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Pemeliharaan dan Perbaikan Komputer serta Printer di Toko Puricom Stephen Stephen; Andik Yulianto; Ni’matul Ma’muriyah
National Conference for Community Service Project (NaCosPro) Vol. 7 No. 01 (2025): The 7th National Conference for Community Service Project 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/nacospro.v7i01.11008

Abstract

Kegiatan  ini dilaksanakan di Toko Puricom, sebuah usaha jasa yang bergerak dalam bidang servis dan pemeliharaan komputer serta printer. Permasalahan utama yang dihadapi mitra adalah tingginya frekuensi keluhan kerusakan perangkat dari pelanggan serta kebutuhan peningkatan efisiensi dalam proses perbaikan.  ini dilakukan melalui observasi, pendampingan langsung, dan praktik teknis yang mencakup pengecekan perangkat, perbaikan komponen, serta pemeliharaan rutin perangkat keras. Hasil pelaksanaan menunjukkan bahwa kegiatan ini tidak hanya memberikan kontribusi dalam membantu operasional toko, tetapi juga meningkatkan pemahaman teknis mahasiswa terhadap perangkat keras komputer dan printer. Mahasiswa mampu menangani masalah seperti penggantian sparepart, instalasi driver, hingga pembersihan komponen secara mandiri. Dokumentasi kegiatan, termasuk logbook harian dan laporan akhir, juga disusun untuk memberikan evaluasi menyeluruh terhadap proses . Kegiatan ini dapat menjadi model pelatihan praktis bagi mahasiswa dalam menghadapi tantangan kerja nyata di bidang teknologi informasi dasar.
The Effectiveness of Smart Traffic Management system in Indonesia: Systematic Literature Review Hendi Sama; Andik Yulianto; Kevin Lius
CESS (Journal of Computer Engineering, System and Science) Vol. 11 No. 1 (2026): Januari 2026
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v11i1.71654

Abstract

Traffic congestion in Indonesia causes significant economic losses and impacts the quality of life of the community. The Smart Traffic Management System (STMS) emerges as a technology-based solution that integrates the Internet of Things (IoT), Artificial Intelligence (AI), and big data to manage vehicle flow adaptively. This research employs the Systematic Literature Review (SLR) method with the PRISMA approach to analyze the effectiveness of STMS in reducing congestion and carbon emissions, both in Indonesia and in other countries. The reviewed articles indicate that STMS can reduce vehicle travel time by 8-15%, improve traffic flow smoothness by up to 50%, and decrease carbon emissions by 30-40% per year. Trials in Jakarta demonstrate a 15% increase in traffic smoothness and a reduction in travel time during peak hours. These findings confirm that the implementation of STMS has tremendous potential to realize a more efficient, safe, environmentally friendly, and sustainable urban transportation system.
Enhancing Smart Wheelchair Control: A Comparative Study of Optical Flow and Haar Cascade for Head Movement Nimatul Ma Muriyah; Paerin Paerin; Andik Yulianto
Journal of Information System and Informatics Vol 7 No 4 (2025): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v7i4.1302

Abstract

The development of Artificial Intelligence, particularly in Computer Vision, has enabled real-time recognition of human movements such as head gestures, which can be utilized in smart wheelchairs for users with limited mobility. This study compares two lightweight non-deep-learning methods Lucas–Kanade Optical Flow and Haar Cascade Classifier for real-time head movement detection. Both methods were implemented in Python using OpenCV and tested in four basic directions (left, right, up, and down) under three different lighting conditions: bright, normal, and dim. Each condition consisted of 16 trials per method, resulting in a total of 96 trials. The evaluation focused on detection accuracy and decision time. Under bright lighting, Optical Flow achieved 87.5% accuracy with a decision time of 0.338-1.41 s, while Haar Cascade reached 50% accuracy with 0.616–1.20 s. Under normal lighting, Optical Flow maintained 87.5% accuracy with 0.89–1.21 s, compared to Haar Cascade’s 68.75% accuracy with 0.83–1.25 s. Under dim lighting, Optical Flow improved to 93.8% accuracy with 0.90–1.31 s, whereas Haar Cascade dropped to 62.5% accuracy with 0.89–1.58 s. These findings confirm that Optical Flow delivers more reliable and adaptive performance across varying illumination levels, making it more suitable for real-time smart wheelchair control. This study contributes to the development of affordable assistive technologies and highlights future directions for multi-user testing and hardware integration.