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All Journal Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Ilmiah Intech : Information Technology Journal of UMUS Journal of Computer Networks, Architecture and High Performance Computing JAMU : Jurnal Abdi Masyarakat UMUS Community: Jurnal Pengabdian Pada Masyarakat Scripta Technica: Journal of Engineering and Applied Technology Servitia: Journal of Community Service and Engagement Jurnal Pengabdian Masyarakat Indonesia Jurnal JEETech Nucleus Journal Technema: Journal of Intelligent Engineering and Computing
Titik Khotiah
Institut Teknologi dan Bisnis Ahmad Dahlan Lamongan
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Explainable AI (XAI) Analysis Using SHAP for Credit Card Fraud Yanuangga Galahartlambang; Titik Khotiah; Ilham Basri K; Masrur Anwar
Journal of Engineering and Applied Technology Vol 1 No 2 (2025): December: Scripta Technica: Journal of Engineering and Applied Technology
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/scxk4755

Abstract

The increased use of credit cards in digital payment systems has also increased the risk of transaction fraud, which has led to financial losses and a decline in user confidence. Various machine learning approaches have been developed to automatically detect fraud, but most high-performance models are black-box in nature, making them difficult to explain and unsupportive of auditing and decision-making processes. This study aims to analyze the application of Explainable Artificial Intelligence (XAI) using the SHAP (SHapley Additive exPlanations) method in credit card fraud detection systems. An imbalanced credit card transaction dataset was used as experimental data, with two classification models, namely Logistic Regression as a baseline and Random Forest as an ensemble model. Performance evaluation was conducted using Precision, Recall, F1-score, and Average Precision (PR-AUC) metrics, which are more suitable for imbalanced data cases. The experimental results show that the Random Forest model performs better than Logistic Regression, especially in terms of Precision, F1-Score, and PR-AUC metrics. Explainability analysis using SHAP was performed to obtain global and local explanations for the model's decisions. Global explanations successfully identified the dominant features that influence fraud predictions, while local explanations provided an overview of the contribution of individual features to specific fraud transactions. The results of this study show that the application of SHAP can improve the transparency and clarity of fraud detection model decisions without sacrificing prediction performance, thereby potentially supporting the development of a more reliable and easily audited fraud detection system.  
Implementation of SHAP in Ensemble Learning for Interpreting Audit Risk Classification: A Preliminary Study Using a Public Audit Dataset Yanuangga Galahartlambang; Titik Khotiah; Ilham Basri K; Masrur Anwar; Mohamad Akhsan Rofiqi
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 2 (2026): : June: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ensemble learning models can achieve strong predictive performance on structured audit-risk data, yet their complex decision logic can limit transparency and technical accountability. This study develops a proof-of-concept Explainable Artificial Intelligence pipeline using SHapley Additive exPlanations (SHAP) to interpret Random Forest and XGBoost classifiers on the public UCI Audit Data. The experiment processed 776 observations and 27 columns, with one missing value in Money_Value imputed using the median of 0.09 and the non-numeric LOCATION_ID removed, resulting in 25 predictors and one binary target. A stratified 80:20 split with random_state = 42 produced 620 training observations and 156 test observations, with Random Forest achieving 1.0000 across accuracy, precision, recall, F1-score, and ROC-AUC, while XGBoost achieved 0.9936 accuracy, 1.0000 precision, 0.9836 recall, 0.9917 F1-score, and 1.0000 ROC-AUC. SHAP analysis identified Audit_Risk as the dominant global predictor, while its contribution to a representative at-risk prediction reached +5.82, shifting the raw model margin from −0.506 to 5.107 and yielding an estimated probability of approximately 0.994. However, because the dataset's target construction is closely associated with the Audit Risk Score, retaining Audit_Risk as a predictor introduces potential structural target leakage. The findings position the near-perfect performance as evidence of successful pipeline implementation and model interpretability rather than external generalization, supporting future leakage-controlled ablation, cross-validation, and external-data validation.
Pendampingan Implementasi Sistem Informasi Laboratorium untuk Penguatan dan Digitalisasi Laboratorium Klinik Pratama Semarang Yanuangga Galahartlambang; Titik Khotiah; Ilham Basri K.; Masrur Anwar; Bayu Malikhul Askhar
Journal of Community Service and Engagement Vol 2 No 1 (2026): August: Servitia: Journal of Community Service and Engagement
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/qecc5w64

Abstract

The management of clinical laboratories that relies on forms and repetitive record-keeping can complicate data traceability and document control. This community service activity aimed to assist the implementation of a Laboratory Information System (LIS) at a Primary Clinical Laboratory in Semarang. A participatory approach was applied through needs assessment, digital workflow arrangement, system configuration, SOP development, workshops, assistance, and evaluation. A total of 12 participants completed the pre-test and post-test. The mean score increased from 59.75 to 79.50, representing a difference of 19.75 points or 33.1%. All participants experienced an increase in scores; nine participants achieved an increase of at least 30%, while six participants obtained a final score of at least 80. The Wilcoxon test indicated a significant difference between scores before and after the activity (W = 0; p < 0.001). The assistance resulted in a digital workflow covering registration, test requests, specimen identification, result recording, validation, and reporting, supported by access-right management and document control. User capacity strengthening was an important component for supporting the sustainability of LIS implementation after the assistance period ended. The findings indicate changes in participants’ understanding and work-process organization. The effects on service time, transcription errors, and continued system use require further monitoring.
Co-Authors Abdul Rohman Afifatul Aprilia Yani Solekhah Aranta Prista Dilasari Bayu Malikhul Askhar David Fahmi Abdillah David Fahmi Abdillah Fery Arianto Heti Nur Ani Ilham Basri K Ilham Basri K Jihan Faradhilah Ramadhani Jumain Jumain Jumain Masrur Anwar Masrur Anwar Mohamad Akhsan Rofiqi Mohammad Bastian Dwiki Rahmat Ninin Nike Pebriyanti Yanuangga Galahartlambang Zahruddin Fanani Zahruddin Fanani