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All Journal Journal International of Lingua and Technology Journal International Dakwah and Communication
Nishida, Daiki
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Education Languange, Linguistic, Communication & Media Social Sciences Other

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Communication Patterns in Intercultural Marriages Involving Muslims Ilyas, Sanusi; Kobayashi, Riko; Nishida, Daiki
Journal International Dakwah and Communication Vol. 4 No. 2 (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/jidc.v4i2.766

Abstract

This study aims to explore communication patterns within intercultural marriages involving Muslim individuals. The increasing globalization and migration have led to a rise in intercultural marriages, often presenting unique challenges, particularly in communication. The study specifically focuses on the experiences of Muslim individuals in such marriages to understand the communication strategies they employ and the factors that influence these patterns. A qualitative approach, involving in-depth interviews with participants from various cultural backgrounds, was adopted to gather rich and detailed data. The findings reveal that effective communication in intercultural marriages involving Muslims is influenced by factors such as cultural differences, religious beliefs, and individual personalities. Participants employed various strategies, including accommodation, compromise, and intercultural mediation, to navigate these complexities. The study contributes to the existing literature on intercultural communication by highlighting the unique dynamics of Muslim intercultural marriages and providing insights for couples, counselors, and researchers.  
Gamification in Language Learning: Strategies and Impacts Rachman, Azhariah; Nishida, Daiki; Liliani Husain, Desy Liliani
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.v3i3.780

Abstract

The integration of gamification in language learning has gained significant attention due to its potential to enhance engagement, motivation, and learning outcomes. Despite its growing popularity, there is a need for a comprehensive analysis of effective strategies and their impacts on learners. This study addresses this gap by exploring how gamification can be strategically implemented in language education. This research aims to identify effective gamification strategies in language learning and evaluate their impacts on learner motivation, engagement, and proficiency. A mixed-methods approach was employed, combining a systematic literature review of 50 studies from Scopus-indexed journals (Q1 and Q2) with a quasi-experimental study involving 120 language learners. Data were collected through surveys, interviews, and pre-post proficiency tests. The findings reveal that gamification strategies such as point systems, leaderboards, and narrative-driven tasks significantly improve learner motivation and engagement. Additionally, learners exposed to gamified environments demonstrated a 20% higher proficiency gain compared to traditional methods. Gamification is a powerful tool in language learning, offering innovative ways to enhance educational outcomes. However, its success depends on the careful design and alignment of game elements with learning objectives.
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 Azhariah Rachman Desy Liliani Husain Ilyas, Sanusi Kobayashi, Riko Muhammadiyah, Mas’ud Na, Li Prayudani, Santi Xiang, Yang