Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal Journal of Information Systems and Technology Research International Journal of Educational Narratives
Rachmat Rasyid
Universitas Pejuang Republik Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Deep Learning-Based Sentiment and Emotion Analysis of Social Media Data to Identify Factors Affecting Healthy Food Choices in Urban Communities Rachmat Rasyid; Muh Rafli R; Faisal Faisal; Suherwin Suherwin; Siti Nur Asia; Amir Karimi
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1288

Abstract

The increasing influence of social media on public perception has made it a powerful driver of dietary behavior in urban communities. Nevertheless, the abundance of unverified health information often obscures individuals’ ability to make informed food choices. This study proposes a deep learning-based framework to analyze sentiment and emotion from social media discourse in order to uncover the key factors affecting healthy food decisions in urban settings. By applying Natural Language Processing (NLP) techniques and advanced deep learning models to a large corpus of user-generated content, the research identifies significant patterns linking emotional expression with food-related decision-making. The results indicate that positive emotions, such as pride and satisfaction, are strongly associated with healthy food promotion, while negative emotions, including frustration, are predominantly tied to affordability, accessibility, and convenience issues. Among these, price and food quality emerge as the most critical determinants shaping consumer preferences. These findings underscore the importance of integrating emotional and socio-economic considerations into public health strategies. Beyond offering empirical insights, this study demonstrates the scalability and effectiveness of deep learning in extracting nuanced perspectives from unstructured social media data, thereby contributing a robust methodological approach for real-time public health monitoring and intervention design.  
Real-Time IoT Integration for Coal Production And Distribution Management Hendra Sani; Rachmat Rasyid; Siti Nur Asia; Syamsuddin Syamsuddin; Suherwin Suherwin; Răzvan Șerban
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1295

Abstract

The coal production and distribution industry faces persistent challenges in data management, operational coordination, and decision-making efficiency. Conventional monitoring methods often result in delayed reporting, low data accuracy, and limited adaptability to dynamic market demands. This study addresses the lack of an intelligent and integrated information system by designing and developing a real-time IoT-based solution for coal production and distribution management. The system was built using the Software Development Life Cycle (SDLC) with the Waterfall model and integrates IoT sensors to automatically capture critical parameters such as pressure, temperature, and coal quality indicators. Artificial Intelligence (AI) components were incorporated to enhance data analysis and support predictive decision-making. System evaluation through simulation with dummy data demonstrated notable improvements, including a 40% reduction in reporting response time and a 95% increase in operational data accuracy. The system also enabled faster production monitoring, streamlined distribution processes, and provided decision-makers with reliable real-time insights. User feedback confirmed the system’s effectiveness in improving accessibility, monitoring efficiency, and overall operational performance in coal production and distribution management.
Digital Narratives And Machine Learning For Personalized Learning Recommendations In Transnational Educational Contexts Rachmat Rasyid; Syamsul Bhahri; Hamria Hamria
International Journal of Educational Narratives Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/ijen.v4i1.3476

Abstract

Background. Personalized learning systems are expanding rapidly in higher education, but many recommendation pipelines still depend on structured indicators such as grades, attendance, task completion, and clickstream activity. In transnational educational settings, those indicators cannot adequately explain how students interpret disciplinary language, negotiate cultural expectations, or express learning difficulties. Purpose. This study develops a narrative-informed framework for personalized learning recommendations by integrating structured academic data and student-authored digital narratives within a multimodal learning analytics perspective. Method. The manuscript is positioned as a design science and framework-development study rather than a completed quantitative experiment. It synthesizes recent literature on learning analytics, educational recommender systems, multilingual education, natural language processing, and human-centred AI in education to specify a technical workflow for narrative preprocessing, multimodal fusion, learner-state modelling, recommendation generation, and evaluation. The small learner records presented in tables are synthetic examples used only to illustrate the data architecture. Results. The main output is a technically explicit and theoretically grounded framework that explains how narrative text can be anonymized, segmented, normalized, encoded into multilingual embeddings, combined with numerical learner indicators through early and late fusion, and evaluated using both predictive and recommender metrics. The framework also operationalizes transnational variables, including language diversity, culturally indirect participation, and contextual adaptation needs. Conclusion. Digital narratives can enrich learner profiling, improve contextual sensitivity, and strengthen culturally responsive recommendations. The study contributes a coherent blueprint for future empirical implementation in multilingual and transnational learning environments
Co-Authors Amir Karimi Faisal Faisal Hamria Hamria Hendra Sani Muh Rafli R Răzvan Șerban Siti Nur Asia Suherwin Suherwin Syamsuddin Syamsuddin Syamsul Bhahri