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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Mantik
Rafif Ramadhan Al Yarda
Universitas Nasional
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Implementation of E-Archives at the Faculty of Communication and Information Technology Using the Web-Based Waterfall Method Rafif Ramadhan Al Yarda; Iskandar Fitri; Benrahman
Jurnal Mantik Vol. 4 No. 4 (2021): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2021.1205.pp2532-2544

Abstract

Information technology is currently developing rapidly so that every aspect of activity cannot be separated from the name technology, of course in the administrative aspect it also really requires fast and accurate information technology. At the Faculty of Communication and Information Technology, National University, currently still using an archive system that has not been developed again, which often causes data synchronization. With the E-Archives application, it is hoped that archiving activities can assist administrative activities accurately so that it can become a reference in making the right decisions
Explainable AI-Based Hybrid LightGBM–LSTM for Banking Fraud Detection Rafif Ramadhan Al Yarda; Fauziah Fauziah
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 4 (2026): Research Articles October 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i4.16711

Abstract

Banking fraud detection faces changing transaction behaviors, class imbalance, and the need to interpret the outputs of complex models. This study develops a Fraud Detection and Analysis System (FDAS) as a Decision Support System in the form of a prototype that integrates tabular and temporal modeling, imbalance handling, operational validation, and post-prediction interpretation. A quantitative experimental approach was employed using a synthetic transaction dataset constructed based on the characteristics of observed fraud and normal patterns, comprising 50 accounts, consisting of 25 fraud accounts and 25 normal accounts, covering the period from January to December 2025. Account-level splitting was used to prevent historical information leakage across accounts, while TPH-SMOTE and KHOI-SMOTE were applied only to the training set. LightGBM and LSTM were trained independently to represent tabular and temporal data, respectively, and their probabilities were subsequently combined through weighted decision-level late-fusion, with SOP serving as the operational validation layer. SHAP was employed as a post-hoc explanation to support interpretability, traceability, and human oversight. Across 1,000 paired evaluation units, the final configuration achieved an Accuracy of 97.4%, Precision of 96.8%, Recall of 96.2%, F1-score of 96.5%, and ROC-AUC of 0.984. Comparison across configurations indicated that the highest performance was achieved by the integrated configuration within the experimental environment used. FDAS demonstrates end-to-end integration to support fraud investigation through recommendations and interpretive information, while the final decision remains with the analyst. The use of synthetic data, 50 accounts, and a prototype status limits direct generalization to operational banking populations and production readiness.
Co-Authors Ben Rahman Fauziah Fauziah Iskandar Fitri