Kartikadarma Etika Kartikadharma Fahri Firdausillah Fajar Agung Nugroho Fajar Agung Nugroho Fajar Agung Nugroho Fajrian Nur Adnan Farah Syadza Mufidah Florentina Esti Nilawati Gery Gadman Rachmad Hafidhoh, Nisa'ul Hafidhoh, Nisa?ul Hafidhoh, Nisa’ul Hafidhoh, Nisa’ul Hafidhoh, Nisa’ul Ika Novita Dewi Ikhsan, Nur Iqlima Zahari Karimah, Sofia Rizkal Karmila Karmila Kartikadharma, Etika Kevin Febrianto Lutfina, Erba Megantara, Rama Aria Mellati, Pita Muhammad Agus Muljanto Muhammad Hafidz Muna, Mohamad Sirojul Natalinda Pamungkas Natalinda Pamungkas Nisa'ul Hafidhoh Nur Ikhsan Nur Iksan Putra, Yogi Pratama Raden Arief Nugroho Ramadhan Rakhmat Sani Sanina Quamila Putri Sanjaya, Yusuf Yudha Soerjowardhana, Aloysius Sri Mulatsih Sri Winarno Suyatno, Revalina Syafira Putri Yuanita Valentina Widya Suryaningtyas, Valentina Widya Widayat Yutriatmansyah, Widi Widi Widayat Yutriatmansyah Wildan Mahmud Wisnumurti, Reza Yuni Lestari Yunita Ayu Pratiwi Yutriatmansyah, Widi Widayat Yutriatmansyah, Widi Widayat  Ignasius Yoga Puji Hascaryo