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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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Geographic Information System for Data Collection of Cooperatives and Small Medium Enterprises in East Sumba Sasqia Adinda Amin; Fajar Hariadi2s; Erwianta Gustial Radjah
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.v4i2.874

Abstract

Cooperatives and Small and Medium Enterprises (SMEs) are important pillars in the regional economy, with the Cooperatives and SMEs Office playing a crucial role in its development. East Sumba Regency has great potential, but the lack of comprehensive spatial data hinders the making of appropriate policies. This research aims to develop a Geographic Information System (GIS) for data collection of Cooperatives and SMEs in East Sumba Regency. The research method used is waterfall, with data collection through interviews, observations, and documentation. The results are in the form of maps and information on the distribution of Cooperatives and SMEs. It is hoped that this GIS can assist the Agency and Regional Governments in formulating policies that are right on target, as well as supporting training and funding. The positive impacts of GIS include improved data accuracy and quality, transparency, and accountability in the development of Cooperatives and SMEs. The System Usability Scale (SUS) method provides a positive picture of user satisfaction, with an average score of 90 indicating the "acceptable" category and an "excellent" rating. This score reflects the effectiveness and ease of use of the system. Overall, this geographic information system has succeeded in supporting the efficient data collection of Cooperatives and SMEs.
Use of Natural Language Processing in Social Media Text Analysis Badry Ali Mustofa; Wawan Laksito Yuly Saptomo
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.v4i2.875

Abstract

Social media generates enormous volumes of text data, creating both opportunities and challenges for analysis. Natural Language Processing (NLP) enables in-depth analysis of public opinion, identification of trends and language patterns from social media texts. However, texts from social media often face problems with informal language, slang, and spelling errors. This research discusses the application of NLP techniques, such as sentiment analysis, tokenization, and text classification, and compares classical machine learning models (Naive Bayes and SVM) with deep learning models (BERT). Results show deep learning-based models excel at understanding informal language contexts, producing more accurate analysis. This study makes an important contribution in the development of AI-based applications for social media analysis.
Improving the Voter List Clustering Model Fixed(DPT) using the K-Means Algorithm in Girinata Village Rizki Aldi; Nana Suarna2; Irfan Ali; Dendy Indriya Efendi
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.v4i2.876

Abstract

Elections are one of the pillars of democracy that require accurate voter data to ensure transparency and fairness. The Permanent Voter List (DPT) is a crucial element in supporting the smooth running of elections, but there are often data validity problems such as duplicate data, voter location errors, or voter data that does not meet the requirements. This research focuses on the application of the K-Means algorithm to increase the accuracy and validity of the DPT at TPS 05, Girinata Village. The problem formulation in this research includes the accuracy level of the DPT, the effectiveness of the K-Means algorithm in identifying inaccuracies, as well as factors that influence the accuracy of voter data. This research aims to analyze the accuracy level of the DPT, evaluate the effectiveness of the K-Means algorithm in grouping data, and identify factors contributing to the validity of the DPT. The analysis results show that the K-Means algorithm succeeded in grouping voter data with good quality, with a Davies-Bouldin Index (DBI) value of 0.389, which indicates clearly defined clusters. The main factors that influence clustering are age, distance to TPS, and location (RT and TPS). This research shows that the K-Means algorithm can be used to detect inaccuracies in voter data, such as data that does not match the TPS location or age that does not meet the requirements as a voter. With these results, the K-Means algorithm makes a significant contribution to validating voter data, thereby supporting a more transparent and accountable election process.
Identify Rattan Sales Patterns Using the FP-Growth Algorithm on CV. Busaeri Rattan Robi; Nana Suarna; Irfan Ali; Dendy Indriya Efendi
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.v4i2.877

Abstract

This research was conducted to recognize the pattern of purchasing rattan products at CV. Busaeri Rattan by utilizing the FP-Growth algorithm. The rattan industry is faced with the challenge of understanding consumer habits in order to improve marketing strategies. The FP-Growth algorithm was chosen for its ability to efficiently identify frequent itemset patterns without requiring a lot of memory. This research includes collecting rattan sales transaction data for one year, data preprocessing, FP-Tree structure formation, and frequent itemset analysis. The analysis was conducted using RapidMiner software with a minimum support setting of 0.005 and confidence of 0.1. The processed data was then used to find combinations of products that are often purchased together. The results revealed some significant patterns, such as the products “Mandola 3/4” and “Jawit 8/11,” which are often purchased together with a confidence level of 100%. These findings provide important insights for CV. Busaeri Rattan in increasing sales through promotional strategies such as bundling or discount offers. In addition, the FP-Growth algorithm proved to be faster and more resource-efficient than traditional methods such as Apriori. The discussion shows that the discovered purchasing patterns can help CV. Busaeri Rattan better manage stock, minimize the risk of running out of goods, and design data-driven marketing strategies. The combination of products that are often purchased together can be utilized to improve customer satisfaction as well as operational efficiency. The conclusion of this research is that the FP-Growth algorithm is an effective tool for analyzing large-scale transaction data. Further research is recommended to explore the application of this algorithm to other types of products or compare it with other data mining algorithms.
Educational Media Introducing Computer Devices Based on Augmented Reality for Elementary Schools Hafizh, Maulana; Ilham Faisal; Arief Budiman
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.v4i2.885

Abstract

Early mastery of technology is a crucial step in preparing young generations to face the challenges of the digital era. Therefore, introducing computer devices to elementary school students is essential. This study aims to develop an educational media based on Augmented Reality (AR) as an interactive learning tool for introducing computer devices to elementary school students. This educational media is designed to facilitate a more engaging and immersive learning process by visualizing computer devices in three dimensions and allowing students to interact with virtual objects. Through the use of AR, students can learn about computer components such as monitors, keyboards, mice, and CPUs in a more interactive and enjoyable way. Trials conducted in several elementary schools students indicate that the use of this media improves students' understanding of computer device concepts compared to conventional teaching methods. Therefore, this AR-based educational media is expected to serve as an innovative alternative in information technology education at the elementary school level.
Detection of Rotten Fruits at Pomona Fruit House Using the Convolutional Neural Network Method Kosasih, Ferly; Hendri; Hendrik, Jackri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 1 (2025): October 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i1.1705

Abstract

The growing public awareness of healthy lifestyles has led to an increasing demand for fresh and high-quality fruits. However, during storage and distribution, fruits are prone to spoilage due to environmental and biological factors. The manual identification process of spoiled fruits remains limited in terms of accuracy and efficiency. To address this issue, this study proposes the application of digital image processing technology based on Convolutional Neural Network (CNN) to automatically detect the condition of fruits. This system is designed to assist in quality monitoring at locations such as Rumah Buah Pomona by classifying fresh and spoiled fruits based on their visual features. This solution is expected to improve the effectiveness of fruit distribution and reduce potential losses caused by unfit products.
Handwritten Batak Toba Script Recognition Based on Deep Learning Using the Convolutional Neural Network (CNN) Algorithm Samosir, Wahyu Ardiantito; Zulfahmi Indra; Insan Taufik; Susiana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 1 (2025): October 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i1.1795

Abstract

The Batak Toba script is one of Indonesia’s cultural heritages that has become increasingly rare and less recognized among younger generations. This research aims to develop a handwriting recognition system for Batak Toba characters using the Convolutional Neural Network (CNN) method, capable of accurately recognizing characters, transliterating them into Latin script, and translating them into Indonesian. The dataset was self-generated using the Noto Sans Batak font and character combinations, totaling 113 labels, which were processed into 64×64 grayscale images. The CNN model was designed with several convolutional and pooling layers and compiled using the Adam optimizer and categorical cross-entropy loss function. Training results achieved a validation accuracy of 98.36% and a testing accuracy of 98.12%, with respective loss values of 0.0268 and 0.0295. The system was then integrated into a web-based application built as a Progressive Web App (PWA), supporting both online transliteration and translation features. These results demonstrate that the CNN approach is highly effective in recognizing Batak Toba characters. In the future, the system can be further developed into a full sentence-level OCR, integrated into a native Android application, and expanded with datasets from real handwritten samples.
User Satisfaction Analysis on the Quality of Lapisbogor.co.id Website using the Webqual 4.0 Method Moses Aripin Indah Setiawan; Kudiantoro Widianto; Syaifur Rahmatullah AR; M. Iqbal Alifudin; Irmawati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 1 (2025): October 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i1.1797

Abstract

Website quality is a vital element for the sustainability and customer satisfaction within the digital business landscape, especially for prominent brands like Lapis Bogor Sangkuriang that rely on an online presence. This study was designed to investigate the impact of website quality, measured by WebQual 4.0 (encompassing Usability Quality, Information Quality, and Service Interaction Quality), on user satisfaction with the lapisbogor.co.id website. A quantitative approach was applied through the dissemination of questionnaires to 150 respondents who are users of the Lapis Bogor Sangkuriang website. Regression analysis results indicate that all three WebQual 4.0 dimensions collectively have a significant influence on user satisfaction. This finding suggests that the website quality of Lapis Bogor Sangkuriang has successfully met user expectations, with website quality variables contributing approximately 58% to the perceived satisfaction level. Therefore, this research recommends that Lapis Bogor Sangkuriang continue to invest in monitoring and improving its website quality. Continuous efforts in optimizing the WebQual 4.0 dimensions will be crucial for maintaining a positive user experience and strengthening the company's competitive position in the market.  
Bus Scheduling Simulation on Urban Routes Using Discrete Event Simulation Approach Meliala, Najwa Aulia; Ferdiansyah Prayoga; Muhammad Kurniawan; M. Khalil Gibran
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 1 (2025): October 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i1.1798

Abstract

Efficient bus scheduling is a crucial element in urban transportation systems. This study aims to simulate a bus scheduling system on an urban route using the Discrete Event Simulation (DES) approach. The simulation models bus movement, passenger arrivals, and waiting times at each stop. The system consists of five bus stops and three buses operating on a fixed schedule. The simulation results indicate that passenger waiting times vary significantly across stops. The first stop shows the lowest waiting time, while mid- and end-route stops experience cumulative delays. The average waiting time can exceed 60 minutes at certain locations. Alternative scenarios involving additional buses show a notable decrease in waiting times and a significant improvement in scheduling efficiency. This study demonstrates that DES is an effective method for evaluating and designing adaptive and efficient transportation systems in urban environments.
Implementation of Artificial Intelligence (AI) Role-Based NormalizationMap of Demographic Data of Prospective Students of SD Al-Imam Islamic School (AI IS) Bajsair, Faik
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 1 (2025): October 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i1.1799

Abstract

This research examines the application of rule-based Artificial Intelligence (AI) to address demographic data inconsistency among prospective students at SD Al-Imam Islamic School. Unstructured applicant data, particularly in the village/sub-district address column, often impedes efficient analysis and strategic decision-making. By implementing a dictionary-based normalization technique (normalizationMap) using Google Apps Script, this study aims to enhance data quality and minimize input inconsistencies. The role-based approach ensures that various input formats are mapped to a predefined standard. The implementation results demonstrate a significant improvement in the accuracy of the address data, directly supporting more precise demographic visualization. This practical and effective AI solution facilitates data-driven decision-making for future student enrollment strategies, showcasing a tangible contribution to data management within a limited-resource educational environment.

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