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All Journal Journal of Information Systems and Technology Research Jurnal JEETech
Siti Nur Asia
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.
Rancang Bangun Tong Sampah Cerdas Menggunakan Suara Sebagai Media Informasi Berbasis Arduino Uno Siti Nur Asia; Sofyan Sofyan; Husna Saleh; Muhammad Ikhwan Mardin; M Noor Fuad
Jurnal JEETech Vol. 6 No. 1 (2025): Nomor 1 May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v6i1.6108

Abstract

Garbage is one of the problems that we often encounter in the community. One of the factors causing the accumulation of garbage is the lack of public awareness to dispose of garbage in its place. In this problem, it is necessary to have a smart trash can using sound information media which includes Ultrasonic Sensors as distance detectors and trash volume detectors, Servos are used to control the trash can cover, Speakers are used as information media, DFPlayer mini is used as a voice recorder and microcontrollers are used as input data processors from the components used. The trash can cover will open and close automatically by detecting humans at a distance of ≤ 30 cm then detecting the height of the trash from the trash can cover ≤ 10 cm then the trash can is full then the trash can cover will not open and emit information in the form of sound. The method used in this study is the experimental method, namely by designing, assembling, and testing a smart trash can system based on Arduino Uno. Testing was conducted to evaluate the performance of the sensor, and the voice module in responding to the presence of objects in front of the trash can. This research is useful for the community because it can increase awareness in disposing of garbage in its place through an interactive approach in the form of sound, thus supporting the creation of a cleaner and more orderly environment. In addition, scientifically, this research contributes to the development of science and technology, especially in the field of hardware that can be used as a basis for similar innovations in various fields of life.
Co-Authors Amir Karimi Faisal Faisal Hendra Sani Husna Saleh M Noor Fuad Muh Rafli R Muhammad Ikhwan Mardin Rachmat Rasyid Răzvan Șerban Sofyan Suherwin Suherwin Syamsuddin Syamsuddin