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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Informatika Agrointek JUSIFO : Jurnal Sistem Informasi Annual Research Seminar JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Jurnal SOLMA INOVTEK Polbeng - Seri Informatika Techno Nusa Mandiri : Journal of Computing and Information Technology IKRA-ITH Informatika : Jurnal Komputer dan Informatika ILKOM Jurnal Ilmiah KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom Transformasi Teknokris
Ali Khumaidi
Universitas Krisnadwipayana
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Other

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Explainable Boosting Machine for Transparent Risk Assessment in BAZNAS Microfinance Desa Wicaksono, Harjunadi; Riyanto, Agus; Darmawan, Risanto; Hidayat, M. Fahmi; Khumaidi, Ali
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.3214.312-322

Abstract

Microfinance institutions face substantial challenges in managing financing risk, particularly in assessing the creditworthiness of mustahik when available data are limited. BAZNAS Microfinance Desa (BMD) requires a predictive risk system that is both accurate and transparent to ensure program sustainability while adhering to sharia principles. This study develops an Explainable Boosting Machine (EBM) model using historical data from 736 mustahik across three BMD locations (2019-2024). The methodology integrates comprehensive feature engineering, including the DTI Ratio, Savings Ratio, Financial Stress Indicator, and Dependency Ratio. Model performance was evaluated using ROC-AUC, precision-recall metrics, and confusion matrix analysis, while interpretability was examined through SHAP values and partial dependence plots. The EBM model achieved strong predictive performance, recording an ROC-AUC of 0.853, an accuracy of 80%, a precision of 82%, and a recall of 77%. Global interpretability analysis identified Remaining Balance (18.2%), Business Type (12.5%), and Household Income (11.3%) as the most influential predictors. Feature-engineered variables contributed 42% to the model’s predictive strength, confirming the added value of domain-knowledge-driven feature engineering. Critical risk thresholds were identified at Remaining Balance below IDR 200,000 and DTI Ratio above 0.8. The EBM framework effectively balances predictive accuracy with full interpretability, making it suitable for deployment in microfinance decision-support systems. The model provides actionable insights for risk-based pricing and early warning mechanisms while maintaining the transparency essential in microfinance financing.
Enhancing Crack Detection on Levees with Synthetic Data Augmentation via ACGAN and Attention-Boosted Faster R-CNN Saludin, Saludin; Priyadi, Wiwit; Artiani, Gita Puspa; Darmawan, Risanto; Khumaidi, Ali
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3206.58-68

Abstract

This study introduces an innovative approach for detecting cracks on levee surfaces by integrating an Auxiliary Classifier Generative Adversarial Network (ACGAN) for data augmentation with a Faster R-CNN model enhanced by an attention mechanism. The ACGAN-based augmentation aims to generate synthetic images that enrich data variability in the original dataset. The attention-optimized Faster R-CNN is designed to improve detection precision, particularly for small objects and fine cracks that are difficult to distinguish from the background. Experimental results demonstrate that the incorporation of ACGAN improves detection performance, increasing both the mean Average Precision (mAP) and Average Recall (AR). The model achieved an mAP of approximately 0.56 at IoU = 0.50 and 0.34 at IoU = 0.75, while the AR (maxDets = 100) reached 0.55, indicating a strong capability in identifying most crack instances. When trained on the combined dataset of original and synthetic images, the Faster R-CNN model reached a precision of 0.92 for the severe crack class, while performance for minor cracks remained lower (precision 0.78). Adjusting the confidence threshold to 0.65 improved detection reliability by reducing noise and retaining high-confidence predictions. Improved performance in detecting severe cracks supports timely maintenance and repair decisions. This study demonstrates the effectiveness of GAN-based data augmentation and attention-enhanced object detection for automated structural health monitoring (SHM) of levee infrastructure
Co-Authors Achmad Pahrul Rodji Agus Riyanto Agus Riyanto Ajat Zatmika Alif Utomo Andika Saputra Andrian Muljadi Angga Mulya Lelana Avip Kurniawan Chusna, Nuke L Darmawan, Risanto Darmawan, Risanto Darmiyanti, Lydia Dhistianti Mei Rahmawantari Dwi Ismiyana Putri Faisal Ruswanto Getah Ester Hayatulah Gita Puspa Artiani Gita Puspa Artiani, Gita Puspa Harjono Padmono Putro Harjunadi Wicaksono, Harjunadi Harry Dwiyana Kartika Harry Dwiyana Kartika Herry Wahyono Herwanto Herwanto Herwanto Herwanto Hidayat, M. Fahmi Hudzaifah Al jihad Indra Permana Solihin Kusmanto, Panji Lukman Aditya Lydia Darmiyanti Moch Fachri Mochammad Fachri Nuke L. Chusna Nuke L. Chusna Nur Hikmah Nur Hikmah Nurpadilah, Aini Pranoto, Setiadi Wiro Priyadi, Wiwit Rahayu, Tuntun Rahmat Hidayat Reztrianti, Diajeng Ridwan Raafi'udin Saludin, Saludin Tuntun Rahayu Wargijono Utomo Wiga Ariani Yonas Prima Arga Rumbyarso Yusuf Aras Ronisah