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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika: Jurnal Pengembangan IT
Mareta, Arvin
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Computer Science & IT

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Fraud Detection in Mobile Phone Recharge Transactions Using K-Means and T-SNE Visualization Sakti, Irwin; Mareta, Arvin; Wasito, Ito
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14330

Abstract

The surge in digital transactions has introduced vulnerabilities in mobile recharge systems, making them susceptible to fraudulent activities that compromise financial security and operational integrity. This study presents to address these challenges by employing a novel fraud detection framework that integrates K-Means clustering and t-Distributed Stochastic Neighbour Embedding (t-SNE) visualization. This work advances the field by integrating scalable, unsupervised learning techniques with robust visualization tools, offering a practical framework for fraud detection in mobile recharge systems. Leveraging a dataset of over 200,000 transactions, this research systematically identifies anomalies indicative of fraudulent behaviour, focusing on key transactional attributes such as processing times, geographic patterns, and error frequencies. The methodology begins with data preprocessing to ensure consistency, followed by the application of K-Means clustering to partition transactions into meaningful clusters. To enhance interpretability, t-SNE visualization is employed, enabling a clear representation of high-dimensional data and the identification of anomalous patterns. A comparative analysis with Autoencoders highlights the strengths of K-Means in terms of computational efficiency, interpretability, and clustering quality, as evidenced by higher Silhouette Scores (0.6215) and lower Davies-Bouldin Index values (0.7074). The combination of K-Means and t-SNE enables service providers to identify fraudulent activities with greater precision, offering actionable insights to mitigate financial risks. This study not only addresses the critical need for robust fraud detection systems but also lays a strong foundation for future advancements through the integration of hybrid models and enhanced feature engineering, demonstrating its adaptability to similar domains.
Transformasi Digital Sistem Kehadiran untuk Budaya Hybrid Work dengan TOGAF Framework Mareta, Arvin; Yohannis, Alfa
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8335

Abstract

Since the end of the COVID-19 pandemic in 2023, hybrid work or work from anywhere (WFA) methods have become increasingly popular. While this work culture offers flexibility, it also poses challenges for companies in monitoring employee attendance. This study proposes the implementation of the TOGAF Framework to design an attendance management system that supports a hybrid work culture. The design process begins with a preliminary phase to identify user needs and the limitations of traditional systems. The system is designed using TOGAF ADM (Architecture Development Method), covering phases such as Architecture Vision, Business Architecture, Information System Architecture, and Technology Architecture. Technologies such as IoT, GPS, facial recognition, and mobile applications are employed to ensure system flexibility and accuracy. Testing is conducted in two stages: Blackbox Testing to verify functionality against specifications, and User Acceptance Testing (UAT) to evaluate system usability in real-world conditions. The test results show that the system meets all specifications, improves operational efficiency by up to 40%, and ensures the security and accuracy of attendance data. The system is also designed for integration with other modules, such as payroll and HRIS, to support strategic decision-making. This approach provides an effective and adaptive solution for monitoring employee attendance, whether working remotely or in the office, and enhances productivity in modern work environments.
Beyond Traditional QoS Management- Harnessing Machine Learning for Predictive Network Service Optimization Mareta, Arvin; Sakti, Irwin; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14664

Abstract

Quality of Service (QoS) is a fundamental aspect of modern computer networks, directly influencing performance and user experience. Key parameters such as latency, throughput, packet loss, and jitter play crucial roles in determining network efficiency. Traditional QoS management approaches, often rule-based or heuristic-driven, lack adaptability to dynamic network conditions. This study explores the application of machine learning techniques to predict QoS using historical network data, enabling proactive network optimization. We employ multiple predictive models, including linear regression, random forest, and deep learning, to analyze network performance trends and forecast QoS degradation. Experimental results demonstrate that machine learning significantly enhances prediction accuracy compared to conventional methods, allowing for more effective resource allocation and congestion control. The findings highlight the potential of data-driven approaches in real-time network management, reducing latency fluctuations and improving service reliability. Moreover, deep learning models outperform traditional statistical techniques in recognizing complex patterns within network data, making them a promising solution for next-generation network optimization. The proposed methodology not only improves predictive accuracy but also offers a scalable framework for automated QoS management in cloud computing, IoT, and 5G environments. Future work will focus on refining model generalization across diverse network conditions and integrating federated learning for privacy-preserving QoS predictions. This research underscores the transformative role of machine learning in enhancing network service quality and operational efficiency.
Co-Authors Ito Wasito Sakti, Irwin Santoso, Handri Yohannis, Alfa