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All Journal Format : Jurnal Imiah Teknik Informatika Jurnal Khatulistiwa Informatika Jurnal Pilar Nusa Mandiri Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Mantik INTI Nusa Mandiri Abditeknika - Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Informatika Teknologi dan Sains (Jinteks) Paradigma Jurnal Aruna Mengabdi (ARMI)
Imam Nawawi
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Journal : Jurnal Pilar Nusa Mandiri

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Physical Violence Detection System to Prevent Student Mental Health Disorders Based on Deep Learning Sukmawati Anggraeni Putri; Achmad Rifai; Imam Nawawi
Jurnal Pilar Nusa Mandiri Vol 19 No 2 (2023): Publishing Period for September 2023
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v19i2.4600

Abstract

Physical violence in the educational environment by students often occurs and leads to criminal acts. Apart from that, repeated acts of physical violence can be considered non-verbal bullying. This bullying can hurt the victim, causing physical disorders, mental health, impaired social relationships and decreased academic performance. However, monitoring activities against acts of violence currently being carried out have weaknesses, namely weak supervision by the school. A deep Learning-based physical violence detection system, namely LSTM Network, is the solution to this problem. In this research, we develop a Convolutional Neural Network to detect acts of violence. Convolutional Neural Network extracts features at the frame level from videos. At the frame level, the feature uses long short-term memory in the convolutional gate. Convolutional Neural Networks and convolutional short-term memory can capture local spatio-temporal features, enabling local video motion analysis. The performance of the proposed feature extraction pipeline is evaluated on standard benchmark datasets in terms of recognition accuracy. A comparison of the results obtained with state-of-the-art techniques reveals the promising capabilities of the proposed method for recognising violent videos. The model that has been trained and tested will be integrated into a violence detection system, which can provide ease and speed in detecting acts of violence that occur in the school environment.
Co-Authors Abdillah Ari Abdul Hamid Achmad Rifai Ahmad Fauzi Ahmad Hafidzul Kahfi Akrom, Akrom Anak Agung Gede Sugianthara Andi Taufik Andi Taufik Andreyestha Andreyestha Andreyestha Eko Pramono Fajar Akbar Fajar Akbar Ganda Wijaya Ginabila Ginabila Hilda Rachmi Jamhari Jamhari Mochammad Abdul Azis Numan Musyaffa Nurajijah Nurajijah Oky Kurniawan Richky Faizal Amir Ricki Sastra Ridwansyah Ridwansyah Rino Ramadan Risa Wati - AMIK BSI Tasikmalaya Risa Wati - AMIK BSI Tasikmalaya Rizal Prasetyo Silvy Nur Azizah Siti Hani Nurlaela Sukmawati Anggraeni Putri Toto Haryanto Verry Riyanto Veti Apriana Widi Intan Priyanti