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All Journal Journal International of Lingua and Technology International Journal of Educatio Elementaria and Psychologia
Muhammadiyah, Mas’ud
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Education Languange, Linguistic, Communication & Media Other

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Multimodal Sentiment Analysis in Indonesian: A Comparative Study of Deep Learning Models for Hate Speech Detection on Social Media Muhammadiyah, Mas’ud; Xiang, Yang; Na, Li; Nishida, Daiki; Prayudani, Santi
Journal International of Lingua and Technology Vol. 4 No. 1 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/jiltech.v4i1.824

Abstract

With the rapid expansion of social media, the prevalence of hate speech has become a critical issue, particularly in the context of Indonesian language and culture. The detection of hate speech in social media platforms is a complex task due to the multimodal nature of online communication, where text, images, and videos are often combined to express sentiments. This study aims to explore and compare deep learning models for multimodal sentiment analysis, focusing on their effectiveness in detecting hate speech in Indonesian social media content. By analyzing both textual and visual data, the study seeks to enhance the accuracy of sentiment classification, specifically identifying instances of hate speech. The research employs several state-of-the-art deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-based models, to perform sentiment analysis on a multimodal dataset. The dataset includes text and images from Indonesian social media posts, labeled for hate speech detection. The results show that multimodal models outperform text-only models, with the Transformer-based model yielding the highest accuracy and F1-score in detecting hate speech. The inclusion of visual data significantly improved the model’s ability to classify complex and subtle expressions of hate speech. This study concludes that multimodal deep learning models offer a promising solution for detecting hate speech in Indonesian social media, with implications for better content moderation and online safety.
The Effectiveness of Using Educational Games in Indonesian Language Learning in Elementary Schools Muhammadiyah, Mas’ud; Farrudin, Ahmad; Fernandez, Fransisca Debby Christine; Chai, Napat; Hamsiah, Andi
International Journal of Educatio Elementaria and Psychologia Vol. 2 No. 1 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijeep.v2i1.1885

Abstract

Language learning in elementary schools plays a crucial role in developing students’ literacy, communication, and critical thinking skills. However, traditional teaching methods often fail to engage students effectively, leading to decreased motivation and suboptimal learning outcomes. This study investigates the effectiveness of using educational games as an innovative approach to enhance Indonesian language learning among elementary school students. A quasi-experimental research design with a pre-test and post-test control group was employed. The study involved 60 students, divided into an experimental group that used educational games and a control group that followed conventional learning methods. Data were collected using language proficiency tests, student motivation questionnaires, and teacher observations. These results indicate that educational games are effective in creating an interactive and enjoyable learning environment. The study concludes that incorporating educational games into language instruction can enhance learning outcomes and student motivation, providing valuable insights for teachers and curriculum developers.  
Co-Authors Chai, Napat Farrudin, Ahmad Fernandez, Fransisca Debby Christine Hamsiah, Andi Na, Li Nishida, Daiki Prayudani, Santi Xiang, Yang