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All Journal Buletin Ilmiah Sarjana Teknik Elektro Jurnal IPTEK Bagi Masyarakat
Mirza, Mohammed Aquil
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Electrical & Electronics Engineering Mechanical Engineering Physics Public Health Social Sciences

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A Lightweight Hybrid Template-Matching–CNN Framework with Attention-Guided Fusion for Robust Small Object Detection Zangana, Hewa Majeed; Omar, Marwan; Mirza, Mohammed Aquil; Cao, Xinwei; Wani, Sharyar
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14751

Abstract

Small object detection in aerial and surveillance imagery remains challenging due to low resolution, occlusion, and background clutter. This study introduces a novel hybrid detection framework that fuses template matching with a deep learning detector (Faster R-CNN) through an attention-guided decision fusion mechanism. The novelty lies in (i) a dual-stage fusion pipeline that integrates precise structural cues from template matching with deep semantic features, and (ii) a custom scale-aware focal loss, adapted from Focal Loss to emphasize hard and small objects by dynamically increasing penalties for low-confidence predictions. Evaluated on a Pascal VOC subset (1000 images, 5 classes), the proposed system achieves an mAP improvement of 3.5% over the Faster R-CNN baseline and surpasses YOLO-Lite and R-CNN variants in precision and recall. The hybrid design adds only a minimal computational overhead (0.45 s/image vs. 0.42 s for Faster R-CNN), demonstrating favorable efficiency–accuracy trade-offs suitable for scalable deployment. These findings highlight the framework’s robustness, particularly in scenes containing occlusion, clutter, or visually small targets. Limitations regarding template dependency are discussed, along with future directions for automatic template generation and real-time video adaptation.
Digitalizing Green Fashion Design via Service-Learning to Empower Fashion SMEs Nasution, Rizki Amelia; Tarigan, M. Faisal Afiff; Mirza, Mohammed Aquil
Jurnal IPTEK Bagi Masyarakat Vol 5 No 2 (2025)
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/j-ibm.v5i2.1369

Abstract

This community service research introduces a model of digitalizing green fashion through a service-learning approach implemented at Rumah Jahit Nila, a fashion MSME in Medan. The intervention includes intensive training on 3D design software such as CLO 3D and Marvelous Designer, the use of AI-assisted tools like Repsketch for pattern optimization, and production mentoring throughout a 12-month project cycle. Measurements show an increase in students' average technical competency scores from 55.1 to 76.9, representing a gain of 21.8 points. Partner evaluation was conducted through satisfaction questionnaires and field observations, both of which indicated positive acceptance of digital patterns and user guides. Claims regarding waste reduction and material efficiency are currently indicative, as direct quantitative metrics such as percentage reduction in fabric use or paper pattern sheets are not yet available; thus, further measurement is recommended. This study offers a replicable digital technology-based community service model for empowering sustainable fashion MSMEs.
Co-Authors Cao, Xinwei Nasution, Rizki Amelia Omar, Marwan Tarigan, M. Faisal Afiff Wani, Sharyar Zangana, Hewa Majeed