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All Journal ComTech: Computer, Mathematics and Engineering Applications International Journal of Computer Science and Humanitarian AI
William William
Bina Nusantara University
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mathematics

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Deteksi Kemacetan Lalu Lintas Melalui Kamera menggunakan Pin Hole Algorithm Samuel Mahatmaputra; Erwin Permata; William William
ComTech: Computer, Mathematics and Engineering Applications Vol. 2 No. 2 (2011): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v2i2.2833

Abstract

This paper describes the modifications to the Pinhole algorithm and applies it to the detection of highway congestion using an IP camera. This algorithm can detect the congestion on the highway with a fairly high degree of accuracy and the small possibility of false alarms, so that it helps reporting congestion and road users to take into account the best and quickest way. The methods used in this research are literature study, questionnaire analysis, and evaluation of application performance. The application made in this study can be further developed for combined detection with many cameras, and can support traffic regulation as well as SMS gateway service to check the condition of a road segment. At the end of this paper are presented techniques and methodology of determining the parameter values used in the calibration. 
An End-to-End Architecture for Stock Market Prediction Integrating Mobile Application, Backend Services, and ML/DL Models Abraham Kefas Wilham; William William; Sonya Rapinta Manalu
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 1 (2026): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i1.15154

Abstract

Prior research on stock market prediction has predominantly focused on algorithmic accuracy, leaving a significant research gap in the system-level realization required for real-world delivery. This paper addresses this disparity by presenting an end-to-end stock prediction delivery system that operationalizes trained machine learning models within a mobile-centric architecture. Unlike model-centric studies limited to offline evaluation, this work focuses on the rarity of system-level implementation. Market data are periodically ingested into a managed relational database, where predictions are generated using a fixed historical window and persisted for downstream access. A cross-platform mobile application serves as the primary user interface, providing structured access to historical prices, predictions, and accuracy metrics via backend APIs without local model inference. A key novelty is the implementation of an in-memory caching layer to optimize responsiveness for repeated mobile access. Experimental results demonstrate that this architecture significantly improves efficiency, reducing average API response times by approximately 94% from 817 ms to 48,7778 ms compared to direct database queries. These findings underscore the critical role of mobile-oriented system design in bridging the gap between predictive modeling and practical deployment, while also highlighting the importance of scalable infrastructure, efficient data synchronization, and reliable service integration for sustainable real-world financial technology applications.
Co-Authors Abraham Kefas Wilham Erwin Permata Samuel Mahatmaputra Sonya Rapinta Manalu