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Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Published by Yayasan Kita Menulis
ISSN : -     EISSN : 28084519     DOI : https://doi.org/10.53842/jaiea.v1i1
Core Subject : Science, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering
The Journal of Artificial Intelligence and Engineering Applications (JAIEA) is a peer-reviewed journal. The JAIEA welcomes papers on broad aspects of Artificial Intelligence and Engineering which is an always hot topic to study, but not limited to, cognition and AI applications, engineering applications, mechatronic engineering, medical engineering, chemical engineering, civil engineering, industrial engineering, energy engineering, manufacturing engineering, mechanical engineering, applied sciences, AI and Human Sciences, AI and education, AI and robotics, automated reasoning and inference, case-based reasoning, computer vision, constraint processing, heuristic search, machine learning, multi-agent systems, and natural language processing. Publications in this journal produce reports that can solve problems based on intelligence, which can be proven to be more effective.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 524 Documents
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Analysis of Trends and Development of Low-Light Image Enhancement Methods in Computer Vision Ani Sanirah; Sri Rahayu; Ade Bastian
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2372

Abstract

This study investigates the development of Low-Light Image Enhancement (LLIE) methods in the field of computer vision using a Systematic Literature Review (SLR) approach. The review was conducted on 56 scientific articles selected from a total of 604 papers entirely sourced from the Scopus database based on the PRISMA 2020 guidelines. The results indicate that LLIE research has evolved from traditional methods, such as histogram equalization and Retinex, toward deep learning-based approaches including CNN, GAN, Transformer, and diffusion models. Modern methods have demonstrated superior performance in improving image illumination, preserving details, and reducing noise. In addition, real-world datasets and zero-reference approaches are increasingly adopted to improve model generalization capability. However, challenges remain regarding computational complexity, detail preservation, and model performance under extreme low-light conditions. This study concludes that future LLIE research will focus on developing models that are more adaptive, efficient, lightweight, and robust for various computer vision applications.
Design of a Web-Based Household Worship Scheduling Information System at GBI Wangga Intan G. Lika Yanggu; Pingky A. R. Leo Lede
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2332

Abstract

The rapid development of information technology has increased the need for effective information management in various organizations, including churches. At GBI Wangga, the scheduling of Household Fellowship Worship (PA) is still conducted manually through written records and verbal announcements, resulting in delays and uneven distribution of information among congregation members. This study aims to design and develop a web-based Household Fellowship Worship Scheduling Information System to facilitate schedule management and improve access to information. The system was developed using the Waterfall method, which includes requirements analysis, system design, implementation, and testing. The resulting system enables users to access worship schedules quickly and accurately through a web platform. It is expected to improve scheduling efficiency, reduce information delivery errors, and support better church services for the congregation.
Sentiment Analysis on the Failure of the Indonesian National Team to the 2026 World Cup During Patrick Kluivert's Coaching Period using the Support Vector Machine (SVM) Algorithm Ade Dharma; A M H Pardede; Muammar Khadapi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2353

Abstract

This study aims to analyze public sentiment regarding the failure of the Indonesian National Team to qualify for the 2026 FIFA World Cup during Patrick Kluivert’s coaching period using the Support Vector Machine (SVM) algorithm. Data were collected through web scraping from Twitter (X), YouTube, and Detik.com, resulting in 5,060 comments. The collected data were processed using Natural Language Processing (NLP), including case folding, cleaning, tokenization, stopword removal, normalization, and stemming. The labeled data were transformed using the Term Frequency–Inverse Document Frequency (TF-IDF) method and divided into training and testing sets with an 80:20 ratio. The classification model was developed using a linear kernel SVM and implemented through a Streamlit-based web application for interactive sentiment prediction. The results showed that negative sentiment dominated with 55.0%, followed by positive sentiment at 36.4% and neutral sentiment at 8.6%. Model evaluation achieved an accuracy of 78.44%, precision of 78.54%, recall of 78.44%, and f1-score of 78.48%. These findings indicate that the SVM method is effective in classifying public sentiment toward the performance of the Indonesian National Team.
Analysis and Simulation of a Queueing System in a Self-Service Seblak MSME using the FIFO Model Hayati; Dio Ananda
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2373

Abstract

Micro, Small, and Medium Enterprises (MSME) Seblak Buffet often experience long queues and inefficient waiting times, especially during peak hours, which negatively impact customer comfort and service quality. This study aims to analyze and analyze the business's queue system using the FIFO (First In First Out) model to improve service efficiency and fairness. Using a quantitative approach with M/M/1 modeling, on arrival times and following exponential distribution service, data were obtained from direct observation. The simulation results show that although FIFO maintains order, waiting times increase significantly during peak hours due to limited facilities. Therefore, this study recommends adding waiters or rearranging service flows during peak hours as an applicable solution to improve quality and customer satisfaction.

Page 53 of 53 | Total Record : 524

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