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All Journal JURTEKSI INOVTEK Polbeng - Seri Informatika
Setiawardhana
Politeknik Elektronika Negeri Surabaya
Computer Science & IT

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Centralized Access Management for Vertical Housing Using Edge Computing and Deep Learning Zaky Oktavianto Wahyu; M. Udin Harun Al Rasyid; Setiawardhana
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 2 (2026): May
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.
IMAGE PROCESSING SYSTEM FOR SEMICONDUCTOR CHIP COUNTING AT PT ELEKTRONIK INDONESIA Hasbullah Hasbullah; Agus Indra Gunawan; Setiawardhana
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4314

Abstract

Abstract: Conventional semiconductor chip counting at PT Elektronik Indonesia relies on manual weighing, which is prone to human error and inefficiency. This study proposes a desktop-based counting system using a digital scanner and image processing. The novelty lies in integrating horizontal-vertical projection with probabilistic Hough transform to robustly detect grid lines, form square cells, and enable accurate unit estimation via average intensity analysis, eliminating the need for reference weighing. Experiments on 15 actual chip images yielded an error rate of 0.009519% and up to 73.674%time efficiency gains compared to the manual method. The system reduces operator dependency, minimizes errors, and accelerates counting, providing a practical machine vision solution for semiconductor production. Keywords: chip counting; image processing; probabilistic hough transform; grid line detection; time effeciency. Abstrak: Penghitungan chip semikonduktor konvensional di PT Elektronik Indonesia bergantung pada penimbangan manual, yang rentan terhadap kesalahan manusia dan kurang efisien. Penelitian ini mengusulkan sistem penghitungan berbasis desktop menggunakan scanner digital dan pengolahan citra. Kebaruan terletak pada integrasi proyeksi horizontal-vertikal dengan probabilistic Hough transform untuk mendeteksi garis grid secara kuat, membentuk sel persegi, serta memungkinkan estimasi unit akurat melalui analisis intensitas rata-rata, sehingga menghilangkan kebutuhan penimbangan referensi. Eksperimen pada 15 citra chip aktual menghasilkan tingkat kesalahan 0,009519% dan peningkatan efisiensi waktu hingga 73,674% dibandingkan metode manual. Sistem ini mengurangi ketergantungan operator, meminimalkan kesalahan, dan mempercepat penghitungan, menyediakan solusi machine vision praktis untuk produksi semikonduktor. Kata kunci: penghitungan chip; pengolahan citra; probabilistic Hough transform; deteksi garis grid; efisiensi waktu.
Co-Authors Agus Indra Gunawan Bima Sena Bayu Dewantara Hasbullah Hasbullah M Udin Harun Al Rasyid, M Udin Harun Muhammad Anwar Sanusi Riyanto Sigit Zaky Oktavianto Wahyu