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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 97 Documents
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Search results for , issue "Vol. 4 No. 2 (2025): February 2025" : 97 Documents clear
Design Of A Mobile-Based Information System For Service Ordering At Teguh Design Printing And Screen Printing Services Mahfudz, Muhammad Syah Farros; Mutiara Handayani Ujianti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.698

Abstract

This study aims to design a mobile-based information system for service ordering at Teguh Design, a printing and screen-printing business. The primary issue identified is the absence of an effective information system, as current ordering processes rely on manual methods, leading to potential data loss and record-keeping errors. Data collection methods included direct observation of the existing system, interviews with stakeholders, and a literature review to support analysis. The findings reveal that the current ordering system is inefficient and prone to errors, impacting customer satisfaction and company performance. Consequently, the implementation of a structured, mobile-based information system is essential to enhance operational efficiency and service quality. With the new system, orders can be placed online, allowing customers to make reservations more easily and quickly. Additionally, the system will improve data management, ensuring greater accuracy and security. Recommendations include employee training, regular monitoring and evaluation, and collaboration with relevant stakeholders to optimize system development. These steps are expected to enhance Teguh Design's competitiveness in the market.
Improving Resnet Model In Safety Gear Classification Using Finest Optimizer Robet; Johanes Terang Kita Perangin Angin; Edi Wijaya
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.703

Abstract

The Occupational accidents that occur in the work environment are increasing day by day. This is caused by workers' non-compliance with the established work safety equipment. Although the supervision of the use of work safety equipment has been carried out, it is still done manually involving less effective human resources. Therefore, it is necessary to develop an intelligent model that can classify the use of work safety equipment more accurately. This study uses the pre-trained ResNet50 model and is combined with the best optimization model to improve accuracy. The results of the study showed that the RMSProp optimization model has better performance with an accuracy value of 97.01% in the 17th epoch of 50 epochs of data training and with training loss and validation loss values ​​of 0.3268 and 0.145, respectively. Testing of 20 images with each image, 10 images using safety equipment, and 10 images not using safety equipment can be classified correctly.
Prediction of the Population of Kapuas Hulu District Based on Gender Using the Backpropagation Method Siregar, Alda Cendikia; Sucipto; Ilham Gunawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.709

Abstract

Rediction is a branch of science used to estimate future events based on historical data. One of the effective methods currently developing is the Backpropagation Artificial Neural Network. This study aims to determine prediction results, the developed model, and its accuracy in forecasting the population of Kapuas Hulu district by gender using the Backpropagation method. The resulting model has an architecture of 2-5-2, with 2 neurons in the input layer, 5 in the hidden layer, and 2 in the output layer. The model uses a learning rate of 0.8, an error tolerance of 0.00001, and 8000 epochs. Predictions for one year after the last dataset year (2024) estimated 138,756 males and 131,434 females, achieving an accuracy of 99.38%. Model validation using the k-fold cross-validation method with 4-folds showed the best accuracy of 99.38% in the first fold. This indicates that the Backpropagation model is highly reliable and effective for predicting population data based on gender.
Developing of A Learning Content Recommendation System Using Collaborative Filtering Based on User Rating Piantari, Erna; Muhammad, Fadjrin Diraja; Prabawa, Harsa Wara
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.710

Abstract

The advancement of artificial technology has paved the way for personalized learning experiences through adaptive systems which could be built by developing a recommendation system. In education filed, a variety of learning material recommendation systems that employ user filtering algorithms has prompted a lot attention as well. These systems aim to enhance the learning journey by offering tailored learning content suggestions based on individual preferences. This research explores the design of recommendation of learning content system, focusing on user filtering algorithms to analyze user preferences. By leveraging techniques such as collaborative filtering and user-based filtering, the system can accurately predict and recommend relevant learning materials to users based on others rating. The system continuously refines itself in an effort to increase user satisfaction and recommendation accuracy, which will eventually contribute to more efficient and engaging learning experiences.
Design of a Web-Based Ordering Information System for the Legendary Ketan Restaurant in Tegal City Maulana, Ikbal; Mutiara Handayani Ujianti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.715

Abstract

The owner of Ketan Legendaris Restaurant in Tegal City also operates two other businesses, Mie Ayam Jhaya Baroe and Cafe Kopi PDKT, all of which are located within the same area. This setup often leads to challenges during peak times, such as unorganized order queues, accumulated orders, and delayed deliveries. Additionally, the restaurant's ordering system remains manual, relying on conventional record-keeping methods, which results in inefficient service and prolonged waiting times for customers. This research aims to design a web-based ordering information system to enhance the efficiency and effectiveness of the ordering process at Ketan Legendaris Restaurant. Data collection methods include direct observation, interviews, and library research. The analysis phase is conducted through several stages: surveying the existing system, analyzing survey findings, identifying information needs, and determining system requirements. The system design adopts the Waterfall model, utilizing UML diagrams as a tool for system modeling. MySQL is employed as the database, while the CodeIgniter framework is used to develop the web-based system using PHP. The research outputs a prototype of a web-based ordering information system designed to address the existing issues and improve the restaurant's operational performance. This system is expected to streamline the ordering process, making it faster, more organized, and accurate, thereby enhancing customer satisfaction and supporting the overall efficiency of the restaurant operations.
Design of a Web-Based Inventory Management System For Toko Fajar Mandiri Tani In Karangmulya, Suradadi Maulidi, Muhammad Akwan; Aries Setyani Wahyu Prasetyawati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.716

Abstract

This study addresses the problem of mismatched stock records and actual inventory levels in the warehouse of Toko Fajar Mandiri Tani, which often disrupts operations and customer service. To solve this, the study used data collection methods such as direct observations, interviews with staff, and literature review to build a strong foundation for the system design. The analysis of the current manual system identified its strengths and weaknesses, which became the basis for developing a new solution. The result is a web-based inventory management system that helps record, monitor, and report stock levels in real-time. The system implementation showed significant benefits, including improved accuracy in inventory data, reduced risks of overstocking or understocking, and better stock order recommendations. Additionally, the automated inventory reporting feature supports faster and more informed decision-making. This system not only records stock but also streamlines the overall operational management of the store. The findings of this study offer a practical technological solution to help small and medium-sized businesses manage their inventory effectively.
Design of a Website-Based Field Rental Information System for Rajawali Futsal Bachtiar, Galuh; Riyanto
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.717

Abstract

This study aims to design and develop a field rental information system for Rajawali Futsal, which has been relying on manual methods for administration and data recording. The manual method often leads to data management errors and inefficiencies in customer service. One of the main issues identified is the inconvenience for customers who need to visit the location in person to make a reservation, which causes time constraints and discomfort. Additionally, the manual handling of financial reports makes it difficult for the management to obtain accurate and timely reports. To address these issues, this study adopts the Waterfall system design methodology, which includes the stages of requirements analysis, system design, implementation, and testing. The system designed in this research aims to improve the efficiency and accuracy of the field rental process and data management. With the website-based system, customers can make field reservations online, choose available times and fields, and make payments without having to visit the location. Additionally, the system includes a financial reporting feature that allows the management to monitor and evaluate transactions in real-time and in a more structured manner. The results of this study show that the implementation of a website-based information system can improve the ease of field rental processes, reduce data recording errors, and speed up the management of financial data. With this integrated system, it is expected that customer satisfaction will increase and the operational management of Rajawali Futsal will become more efficient and effective.
Comparative Analysis of Model Architectures Using Transfer Learning Approach in Convolutional Neural Networks for Traditional Ulos Fabric Classification Abdullah, Taufik; Saputra S, Kana; Syahputra, Hermawan; Indra, Zulfahmi; Kartika, Dinda
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.719

Abstract

Ulos cloth is a traditional woven fabric of the Batak tribe in North Sumatra, valued for its aesthetic and symbolic significance in various ceremonies. The diversity of ulos motifs presents challenges in preservation due to their unique patterns and functions. This study aims to develop an accurate method for classifying ulos motifs using Transfer Learning on Convolutional Neural Network (CNN) architectures. Five popular models—VGG16, VGG19, MobileNetV3, Inception-V3, and EfficientNetV2—were evaluated on a dataset of 962 ulos images across six motif categories.The results show that Inception-V3 outperformed other models with an average validation accuracy of 98.13% and the lowest loss of 5.67%. Inception-V3 also demonstrated superior generalization, achieving the highest K-fold validation accuracy, while VGG16 and VGG19 exhibited overfitting at higher learning rates. Two-way ANOVA analysis confirmed significant performance differences among the models and highlighted the interaction between model type and training methods. This research recommends Inception-V3 as the optimal model for ulos motif classification, offering an efficient and reliable tool to support cultural preservation through advanced image recognition technology.
Track Record Model in Employee Performance Optimization Using Weight Product Method Safrizal; Lili Tanti; Surbakti, Dio Febrian; Nurainun
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.721

Abstract

Employee performance improvement is a crucial aspect for the growth and success of a company, especially in the agricultural sector that relies on the quality and competence of human resources. However, subjective and manual employee assessments often face challenges, such as high levels of subjectivity and the time required to complete the process. To overcome these obstacles, this study proposes the use of the Weighted Product (WP) method as an approach to building a track record model in employee performance assessment. This study involves several methodological stages, first by studying the literature related to decision support systems, WP methods, track records, and employee performance assessments. Furthermore, data collection is carried out from a dataset that includes monthly assessments of employee performance based on several criteria such as attendance, cooperation, work quantity, responsibility, and others. The next process involves modeling, where the WP model is designed to produce the maximum total value of the existing assessment criteria. Model validation is carried out through two approaches, namely the Criterion-related Validity Test and the Internal Consistency Test. The test results show that the WP model has a Criterion-related Validity of 0.9851, indicating a strong relationship between the employee scores generated and the assessments given by the supervisor. In addition, Cronbach's alpha reached a value of 1.0, indicating excellent internal reliability of the model. Thus, the use of the WP method in the employee performance tracking system can be considered effective and can improve objectivity and efficiency in employee performance assessment in the context of agricultural companies. This method not only helps in identifying high-performing employees, but also in motivating them to achieve the highest performance standards, which in turn can improve the overall operational quality and reputation of the company
Operational Data Analysis and Visualization of PT XYZ Using Business Intelligence Approach with Microsoft Power BI M. Frizky Feri Setiawan; Yekti Condro Winursito
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.722

Abstract

The study aims to analyze and visualize operational data at PT XYZ, a furniture manufacturing company, utilizing Business Intelligence methods with Microsoft Power BI. A systematic approach was employed, encompassing data import, transformation, cleaning, and visualization, to develop an interactive dashboard that enhances decision-making. Key findings indicate that certain production sections consistently met or exceeded their targets, while others revealed opportunities for improvement. Insights into service wage distribution, standard time requirements, and target realizations were derived from the dashboard. The research identified sections with high service wages and highlighted areas with elevated standard times, suggesting a need for efficiency enhancements. Recommendations include focusing on underperforming sections and optimizing operations to reduce service wages. The study concludes that the developed dashboard supports data-driven decision-making, ultimately contributing to improved operational performance within the companys.

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