Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Kursor International Journal of New Media Technology
Muhamad Syukron
Institut Teknologi Sepuluh Nopember
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A Hierarchical Two Stage BERT Model for Cyberbullying Detection Muhamad Syukron; Nayya Safitri Ramadani; Muhammad Ilham Firmansyah; Asi Emilia Putri
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4730

Abstract

Cyberbullying has emerged as a persistent social issue in tandem with the rapid proliferation of social media usage, particularly in developing nations such as Indonesia. Online bullying extends beyond explicit insults to encompass more nuanced forms, including denigration and social exclusion, which are often subtle and context-dependent. This diversity presents significant challenges for automated detection systems, especially when cyberbullying is treated as a single, homogeneous category. To address this challenge, this study proposes a two-stage classification framework for detecting cyberbullying in the Indonesian language using the IndoBERT model. In the first stage, a binary classification model is employed to differentiate between bullying and non-bullying content. In the second stage, cyberbullying tweets are further categorized into three specific types: harassment, denigration, and exclusion. The model is trained using a curated dataset of Indonesian tweets collected from publicly available social media content. The results indicate that the proposed framework achieves strong and consistent performance, particularly in fine-grained cyberbullying classification, with an overall accuracy of approximately 92% in the second stage. These findings suggest that hierarchical classification can yield robust performance and effectively aid in the detection of cyberbullying.
Deep learning algorithms for autism spectrum disorder detection using eye-tracking patterns Muhamad Syukron; Rangga Alif Faresta
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.468

Abstract

Early diagnosis of autism spectrum disorder (ASD) plays a crucial role in facilitating prompt interventions and assessing post-therapy progress, both of which can greatly improve developmental trajectories. The emergence of artificial intelligence—particularly deep learning—has opened new possibilities for clinicians to detect ASD with improved precision and speed. This study investigates the utilization of deep learning models to recognize ASD among 13-year-old children based on eye movement data collected as participants observed static images and short video sequences. The dataset included visual and numerical variables, such as gaze position and pupil diameter, allowing for a multimodal analytical approach. For the numerical dataset, a multilayer perceptron (MLP) neural network produced the best outcomes, yielding an accuracy of 91.7% and a recall rate of 83.3% in ASD classification. Meanwhile, the Vision Transformer (ViT) model performed best for image-based analysis, reaching an accuracy of 78.2% and a recall rate of 88.6%. Overall, the findings highlight the potential of deep learning frameworks as objective, data-driven tools for ASD detection in both clinical and research contexts.Key words: Autism, Computer Vision, Deep Learning Model, Vision Transformer.
Co-Authors Asi Emilia Putri Muhammad Ilham Firmansyah Nayya Safitri Ramadani Rangga Alif Faresta