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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 525 Documents
 48   49   50   51   52 
Design of 3D Puzzle Game "Moodoria" Using Unity as an Educational Media for Emotional Intelligence Bryan Anderson Basli; Didik Aryanto; Joni
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.v5i3.2312

Abstract

Emotional awareness is crucial for mental health, yet conventional education methods are often less engaging for adolescents. This study aims to design and develop a 3D puzzle game called "Moodoria" using Unity as an interactive medium for emotional intelligence education. The research method used is Research and Development (R&D) with the Game Development Life Cycle (GDLC) model, including needs analysis, literature study, concept design, implementation, and user testing. 3D assets were created using Blender. The game was tested on a small group of users (5–10 people) using a Likert-scale questionnaire. Results show that all main features (menu navigation, character movement, object interaction) function well. User assessments scored high on gameplay (4.87 for challenge) and enjoyment (4.67), and the game was considered feasible as an emotional education medium (average score 4.23). In conclusion, "Moodoria" was successfully developed as an engaging educational game, although sound effects and character expression variations need improvement.
Classification of Handwriting Margin Patterns Using Ensemble Bagging Decision Tree Rista Ifanka; Soffiana Agustin
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.v5i3.2313

Abstract

Analysis of handwriting margins plays an important role in graphology, as margin patterns are often associated with individual behavioral tendencies and personality traits. Therefore, detecting and classifying margin characteristics is essential to support automated handwriting analysis using computational approaches. This study uses a computer vision-based approach to classify left-margin handwriting patterns into widening and narrowing categories. The classification is performed by analyzing margin characteristics extracted from scanned handwriting images. The processing pipeline consists of image preprocessing, hybrid feature extraction, and classification using an Ensemble Bagging Decision Tree model. The preprocessing stage enhances image quality through grayscale conversion, contrast adjustment, adaptive thresholding, and noise removal, followed by Region of Interest extraction to focus on the handwriting area. The feature extraction stage applies a hybrid strategy that combines line-based margin analysis and global spatial features to capture both local variations and overall structure. Model performance is evaluated using stratified 5-fold cross-validation to ensure reliable and unbiased results. The experimental findings show that the method achieves an average accuracy of 84.91%, with relatively balanced precision, recall, and F1-score across both classes. These results indicate that margin-based features are effective for representing handwriting patterns in classification tasks. However, variations in writing style and noise from the scanning process may influence performance. Overall, this study demonstrates that the applied approach provides reliable classification results and has potential for further improvement through feature expansion and more advanced learning models.
Implementation of the Gradient Boosting Algorithm for Palm Oil Price Prediction Wilbert Fernando; Hendri; Robby Wijaya
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.v5i3.2316

Abstract

The price of palm oil is highly volatile due to the influence of global market dynamics, trade policies, and climate change, creating uncertainty for industry players in decision-making. This research aims to implement the XGBoost (Extreme Gradient Boosting) algorithm, optimized using Grid Search Cross-Validation, to predict palm oil prices. The dataset used is the Palm Oil Futures Historical Data.csv obtained from Kaggle, consisting of nine features. Data preprocessing is performed using StandardScaler for normalization, followed by model training with hyperparameter tuning. The system is built as a web-based application separating the frontend using PHP and Flask as the Backend API. Testing on 105 test data points yielded an MAE of 43.97, RMSE of 65.14, and R² of 91.82%, demonstrating the model’s strong ability to explain palm oil price variation. Based on the results, the XGBoost algorithm is suitable as a decision-support tool for commodity price prediction, achieving high accuracy consistent with standard criteria for commodity price forecasting and capable of handling large datasets.
Implementation of the Random Forest Algorithm for Loan Eligibility Prediction and Feature Analysis Based on Financial Data Angel; Joni; Herman
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.v5i3.2317

Abstract

The advancement of information technology has led to an increasing demand for loan access, both through banking institutions and online lending platforms. However, the process of evaluating loan eligibility, which is still carried out manually or semi-manually, is prone to human error and decision-making bias, ultimately increasing the risk of loan defaults. This study aims to implement the Random Forest algorithm to predict loan eligibility based on financial data, as well as to evaluate its accuracy. The dataset used in this study is loan_approval_dataset.csv, which is downloaded from Kaggle, utilizing 11 input features. The system is developed as a web-based application using Laravel as the main frontend and backend framework, while Flask is used as a backend API for executing the machine learning processes. The testing results show that the Random Forest model achieves an accuracy of 98.44%, with a precision of 98.14%, recall of 99.37%, and an F1-score of 98.75%. Furthermore, the cibil score feature is identified as the most influential factor in the prediction process, contributing 80.65% to the model's outcome. These findings indicate that the Random Forest algorithm is highly effective for use in a loan eligibility prediction system, as it provides fast, objective, and highly accurate results.
Influencing the Success of SPBE Jambi Provincial Government Using the SEM Method Chandy Ophelia S; Lola Yorita Astri
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.v5i3.2321

Abstract

The Electronic-Based Government System (SPBE) is a strategic instrument for digital governance in Indonesia, but its success in local government remains uneven. The problem addressed in this study is the fluctuating SPBE performance of the Jambi Provincial Government, which is associated with network instability, changing application coordinators, overlapping data input, diverse user age groups, and uneven digital literacy among state civil apparatus (ASN) and service users. This study aims to identify the determinants of SPBE success from the ASN perspective and to explain how system quality and information quality shape perceived ease of use, perceived usefulness, user satisfaction, and net benefits. A quantitative explanatory survey was conducted with 385 ASN respondents who interact with SPBE services in the Jambi Provincial Government. The research model integrates constructs from technology acceptance and information system success perspectives and was tested using partial least squares structural equation modeling. The measurement results show that all indicators are valid, with outer loading values above 0.70, AVE values above 0.50, and Cronbach alpha values above 0.80. The structural results indicate that information quality has the strongest effect on perceived usefulness (beta = 0.861), followed by user satisfaction on net benefits (beta = 0.844) and system quality on perceived ease of use (beta = 0.834). Perceived usefulness also has a stronger effect on user satisfaction than perceived ease of use. These findings confirm that SPBE success in Jambi depends primarily on accurate, complete, timely, and relevant information that creates real work benefits and sustained user satisfaction.
Sentiment Analysis of SPayLater and SPinjam Features in the Shopee Application Using the Support Vector Machine (SVM) Algorithm Rahmad Rahmad Nawi Pane; Wilda Wilda Rina Hasibuan
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.v5i3.2322

Abstract

The rapid development of information technology and the increasing use of e-commerce applications have generated a large number of user reviews that can be used to measure user satisfaction. SPayLater and SPinjam, as features in the Shopee application, receive various responses in the form of positive, negative, and neutral sentiments, making automatic sentiment analysis necessary. This study aims to analyze user sentiment and implement the Support Vector Machine (SVM) algorithm to classify reviews. The data used consist of 500 user reviews obtained from the Google Play Store. The method includes preprocessing, labeling, and classification using SVM. The results show that there are 231 positive, 230 negative, and 39 neutral sentiments. Model evaluation yields an accuracy of 74%, precision of 0.78, and recall of 0.84, indicating that the model performs fairly well. The developed system is also capable of processing data automatically and displaying classification results effectively. Therefore, the SVM algorithm is effective for sentiment analysis of SPayLater and SPinjam services in the Shopee application.
Analysis of Green Computing Implementation Strategies for Energy Efficiency in Server Infrastructure Daniel Rionaldo; Alvin Leonardo Ishak
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.v5i3.2325

Abstract

The rapid development of information technology has increased the use of server infrastructure in various organizations and data centers, resulting in higher energy consumption and operational costs. Green computing is considered an effective approach to improve energy efficiency while reducing environmental impacts. This study aims to analyze green computing implementation strategies for improving energy efficiency in server infrastructure. The research used a descriptive qualitative method through literature studies and comparative analysis of energy management strategies in data centers. The strategies analyzed include server virtualization, server consolidation, energy-efficient hardware, and cooling system optimization. The results indicate that the implementation of green computing can significantly reduce energy consumption compared to conventional server systems. In addition, the implementation improves operational efficiency, reduces electricity usage, and supports environmentally sustainable data center management. Therefore, green computing can be considered an effective solution for developing efficient and environmentally friendly server infrastructure.
Implementation of Random Forest Algorithm for Classifying Land and Building Tax Arrears and Risk Factor Analysis Dashboard Risky Firmansyah Manik; A M H Pardede; Anton Sihombing
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.v5i3.2326

Abstract

This study aims to develop a predictive model to identify the potential for land and building tax arrears and analyze the dominant risk factors contributing to non-compliance. The research utilizes the Random Forest classification algorithm applied to historical tax data from the Regional Financial and Revenue Management Agency of Binjai City. The approach involves data preprocessing, feature engineering including target encoding for geographical areas, and model training with hyperparameter tuning to optimize classification performance. Furthermore, a web-based interactive dashboard is developed using the Flask framework to visualize the predictions and risk factors. The results demonstrate that the Random Forest model achieves a robust and consistent accuracy of approximately 85% in classifying compliant and non-compliant taxpayers. Feature importance analysis reveals that land area is the most dominant risk factor influencing tax arrears, significantly outweighing other variables. In conclusion, the integration of the Random Forest algorithm with an interactive dashboard provides a highly accurate, efficient, and scalable solution for local governments to transition from reactive tax collection to proactive, data-driven risk management.
Design of a Multi-Tenant Waste Management System with Volume Estimation and Vehicle Trip Optimazation Intan Nur Sifa
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.v5i3.2327

Abstract

Waste management at the village level still faces a number of challenges, such as unstructured waste volume recording, suboptimal collection scheduling, and a lack of transparency in cost management. This study aims to design a multi-user waste management system equipped with a volume estimation model and vehicle route optimization. The approach applied includes a literature review to analyze system requirements, followed by design using flowcharts, Data Flow Diagrams (DFDs), and Entity-Relationship Diagrams (ERDs). The research findings indicate that the developed system successfully integrates the management of waste source data, transportation processes, and cost calculations in a structured manner. The volume estimation model is used to estimate the amount of waste in the field, while route optimization determines the number of vehicle trips based on their carrying capacity. Additionally, the multi-tenant concept allows this system to be used by various regions simultaneously while ensuring data separation. Therefore, this system is expected to improve operational efficiency, management transparency, and the quality of waste transportation services.
Application of Data Mining using the Apriori Algorithm in Analyzing Subject Selection Patterns of Tutoring Students Rizky Ferdiansyah; Naufal renanda; Afriza Akhid Khoiruddin; Arya Subastian; Muhammad Arifin
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.v5i3.2328

Abstract

This study examines the application of data mining using the Apriori algorithm to analyze subject selection patterns among tutoring students in Kudus, Central Java. With the increasing number of students attending tutoring, understanding subject selection patterns is crucial to improve the effectiveness of educational services. The Apriori algorithm, a popular association rule mining technique, is used to identify relationships between frequently selected subjects. The research dataset consists of student subject selection transaction data, including information such as student name, student ID number, tutoring branch, and selected subjects. The analysis process included data preprocessing, data transformation into transaction format using Transaction Encoder, application of the Apriori algorithm with a minimum support of 0.05, and formation of association rules with a minimum confidence of 0.3. The results show frequent itemsets indicating the most popular subjects and association rules that describe students tendencies in selecting subject combinations. These findings can be utilized by tutoring managers to design more effective learning packages, optimize the allocation of teaching resources, and provide subject recommendations tailored to student needs. This research contributes to the development of educational data mining in the context of tutoring institutions in Indonesia.

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