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All Journal Al-Hijr: Journal of Adulearn World Journal International of Lingua and Technology
Na, Li
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Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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Management of AI-Based Education Data to Optimize the Learning Process Oci, Markus; Na, Li; Hui, Zhou
Al-Hijr: Journal of Adulearn World 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/alhijr.v4i1.850

Abstract

The rapid advancement of artificial intelligence (AI) technologies has led to their growing integration in educational systems, promising to revolutionize the learning process. AI has the potential to optimize learning by personalizing educational experiences, improving decision-making, and enhancing the management of educational data. However, despite these advancements, there is still a lack of systematic approaches to managing AI-based education data in a way that can consistently optimize the learning process. This study aims to explore how AI-based education data management can enhance the learning process by improving data-driven decision-making, student engagement, and performance tracking. A mixed-methods research design was used, combining qualitative case studies and quantitative data analysis. The study involved analyzing AI-driven data management tools used in several educational institutions to optimize learning outcomes. Surveys, interviews, and data analysis were used to evaluate the effectiveness of these tools in real-world educational settings. The results indicate that AI-based data management tools significantly enhance the learning process by providing real-time feedback, personalized learning paths, and better resource allocation. Educators and students reported increased engagement and improved learning outcomes due to the use of AI-powered tools. This study concludes that effective management of AI-based education data is essential for optimizing the learning process. Educational institutions should prioritize the integration of AI-driven data systems to maximize learning outcomes and efficiency.
Language and Identity in the Digital Age: Discourse Analysis of Online Communities Based on Regional Languages Evizariza, Evizariza; Na, Li; Wei, Sun
Journal International of Lingua and Technology Vol. 3 No. 3 (2024)
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.v3i3.813

Abstract

Humans are social creatures who cannot live alone. This requires a language that unites one individual with another, group with group, or individual with group. Language is an important component in a person's identity. Language is not only a means of communication, delivering messages, or even jokes. The influence of digital can create online communication. The purpose of this study is to find out how regional languages are used in online communities that affect the identity of their users. Through an analytical approach and literature study, researchers collected information from various journals or related articles. The results of the study show that regional languages can strengthen the identity of a region and its individuals. In addition, it also creates space for social solidarity and strengthening language communities. The results of this study in the form of a community of regional languages will revive regional languages that have faded. The existence of a new spirit in creativity in developing regional languages as local cultures will be able to reduce the rate of extinction of regional languages. Many regions have reused regional languages as everyday languages and are combined in learning. The conclusion of this study, a strong desire is needed to increase the protection of diverse cultures in Indonesia. 700 languages that must continue to be pursued in cultural defense. We need a young generation that is not embarrassed to use regional languages in everyday life.
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.
Co-Authors Evizariza Evizariza, Evizariza Hui, Zhou Muhammadiyah, Mas’ud Nishida, Daiki Prayudani, Santi Sun Wei Xiang, Yang