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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal Sensi: Strategic of Education in Information System Community Development Journal: Jurnal Pengabdian Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Cakrawala Informasi (JCI) Lamahu: Jurnal Pengabdian Masyarakat Terintegrasi Edu Komputika Journal
Khaira, Mulil
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences

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Journal : Edu Komputika Journal

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Benchmarking YOLOv3 and SSD: A Performance Comparison for Multi-Object Detection Prasetyo, Septian Eko; Atmaja, Chandra; Ardian, Muhammad; Ardhiansyah, Alfian; Sudarni, Ajeng Rahma; Khaira, Mulil
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.28005

Abstract

Multiple object detection remains a significant challenge in the field of computer vision. One of the key factors affecting detection performance is the feature extraction process, especially when objects are relatively small or positioned closely together. This study aims to compare the effectiveness of two popular object detection models, YOLO (You Only Look Once) and Single Shot MultiBox Detector (SSD), in detecting multiple objects within images. These models were selected due to their reported high accuracy and real-time processing capabilities, outperforming traditional methods such as the Hough Transform, Deformable Part-based Models (DPM), and conventional CNN architectures. The models were evaluated using a subset of the PASCAL VOC dataset, which includes object categories such as aircraft, faces, cars, and others, with a total of 1,447 annotated images used in training and testing. The evaluation metric used was mean Average Precision (mAP) to assess detection accuracy. Experimental results indicate that YOLO achieves a mAP of 82.01%, while SSD achieves 70.47%. These findings demonstrate that YOLO provides better performance in detecting multiple objects under the same conditions. Overall, this study confirms the advantages of YOLO in scenarios requiring fast and accurate multi-object detection, highlighting its potential for deployment in real-time applications such as autonomous vehicles, surveillance systems, and robotics. The main contribution of this study lies in providing a comparative performance benchmark between YOLO and SSD on a standard multi-object dataset to guide practical model selection in real-time computer vision tasks.
Ontology Engineering for Modeling National Student Achievements in Higher Education Sudarni, Ajeng Rahma; Prasetyo, Septian Eko; Ardhiansyah, Alfian; Khaira, Mulil
Edu Komputika Journal Vol. 11 No. 2 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i2.28254

Abstract

The need for structured and semantically rich data in higher education underscores the role of ontology-based knowledge modeling. This study develops an ontology to represent national-level student achievements, covering key aspects such as institution, achievement field, category, year, level, and student status. Using a formal ontology engineering approach, the ontology was developed in Protégé and encoded in OWL. Evaluation involved technical validation and reasoning tests including class subsumption, consistency checking, instance classification, and rule-based inference to assess logical soundness and semantic correctness. Description Logic (DL) queries were also executed based on competency questions to evaluate the ontology’s ability to support semantic querying. The results demonstrate that the ontology effectively supports knowledge inference and structured data retrieval, offering strong potential for integration within semantic web environments. This provides a foundation for data interoperability and knowledge sharing across educational systems at the national level. Future work includes expanding the ontology to incorporate dynamic achievement updates and linking with external educational data sources.
Co-Authors Aditya Putra Ramdani Ajeng Rahma Sudarni Al Amin, Muhammad Zainudin Alfian Ardhiansyah Anis Susanti Ardhiansyah, Alfian Atmaja, Chandra Basirudin Ansor Dirgahayu, R Teduh Fachry Abda El Rahman Galih Malela Damaraji Ismiyati, Ismiyati Muhammad Ardian, Muhammad Nova Christina Sari Prasetyo, Septian Eko Risanda Alirastra Budiantoro Sandy Arief Septian Eko Prasetyo Sudarni, Ajeng Rahma Taufiq Hidayat