Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal Journal of Informatics and Information Security Journal of Intelligent Systems for Community Development (JISCoDe)
Mochamad Galih Pradipta
Unknown Affiliation
Computer Science & IT

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Analisis Keamanan Aplikasi Fintech di Indonesia: Studi Kasus OVO, GoPay, ShopeePay dan Dana Bertnardo Mario Uskono; Rian Wijaya; Mochamad Galih Pradipta; Aldiansyah Kusnadi
Journal of Informatic and Information Security Vol. 5 No. 2 (2024): Special Issue (Manajemen Sekuriti)
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/fhyes229

Abstract

This research analyzed the security of four leading digital wallet applications in Indonesia (OVO, GoPay, ShopeePay, and DANA) using the Mobile Security Framework (MobSF) to identify potential user data security vulnerabilities. Through static analysis methods, the study examined security parameters such as weak encryption, SSL bypass, dangerous permissions, and hidden secret detection, aiming to provide a comprehensive insight into fintech data protection. Research findings revealed that all four applications were at security level four, with security scores ranging from 45-48, which highlighted significant security improvements for ShopeePay and DANA from the previous year, while OVO and GoPay showed minimal changes, underscoring the importance of continuous enhancement in digital financial application security.
Detection and Classification of Indonesian Batik Motifs Using YOLOv11 Mochamad Galih Pradipta; Herlawati Herlawati; Rakhmat Purnomo
Journal of Intelligent Systems for Community Development Vol. 1 No. 2 (2026): 2026: JISCoDe Volume 1 Issue 2 Year 2026
Publisher : Indonesian Artificial Neural Network Society

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

Abstract

The complexity of asymmetric visual patterns and overlapping ornaments in batik motifs presents challenges for automatic identification in modern applications. The limitations of conventional classification methods have created a need for computer vision techniques capable of accurate object localization. This study aimed to detect and classify various Indonesian batik motifs in real time using the YOLOv11 model. The research methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM), consisting of six structured phases, from business understanding to system deployment. The study focused on major variations of batik motifs originating from different regions of Indonesia, with model performance evaluated using the mean Average Precision (mAP) metric. The results demonstrated that: (1) the YOLOv11 model achieved an mAP50 of 77%; (2) data augmentation effectively reduced the risk of model overfitting; and (3) the trained detection model was successfully integrated into a web-based platform, enabling users to perform real-time image testing.
Co-Authors Aldiansyah Kusnadi Bertnardo Mario Uskono Herlawati Herlawati Rakhmat Purnomo Rian Wijaya