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All Journal Journal of Information Systems and Technology Research Journal of Tecnologia Quantica
Suherwin Suherwin
Universitas Pejuang Republik Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Physics

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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.
The Effectiveness of Interactive Learning Media Based on Augmented Reality in Enhancing Elementary School Students’ Learning Motivation Faisal Faisal; Rachmat Rachmat; Siti Nur Asia; Suherwin Suherwin; Abdul Ibrahim
Journal of Tecnologia Quantica Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i3.2443

Abstract

This study aims to analyze the effectiveness of using augmented reality (AR) based learning media in enhancing the learning motivation of elementary school students. By employing a multiple linear regression approach on simulated data, this research evaluates the influence of several key factors, namely AR visualization quality, teacher support, ease of use, and supporting infrastructure. The analysis results show that AR visualization quality, teacher support, and ease of use significantly affect the improvement of learning motivation. The developed model has a coefficient of determination (R² ? 0.77), indicating that 77% of the variation in learning motivation can be explained by the independent variables, with a relatively small prediction error (RMSE ? 0.53). The F-test also confirmed that the model is overall significant. These findings indicate that the integration of AR in learning not only increases visual appeal but also strengthens the role of teachers and enhances students’ ease of interaction with the material. Nevertheless, this study is still based on simulated data, so further research with broader and more realistic empirical data is required to validate the results.
Co-Authors Abdul Ibrahim Amir Karimi Faisal Faisal Hendra Sani Muh Rafli R Rachmat Rachmat Rachmat Rasyid Răzvan Șerban Siti Nur Asia Syamsuddin Syamsuddin