Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of System and Computer Engineering
Jemmy Edwin Bororing
Department of Informatics, Faculty of Engineering, Janabadra University, Yogyakarta, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Perbandingan DNN dan CNN dalam Klasifikasi Kata-Kata Kasar pada YouTube Shorts Evan Febditya Pratama; Ryan Ari Setyawan; Jemmy Edwin Bororing
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2759

Abstract

Abusive words in YouTube Shorts are difficult to moderate because harmful expressions may appear in captions, comments, or embedded text within visual frames. This study compares the performance of a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN) for abusive-word classification using text and image features from YouTube Shorts content. The dataset consisted of 1,013 image-text samples labeled into two binary classes: abusive and non-abusive. Textual features were derived from the text column representing OCR/caption information and transformed into 2,500-dimensional features, while image features were obtained from RGB images resized to 96 by 96 pixels. The dataset was divided into training, validation, and testing subsets consisting of 658, 202, and 153 samples, respectively. Both models were trained using dropout, L2 regularization, early stopping, and image augmentation. The results show that CNN outperformed DNN, achieving test accuracy of 0.8627 and macro F1-score of 0.8527, compared with DNN accuracy of 0.8301 and macro F1-score of 0.8195. These findings indicate that CNN provides more effective multimodal classification performance for detecting abusive words in YouTube Shorts content.
Co-Authors Evan Febditya Pratama Ryan Ari Setyawan