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INOVTEK Polbeng - Seri Informatika
Published by Politeknik Negeri Bengkalis
ISSN : 25279866     EISSN : -     DOI : https://doi.org/10.35314
Core Subject : Science,
Computer Science & IT
The Journal of Innovation and Technology (INOVTEK Polbeng—Seri Informatika) is a distinguished publication hosted by the State Polytechnic of Bengkalis. Dedicated to advancing the field of informatics, this scientific research journal serves as a vital platform for academics, researchers, and practitioners to disseminate their insightful findings and theoretical developments. Scope and Focus: INOVTEK Polbeng - Seri Informatika focuses on a broad spectrum of topics within informatics, including but not limited to Web and Mobile Computing, Image Processing, Machine Learning, Artificial Intelligence (AI), Intelligent Systems, Information Systems, Databases, Decision Support Systems (DSS), IT Project Management, Geographic Information Systems, Information Technology, Computer Networks and Security, and Wireless Sensor Networks. By covering such a wide range of subjects, the journal ensures its relevance to a diverse readership interested in both the practical and theoretical aspects of informatics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "Vol. 11 No. 2 (2026): May (Inpress)" : 2 Documents clear
Centralized Access Management for Vertical Housing Using Edge Computing and Deep Learning Wahyu, Zaky Oktavianto; Al Rasyid, M. Udin Harun; Setiawardhana
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/dnscbn25

Abstract

The implementation of security systems in vertical housing often has a choice between high infrastructure costs from decentralized hardware and privacy risks from cloud solutions. This study presents a prototype for a centralized access management system utilizing edge computing (Intel NUC) as a local server to authenticate residents at various access points. The system uses Frigate NVR for lightweight real-time object detection and the ArcFace Deep Learning model for facial recognition. It processes all biometric data locally to protect privacy. We used a dataset of three registered subjects to test the experiment. The tests looked at how well the system worked at different distances (1 to 5 meters), in different lighting conditions (daylight and infrared), with different types of facial occlusions (medical masks), and with 2D spoofing attacks (print and digital media). Using a confusion matrix over 50 random test samples that included both authorized users and unknown intruders, the system got a global accuracy of 80.0%. The system also had a Genuine Acceptance Rate (GAR) of 86.6%. The system was very stable when it was 1 to 2 meters away, but it didn't work as well in extreme conditions. With an average CPU usage of 46.87% and physical control latency via the MQTT protocol of less than 0.2 seconds, resource efficiency was kept up. These results show that the proposed edge architecture can work as a responsive and computationally efficient prototype for smart apartment security. They also show that liveness detection needs to be improved in the future to reduce the risk of digital spoofing.
A Web-Based Decision Support System for Determining High-Achieving Students Using The Simple Additive Weighting (SAW) Method at SMK Kanisius Ungaran Cesillia Ayu Kumala Sari; Yoannes Romando Sipayung
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 2 (2026): May (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/n202pv96

Abstract

This study develops a web-based Decision Support System (DSS) to assist in determining academically high-achieving students at SMK Kanisius, Ungaran. The current evaluation process in the school relies largely on manual assessment, which can make the management of multiple evaluation criteria time-consuming and difficult to organize systematically. To support a more structured evaluation process, this research applies the Simple Additive Weighting (SAW) method as a multi-criteria decision-making approach. Four assessment criteria were used in the system: report card average scores, school examination results, non-academic achievements, and attendance. Each criterion was assigned a weight based on institutional priorities. The system was implemented as a web application using Next.js and React.js for the front-end interface, while Supabase with PostgreSQL was used for data storage and management. The SAW procedure integrated into the system includes score normalization, weighted aggregation, and the generation of ranking results for students. A sample dataset consisting of five student alternatives was used to demonstrate the calculation process and system functionality. The results show that the system can process student evaluation data and generate ranking outputs based on the predefined criteria and weights. In the calculation example, the highest-ranked student obtained a final score of 0.9902. The developed system demonstrates how the SAW method can be operationalized within a web-based platform to support the organization and processing of multi-criteria student evaluation data. The study primarily contributes a practical implementation of a DSS for academic assessment in vocational secondary education contexts.

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