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All Journal Jurnal E-Komtek Journal of Information Systems and Technology Research
Rasyid, Rachmat
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementation of Machine Learning Using the Convolution Neural Network Method for Aglaonema Interest Classification Rasyid, Rachmat; Abdul Ibrahim
Jurnal E-Komtek (Elektro-Komputer-Teknik) Vol 5 No 1 (2021)
Publisher : Politeknik Dharma Patria Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v5i1.434

Abstract

One of the wealth of the Indonesian nation is the many types of ornamental plants. Ornamental plants, for example, the Aglaonema flower, which is much favored by hobbyists of ornamental plants, from homemakers, is a problem to distinguish between types of aglaonema ornamental plants with other ornamental plants. So the authors try to research with the latest technology using a deep learning convolutional neural network method. It is for calcifying aglaonema interest. This research is based on having fascinating leaves and colors. With the study results using the CNN method, the products of aglaonema flowers of Adelia, Legacy, Widuri, RedKochin, Tiara with moderate accuracy value are 56%. In contrast, the aglaonema type Sumatra, RedRuby, has the most accuracy a high of 61%.
Deep Learning-Based Sentiment and Emotion Analysis of Social Media Data to Identify Factors Affecting Healthy Food Choices in Urban Communities Rasyid, Rachmat; Rafli R, Muh; Faisal, Faisal; Suherwin, Suherwin; Asia, Siti Nur; Karimi, Amir
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 Sani , Hendra; Rasyid, Rachmat; Asia, Siti Nur; Syamsuddin, Syamsuddin; Suherwin, Suherwin; Șerban, Răzvan
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.
Co-Authors Abdul Ibrahim Asia, Siti Nur Faisal Faisal Karimi, Amir Rafli R, Muh Sani , Hendra Serban, Razvan Suherwin, Suherwin Syamsuddin Syamsuddin