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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal Information System Development ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Sistem Informasi Kaputama (JSIK) Building of Informatics, Technology and Science Majalah Ilmiah Warta Dharmawangsa JTIK (Jurnal Teknik Informatika Kaputama) JUKI : Jurnal Komputer dan Informatika Jurnal Manajemen Informatika Jayakarta Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Journal of Vision and Ideas (VISA) Jurnal Pengabdian Masyarakat IPTEK EXPLORER Bulletin of Multi-Disciplinary Science and Applied Technology Journal Of Human And Education (JAHE) Journal of Information Systems and Technology Research Sci-Tech Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Informatics, Economics, Management and Science Ulead : Jurnal E-pengabdian Journal of Engineering, Technology and Computing (JETCom) Journal of Mathematics and Technology (MATECH) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) JOURNAL OF ICT APLICATIONS AND SYSTEM Jurnal Teknik, Komputer, Agroteknologi dan Sains Zadama: Jurnal Pengabdian Masyarakat International Journal of Health, Engineering and Technology Jurnal Penelitian Sistem Informasi Indonesian Journal of Education And Computer Science Indonesian Journal of Science, Technology, and Humanities Pengabdian Pendidikan Indonesia (PPI) Jurnal Ilmu Komputer dan Sistem Informasi Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan Switch: Jurnal Sains dan Teknologi Informasi Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Saturnus: Jurnal Teknologi dan Sistem Informasi KETIK : Jurnal Informatika Ulil Albab Pascal: Journal of Computer Science and Informatics Journal of Computer Science Artificial Intelligence and Communications Jurnal Ilmu Komputer dan Teknik Informatika Jurnal Pengabdian Masyarakat Berdampak Global Science: Journal of Information Technology and Computer Science Journal of Data Science and Informatics Engineering
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Journal : Global Science: Journal of Information Technology and Computer Science

Benchmarking Machine Learning Models for Large-Scale Loan Default Prediction Using Real Data Devianto, Yudo; Saragih, Rusmin; Cahyana, Yana
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 1 (2026): March: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i1.181

Abstract

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.
Context Sensitive Artificial Intelligence for Dynamic User Behavior Modeling in Next Generation Smart Information Platforms Rusmin Saragih; Enda Ribka Meganta P; Tiwuk Widiastuti; Ahmad Jurnaidi Wahidin; Erlita Sulistiati; Muhamad Furqon
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.194

Abstract

This study explores the development and implementation of a context sensitive artificial intelligence (AI) model designed to predict and personalize user behavior in smart information platforms. Traditional user behavior models often fail to adapt to dynamic and evolving user needs, especially in diverse environments where contextual factors such as time of day, location, and device type play a critical role in shaping user preferences. To address these limitations, the proposed context sensitive AI model integrates real time contextual data alongside traditional behavioral data, enabling it to make more accurate predictions and provide personalized, relevant content. The model utilizes advanced machine learning techniques, such as deep learning and reinforcement learning, to continuously update and refine user behavior models based on contextual shifts. Through the integration of contextual parameters, the model demonstrates improved prediction accuracy, system responsiveness, and overall user satisfaction compared to static, context agnostic models. Furthermore, the study discusses the key advantages of context aware AI, such as its ability to dynamically adjust to real time changes in user behavior, providing more adaptive, personalized services. Challenges encountered during the model's development, including issues related to data privacy, scalability, and the integration of multiple contextual data sources, are also addressed. The findings suggest that context sensitive AI can significantly enhance the effectiveness of smart platforms by improving user engagement and content relevance. Finally, the study provides recommendations for further research to explore deep learning methods for context detection and to improve the discoverability and integration of AI driven features in user interfaces.
Co-Authors , Eka Putra Abdul Azan Abdul Azan Abdullah Hamid, Abdullah Abdullah Husein Achmad Fauzi ACHMAD FAUZI Ahmad Jurnaidi Wahidin Alfina Damayanti Ambarita, Indah Andini Andini Andre Adrian Andrean Samuel Siahaan Aprilianda, Dinda Arianta Bangun Arnes Sembiring Asih, Munjiat Setiani Barany Fachri Boyke Gunawan Manurung Br Sitepu, Dinda Isabella Buaton, Relita Chairul Rizal Charles Jhony Mantho Sianturi, Charles Jhony Mantho Cindy Primadona Siahaan Damayanti, Fera Dandi Satria R Darmawan Ginting Deni Apriadi Dewantara, Nowell Dimas Prayogi Dinda Firdawati Simamora Divi Handoko Eka Pandu Cynthia Eka, Muhammad Enda Ribka Meganta P Erlita Sulistiati Fany Juliawati Fatimah Fatmaira, Zira Fauzi, Achmad frans ikorasaki Fuzy Yustika Manik Fuzy Yustika Manik, Fuzy Yustika Gea, Fide Evianti Gultom, Imeldawaty Handoko, Divi Herdiansyah Harahap Herdiansyah Harahap Hesty Vitara I Gusti Prahmana Ikhsan Arif Indra Prasetia, Indra Irfan Yusuf Ismi Asmita Jesayas Sembiring Khair, Husnul Khalidy, Furqan Lestari, Yuyun Dwi Lili Musarofah Lili Musarofah M. Yogi Riyantama Isjoni Magdalena Simanjuntak Mardiah Marto Sihombing Marto Sihombing Meisaroh Melda Pita Uli Sitompul Mhd Ferdiansyah Putra Mili Alfhi Syari Muhamad Furqon Muhammad Danil Syahputra Muhammad Danil Syahputra Muhammad Eka Muhammad Eka Muhammad Noor Hasan Siregar Muhammad Reza Habibi Muhammad Zen, Muhammad Munadi Munadi Nadia Nurhafiza Nasril Hidayat Nico Kurniawan Purba Nikous Soter Sihombing Novriyenni Novriyenni Novriyenni Novriyenni, Novriyenni Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurlaila Nurlaila Nuryahati - Pakpahan, emma martina Pakpahan, Victor Maruli Pardede, Akim Manaor Hara Pasaribu, Tioria Petrus Loo Rafli Fitriawan Rahayu Utami Rahmadani Rahmadani Rahmawati Rahmawati, Rahmawati Raihan, Muhammad Ramadani, Suci Ramli Ramli Ramos Parulian Ambarita Ratih Puspadini Rianty Zabitha Siregar Ricky Ramadhan Harahap Rizki Kurniawan Ryan Hidayat Sari Suwandi, Ema Saripurna, Darjat Satria R, Dandi SELVY ANGGRAINI, SELVY Sihombing, Anton Sihombing, Marto Simanjuntak, Magdalena Simanjuntak, Magdalena Sinaga, Ayu Puspita Sari Sirait, Win Gomgom Parsaulian Siswan Syahputra Sitepu, Ruine Buana Br SITORUS, ERBIN Sonadi Perangin Angin Suci Pratiwi, Kiki Supiyandi Supiyandi Syahputra, Siswan Syari, Milli Alfhi Tantia Azzahra Tata Mustika Dewi tata, tatamustikadewi Theodora MV Nainggolan Tiwuk Widiastuti Ulandari, Seri Wati, Sri Kesuma Yana Cahyana Yani Maulita Yekolya Anatesya Yessi Fitri Annisah Lubis Yudo Devianto Yulia Ningsih Yusuf Afani Yuyun Dwi Lestari