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All Journal ILKOM Jurnal Ilmiah Madani : Indonesian Journal of Civil Society Jurnal Teknik Informatika (JUTIF) Mejuajua Journal Computer Science and Informatic Systems : J-Cosys Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Pengabdian Pada Masyarakat Kapas: Kumpulan Artikel Pengabdian Masyarakat Jurnal Pengabdian Masyarakat dan aplikasi Teknologi Jurnal Pengabdian Sosial Engagement: Jurnal Pengabdian Kepada Masyarakat
Muhaimin Hasanudin
Universitas Mercu Buana, Jakarta, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Enhanced Violence Detection in CCTV Using LSTM Hasanudin, Muhaimin; Santoso, Hadi; Wahab, Abdi; Indrianto, Indrianto; Kuswardani, Dwina; Ridlan, Ahmad
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2318.196-202

Abstract

Violence detection in CCTV footage remains a critical challenge for public safety, necessitating automated solutions to overcome human monitoring limitations. This study proposes an LSTM-based framework to improve detection accuracy by analyzing temporal patterns in surveillance videos. Using a dataset of 2,000 videos (1,000 violent/1,000 non-violent), the model extracts spatial-temporal features via optical flow and achieves 93% training accuracy and 91% test accuracy, with a precision of 92% and AUC of 0.94. Results demonstrate significant improvements over traditional methods, particularly in dynamic scenarios, though performance dips for occluded actions or weapon-related violence. The discussion highlights the model’s real-time applicability, computational efficiency (120 ms latency per segment), and alignment with smart city surveillance needs. Limitations include dataset diversity and environmental variability, suggesting future directions in multi-modal data fusion and edge computing. This research advances AI-powered security systems, offering a robust tool for proactive threat detection while underscoring the need for scalable, context-aware solutions.
Deep Learning-Based Autism Detection Using Facial Images and EfficientNet-B3 Hasanudin, Muhaimin; Afiyati, Afiyati; Budiarto, Rahmat; Wahab, Abdi; Jokonowo, Bambang; Indrianto, Indrianto; Yosrita, Efy; Hanifah, Nurul Afif
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4574

Abstract

This study presents a novel deep learning approach for early detection of Autism Spectrum Disorder (ASD) using facial image analysis. Leveraging the EfficientNet-B3 model, the research addresses limitations in traditional diagnostic methods by autonomously extracting discriminative facial features associated with ASD. A balanced dataset of 2,940 facial images (1,470 autistic and 1,470 non-autistic children) from Kaggle was pre-processed to 200x200 pixels and evaluated under three dataset-splitting scenarios (80:10:10, 70:15:15, and 60:20:20) to assess generalisability. The model, trained with the Adam optimiser over 10 epochs, achieved optimal performance in the 80:10:10 scenario, with 84.67% precision, 84.35% recall, and 84.32% F1 score. Results demonstrate high confidence (>90% probability) in distinguishing autistic from non-autistic individuals on unseen data. The study underscores the potential of integrating deep learning into clinical decision-support systems for ASD detection, offering a robust, scalable, and efficient solution to improve diagnostic accuracy and reduce reliance on manual methods.
Co-Authors AA Sudharmawan, AA Abdi Wahab Afiyati, Afiyati Alfalah, Win Bambang Jokonowo Berlinton, Budi Dody, Dody Dwi Wulandari Sari Dwina Kuswardani, Dwina Efy Yosrita, Efy Emilia Emilia Hadi Santoso Hanifah, Nurul Afif Hendri Hendri Ifan Prihandi Indrianto Indrianto Karisma Riskinanti Kurnia Gusti Ayu Latumaerissa, Daniel Eliazar Lestari, Mulyati Muchlis, Esa Firmansyah Muhamad Fadli Muhammad Rifqi Nasril Sany Novrizal, Kevin Putra, Rizki Pratama Rahmat Budiarto Ridlan, Ahmad Rusjdi, Halim Saruni Dwiasnati Utami Wahyuningsih Wawan Gunawan Wawan Ridwan