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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Jurnal Komtekinfo Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Yuhandri, Y
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Convolutional Neural Network Method in Detecting Digital Image Based Physical Violence Elpina, Elpina Sari Dewi Hasibuan; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.657

Abstract

Physical violence in the educational environment has a serious impact on mental health, safety, and student achievement, in addition to causing physical injury, violence can cause psychological trauma that interferes with the learning process, due to the limited supervision system, lack of officers, and the absence of automatic detection technology. This research aims to design and develop an automatic detection system of physical violence using digital image processing technology. This study uses the Convolutional Neural Network (CNN) method with the stages of digital image collection and labeling, preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The CNN architecture was chosen because it is efficient and accurate, and it supports data augmentation to improve generalization. The dataset was taken from kaggle and primary data at the al-falah huraba Islamic boarding school which consisted of 2000 images which included: 800 images of violence on CCTV of the dormitory room, 500 images of violence simulation of training videos and 500 non-violent images. The results showed that the developed CNN model was able to detect physical violence with an accuracy of above 88%, making it feasible to apply in surveillance camera-based school surveillance systems (CCTV). The system is able to classify images in real-time into two categories: safe and hard. This research contributes to the use of artificial intelligence to support efficient and affordable technology-based education security.
Combination of Support Vector Machine and Artificial Neural Network Methods in Negative Content Filtering System Wira, M Wira Sanjaya; Yuhandri, Y; Hendrik, Billy
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.660

Abstract

Local Wi-Fi network access has become a common necessity in everyday digital activities, but it is vulnerable to misuse to access negative content. This content includes pornographic material, hate speech, and violent content that can adversely affect users, especially in educational settings. For this reason, a system that is able to filter malicious content automatically and efficiently is needed. This research aims to design an artificial intelligence-based negative content filtering system that can be run on local network devices. The methods used include image classification using Convolutional Neural Network (CNN) and Artificial Neural Network (ANN), as well as text classification with DistilBERT and Support Vector Machine (SVM). To maintain user privacy, the model is trained using a federated learning approach that allows for decentralized learning. Knowledge distillation is also applied to produce lightweight models that can be run on edge devices such as routers. The datasets used include NSFW Image Dataset, OpenPornSet, as well as a collection of toxic comments from Reddit and Twitter. The evaluation was carried out in a simulation of a local network with 50 active devices. The test results showed an ANN accuracy rate of 93.4% in recognizing visual content, and SVM accuracy of 91.7% in detecting text-based hate speech. This research can be a reference in the application of AI-based content filtering systems for safe and responsible digital access protection
Co-Authors Alfallah, Fadhly Borianto, B Elpina, Elpina Sari Dewi Hasibuan Gunadi Widi Nurcahyo Hendrik, Billy Khairani, Maisan Dewi Puspa Nissa, Ika Ima Olivia, Ladyka Febby Prihandoko, P Rifky, Muhammad Sonang, Sahat Sovia, Rini Sumijan, S Syafri Arlis Tajuddin, Muhammad Tamin, Zulfiqar Wira, M Wira Sanjaya Wirahmadayanti, Isna Yuda Irawan Zufari, Faisal