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Pulung Nurtantio Andono
Universitas Dian Nuswantoro
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : Journal of Applied Data Sciences

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HU Variance Moment Optimizes Keyframe Selection Based on Deep Learning for Violence Detection Putri, Sukmawati Anggraeni; Andono, Pulung Nurtantio; Purwanto, Purwanto; Soeleman, Moch Arief
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.648

Abstract

Violence in public spaces poses a serious threat to individuals and society. Manual monitoring and violence detection require much time and human resources, ultimately hindering detection accuracy and speed. Therefore, an automated method is needed to detect violence to ensure fast and efficient action. Along with technological advances, violence detection research has adopted various methods and models, including deep learning, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In this study, the classification process for detecting violence and non-violence uses the VGG19 model, one of the CNN models that has good performance with limited computing. In addition, the Long Short-Term Memory (LSTM) model is the best RNN model for processing temporal data in videos. However, this performance will decrease with noise and irrelevant data in the classification process. Therefore, to optimize deep learning performance, this study in the pre-processing phase selects keyframes in frame extraction using the Hu Variance Moment Technique. This method calculates each frame’s Hu and Variance Moment values and selects keyframes based on high Hu values. Next, we use Adaptive Moment Estimation (Adam) to optimize the gradient of the selected keyframes. This study produces a Hu19LSTM model tested on three datasets: hockey fight, crowd, and AIRTLab. The proposed Hu19LSTM model produces an accuracy of 97% on the Hockey Fight dataset, 97% on the Crowd dataset, and 95% on the AIRTLab dataset. These results indicate that the Hu19LSTM model can increase its accuracy on the hockey fight and Crowd dataset by 97%.
Co-Authors Abdussalam Abdussalam, Abdussalam Achmad Ridwan Affandy Agus Winarno, Agus Al zami, Farrikh Al-Fatih, Gilang Fajar Alzami, Farrikh Aria Hendrawan, Aria Arry Maulana Syarif, Arry Maulana Asih Rohmani Asih Rohmani, Asih Bastiaans, Jessica Carmelita Budi Harjo Cahaya Jatmoko Candhy Fadhila Arsyad Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Chaerul Umam Christy Atika Sari D, Ishak Bintang Dalimarta, Fahmy Ferdian Danang Bagus Chandra Prasetiyo Darmawan, Aditya Aqil Denny Senata Dito, Aliffia Putri Doheir, Mohamed Dwi Eko Waluyo Dwi Puji Prabowo, Dwi Puji Dwiza Riana Edi Noersasongko Edi Noersasongko Edi Noersasongko Egia Rosi Subhiyakto, Egia Rosi Ekaprana Wijaya Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erna Zuni Astuti Fajrian Nur Adnan Fauzi Adi Rafrastara Firman Wahyudi, Firman Fitri Yakub Guruh Fajar Shidik Hamir, Mun Hanny Haryanto Hartojo, James Harun Al Azies Heru Lestiawan Hidayat, Sholeh Hisyam Syarif Husain Husain I Ketut Eddy Purnama Ibnu Utomo Wahyu Mulyono, Ibnu Utomo Irwan, Rhedy Islam, Hussain Md Mehedul Ivan Maulana Jumanto Jumanto, Jumanto Junta Zeniarja Karis Widyatmoko Khafiizh Hastuti Kiat, Ng Poh Kunio Kondo L. Budi Handoko M Arief Soeleman M. Arief Soeleman M. Arif Soeleman Maria Goretti Catur Yuantari Megantara, Rama Aria Mila Sartika, Mila Minghat, Asnul Dahar Bin Moch Arief Soeleman Moch Arief Soeleman Moch Arief Soeleman, Moch Arief Mochamad Hariadi Mochammad Arief Soeleman Muhammad Munsarif Muhammad Naufal, Muhammad Muljono Muljono Nanna Suryana Herman Ningrum, Novita Kurnia Nita Merlina Noor Ageng Setiyanto, Noor Ageng Nur Azise Ocky Saputra, Filmada Panca Hutama Caniago Paramita, Cinantya Pergiwati, Dewi Pramitasari, Ratih Prasetyoningrum, Devi Puji Purwatiningsih, Aris Pujiono Pujiono Purwanto Purwanto Putra, Angga Permana Raden Arief Nugroho Rafsanjani, Muhammad Ivan Rahmatullah, Muhammad Rifqi Fadhlan Ramadhan Rakhmat Sani ramayanti, ismarita Ricardus Anggi P Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Ruri Suko Basuki Saputra, Filmada Ocky Saputri, Pungky Nabella Saputro, Wicaksono Agung Saraswati, Galuh Wilujeng Sari Ayu Wulandari Sarker, Md. Kamruzzaman Satriyawibawa, Muhammad Yiko Savicevic, Anamarija Jurcev Senata, Denny Sendi Novianto Shafa, Raihanaldy Ash Shier Nee Saw Sinaga, Daurat Sindhu Rakasiwi Siti Hadiati Nugraini Soeleman, M Arief Soeleman, M. Arief Soeleman, Moch. Arief Soong, Lim Way Sri Winarno Sri Winarno Steven, Alvin Sudibyo, Usman Sukmawati Anggraeni Putri, Sukmawati Anggraeni Sukmono, Indriyo K. Supriyono Asfawi Susanto Susanto Tendi Tri Wiyanto, Tendi Tri Tengku Riza Zarzani N Thifaal, Nisrina Salwa Torhino, Rizal Wellia Shinta Sari Yaacob, Noorayisahbe Mohd Yusianto Rindra Zahrotul Umami, Zahrotul Zainal Arifin Hasibuan