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All Journal ComEngApp : Computer Engineering and Applications Journal Jupiter Annual Research Seminar Sistemasi: Jurnal Sistem Informasi Dinamisia: Jurnal Pengabdian Kepada Masyarakat Journal of Information Technology and Computer Engineering Jurnal Generic Jurnal Mandiri IT
Rahmat Fadli Isnanto
Computer Systems Universitas Sriwijaya Palembang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences Other

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Journal : ComEngApp : Computer Engineering and Applications Journal

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Video Annomaly Classification Using Convolutional Neural Network Rachmatullah, Muhammad Naufal; Sutarno, Sutarno; Isnanto, Rahmat Fadli
Computer Engineering and Applications Journal Vol 13 No 1 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i1.468

Abstract

The use of surveillance videos is increasingly popular in city monitoring systems. Generally, the analysis process in surveillance videos still relies on conventional methods. This method requires professional personnel to constantly monitor and analyze videos to identify abnormal events. Consequently, the conventional approach is time-consuming, resource-intensive, and costly. Therefore, a system is needed to automatically detect video anomalies, reducing the massive human resource utilization for video monitoring. This research employs deep learning methods to classify anomalies in videos. The video anomaly detection process involves transforming the video into image format by extracting each frame present in the video. Subsequently, a Convolutional Neural Network (CNN) model is utilized to classify anomalous events within the video. Testing results using the CNN architectures DenseNet121 and EfficientNet V2 yielded performance accuracies of 99.89 and 98.24, respectively. The testing results indicate that the DenseNet121 architecture outperforms the EfficientNetV2 architecture in terms of performance.
Implementation of Feature Selection for Optimizing Voice Detection Based on Gender using Random Forest Abdurahman; Vindriani, Marsella; Prasetyo, Aditya Putra Perdana; Sukemi; Buchari, M. Ali; Sembiring, Sarmayanta; Firnando, Ricy; Isnanto, Rahmat Fadli; Exaudi, Kemahyanto; Dudifa, Aldi; Riyuda, Rafki Sahasika
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Gender-based voice detection is one of the machine learning applications that has various benefits in technology and services, such as virtual assistants, human-machine interaction systems, and voice data analysis. However, the use of too many features, including irrelevant features, can cause a decrease in accuracy and model performance. This research aims to optimize voice-based gender detection by applying a feature selection method to select significant features based on their correlation value to the target. Experimental results show that by using only the significant features selected through correlation analysis, the accuracy of the model is significantly improved compared to using all available features. This research confirms the importance of feature optimization to support the development of more efficient and accurate gender-based speech detection models.
Co-Authors A. Noviar Satria Mukti Abdurahman Adi Hermansyah, Adi Aditya Putra Perdana Prasetyo Ahmad Rifai Ariansyah Saputra Asvi, M. Fauzi Bayu Wijaya Putra Buchari, M. Ali Buchari, Muhammad Ali Dedy Kurniawan Dudifa, Aldi Fathoni - Haidar, Rosali Hanifah, Izzati Millah Huda Ubaya Huda Ubaya Kemahyanto Exaudi M. Rudi Sanjaya Marjusalinah, Anna Dwi Muhammad Naufal Rachmatullah Nabillah Syafitri Novi Yusliani Osvari Arsalan Perdani, Tharisa Antya Purwita Sari Purwita Sari, Purwita Ricy Firnando Riyuda, Rafki Sahasika Rizki Kurniati Rizki Ramadandi Rossi Passarella Sarmayanta Sembiring Sitorus, Harlis Richard Sukemi Sutarno Sutarno Syamsuryadi Syamsuryadi Vindriani, Marsella Willy