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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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Performance Evaluation of the BERT Model in Sentiment Analysis of DANA Application User Reviews Hazael Susanto; Weiskhy Steven Dharmawan; Riski Annisa; Lady Agustin Fitriana
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.v5i3.2359

Abstract

The rapid growth of digital wallets in Indonesia generates a large volume of user reviews on platforms such as the Google Play Store that cannot be efficiently analyzed manually. This study aims to evaluate the performance of the BERT (Bidirectional Encoder Representations from Transformers) model in sentiment classification tasks on a dataset of DANA application user reviews collected from the Google Play Store. The BERT model is fine-tuned using labeled Indonesian-language data with three sentiment classes: positive, negative, and neutral. Specialized preprocessing strategies are applied to handle the characteristics of informal text, abbreviations, and code-switching phenomena prevalent in Indonesian user reviews. Evaluation is conducted using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that the fine-tuned IndoBERT model achieves an accuracy of 91.24% with a weighted F1-score of 0.91 on a test dataset of 6,106 samples. The Negative class achieves the highest performance with an F1-score of 0.95, followed by the Positive class (0.88) and Neutral class (0.84). This study provides empirical evidence of the effectiveness of the IndoBERT Transformer architecture for sentiment analysis in the Indonesian-language fintech domain and can serve as a reference for developing deep learning-based NLP systems in similar contexts.
Analysis of Green Computing Implementation in Efforts to Improve Resource Efficiency in the Campus Environment Alfin Budiman Sihotang
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.v5i3.2360

Abstract

This study aims to analyze the level of green computing implementation in efforts to improve resource efficiency in the campus environment. The primary problem addressed is the high energy consumption in higher education environments due to the increasing use of information technology devices, necessitating efficient and sustainable energy management measures. This study employs a mixed methods approach, combining literature review with quantitative data collection through questionnaires to obtain data on user understanding and behavior regarding green computing. The results indicate that the majority of respondents demonstrate a good understanding of energy efficiency. Based on the data obtained, the authors conclude that the level of awareness and implementation of green computing among students is very good. The findings also reveal that students have a high concern for the impact of energy consumption on the environment and support energy conservation efforts. Overall, this study demonstrates that the application of green computing has great potential for development through broader research and targeted campus policies. This research is expected to serve as a foundation for developing more efficient energy policies in higher education.
Design of a Web-Based Village Tourism Management Information System with Multi-Tenant Architecture as an Integrated Platform: The Nusa Wisata Module Mayza Nurul Khasanatun Nisa mayza; Dinar Mustofa; Aulia Suryaning Tyas; Intan Nur Sifa; Purnia Setiawati; Rizki Cahya Putri; Sri Rahayu; Lina Nur Afifah
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.v5i3.2361

Abstract

The rapid development of information technology has significantly impacted various sectors, including tourism. However, in practice, village tourism management is still commonly conducted conventionally and lacks integration, resulting in various issues related to service efficiency, data management, and operational transparency. This study aims to design a web-based village tourism management information system using a multi-tenant architecture approach in the Nusa Wisata module as part of the integrated Nusa Eka platform.The research method employed in this study is system design using the System Development Life Cycle (SDLC) approach, focusing on the requirements analysis and system design stages. System modeling was conducted using Entity Relationship Diagram (ERD) for database design and flowcharts to illustrate the system process flow. The results of this study are in the form of a system design capable of integrating the management of multiple villages within a single platform while maintaining data separation through the use of the tenant_id attribute. The system is designed with several main features, including e-ticketing, tourism package and attraction management, tourism operational staff management, and parking management with an automated revenue-sharing mechanism. In addition, the system supports integration with other modules within the Nusa Eka platform, such as Nusa Praja, Nusa Graha, and Nusa Artha. Based on the design and analysis results, the proposed system provides a more integrated, flexible, and scalable solution compared to previous systems that still use a single-tenant approach. This system design is expected to improve operational efficiency, data transparency, and service quality in digital-based village tourism management.
Performance Evaluation of Machine Learning Algorithms in Sentiment Analysis of Spotify Reviews Frizi Olivian; Sahrul Bariyah; Grant Christo Budiyanto; Riski Annisa; Lady Agustin Fitriana; Weiskhy Steven Dharmawan
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.v5i3.2362

Abstract

The rapid growth of digital music streaming platforms has generated a massive volume of user reviews on the Google Play Store, making manual analysis practically infeasible. This study evaluates and compares the performance of three machine learning algorithms Support Vector Machine (SVM), Neural Network (Multilayer Perceptron), and Random Forest in classifying sentiments from Spotify user reviews written in Indonesian. A total of 10,000 reviews were collected from the Google Play Store using the google-play-scraper library and processed through a text preprocessing pipeline comprising cleaning, case folding, word normalization, tokenization, stopword removal, and stemming using the Sastrawi library. Sentiment labeling was performed automatically using the InSet lexicon, categorizing reviews into three classes: Positive (56.63%), Neutral (30.60%), and Negative (12.76%). Feature extraction was conducted using the TF-IDF method, with an 80:20 train-test split strategy and stratified sampling to maintain class distribution. Model performance was evaluated based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that SVM and Neural Network achieved equivalent and superior accuracy of 0.937, with macro F1-scores of 0.908 and 0.907, respectively, outperforming Random Forest which recorded an accuracy of 0.853 and a macro F1-score of 0.777. These findings indicate that SVM and Neural Network are more optimal and reliable for sentiment classification of Indonesian-language Spotify reviews, while Random Forest requires further improvement, particularly in recognizing minority classes.
Topic Modeling of Clash of Clans Player Reviews Using NLP-Based Latent Dirichlet Allocation (LDA) Machine Learning Method Rai Markus Panamuan; Debi Handika; Muhamad Rizki Pratama; Weiskhy Steven Dharmawan; Lady Agustin Fitriana; Riski Annisa
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.v5i3.2364

Abstract

The rapid growth of the mobile gaming industry has generated millions of player reviews on platforms like the Google Play Store. Clash of Clans, developed by Supercell, is one of the world's most popular mobile strategy games, generating a vast volume of user reviews that are difficult to analyze manually. This study applies Latent Dirichlet Allocation (LDA), a generative probabilistic machine learning model based on Natural Language Processing (NLP), to identify and cluster key topics discussed in player reviews on the Google Play Store. A total of 10,000 player reviews were collected through web scraping, followed by NLP-based text preprocessing including tokenization, stopword removal, and lemmatization. The LDA model was optimized using a coherence score evaluation of 0.512, resulting in the identification of five dominant discussion topics: technical issues and bugs, game updates and balance, gameplay and strategy, monetization and in-app purchases, and social interactions and clan systems. The results show that LDA-based topic modeling provides structured and actionable insights for game developers to understand player feedback and improve game quality. This research contributes to the field of NLP-based mobile game review analysis.
Web-Based Congregation Data Management Information System for the Pamalar Sumba Christian Church Marthen Umbu Delu Palabu; Rambu Yetti Kalaway; Alfrian Carmen Talakua
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.v5i3.2365

Abstract

Information technology plays an important role in improving efficiency, accuracy, and accessibility in administrative processes. Churches with large and dispersed congregations often face difficulties in managing and searching congregation data. The Sumba Christian Church, located in Umbu Langang Village, Central Sumba Regency, also experiences these challenges. The congregation consists of 1,231 members, including 589 males and 642 females, and the number continues to grow. Currently, congregation data is still recorded manually in books, making data updates slow, inefficient, and prone to errors or data loss due to non-centralized storage. To address these problems, this study developed a web-based Congregation Data Management Information System using the Waterfall development method. The system aims to simplify data recording, updating, and searching processes, making church administration more effective and efficient. The implementation of this system is expected to improve the speed, accuracy, and reliability of congregation data management. System testing was conducted using the Black Box Testing method, which showed that all system features functioned successfully with a 100% success rate. In addition, usability evaluation using the System Usability Scale (SUS) produced an average score of 80.75, indicating that the system is highly usable and acceptable for church administration activities.
Design and Development of a News Website and CMS Based on Three-Tier Architecture: A Case Study of PLTU Pangkalan Susu Zainuddin; Veri Ilhadi
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.v5i3.2367

Abstract

Pangkalan Susu Steam Power Plant does not yet have a structured official publication platform, causing information dissemination to depend mainly on social media and making news archives difficult to organize and retrieve regularly. This condition highlights the need for a web-based information system that supports more professional content management. This study aims to design and develop a news website and Content Management System (CMS) by applying a Research and Development (R&D) approach and three-tier architecture, which separates the presentation layer, application logic layer, and data layer. The development process consists of requirements analysis, system architecture and database design, implementation, and functional testing. The system was implemented using Laravel as the application framework, Blade for the user interface, Filament as the administration panel, and MySQL as the database management system. Functional testing was conducted using the black-box method on 14 main scenarios, including the management of news, categories, authors, banners, and logout features. The results show a 100% success rate, indicating that all tested functions operated according to the expected outcomes without functional errors. The proposed architecture produces a modular, scalable, and maintainable system that can support the future development of the official information platform of Pangkalan Susu Steam Power Plant.
Integration of the Webqual 4.0 and UEQ Methods in Evaluating the Impact of the Student Recruitment Information System on Business Efficiency and Admissions Services (Case Study: Sari Mutiara Indonesia University) Lius Luaha; Delisman Hulu; Rianto Sitanggang; Thomas Gentelman Loi; Ebrael Harita; Novia Marito Hutagalung
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.v5i3.2368

Abstract

This study aims to evaluate the service quality of the New Student Admission Information System (PMB) at Sari Mutiara Indonesia University and its impact on business efficiency using the WebQual 4.0 method and the User Experience Questionnaire (UEQ). Based on a population of 1,700 users, the sample size was determined using the Slovin formula (5% margin of error), resulting in 324 respondents (319 prospective students, 5 admissions staff) selected via purposive sampling. The WebQual 4.0 evaluation yielded an average score of 4.05 (Good category), with the Usability dimension scoring highest (4.30) and Service Interaction lowest (3.80). UEQ analysis showed that all scales fell within the positive category (>0.8), with the main strength lying in the pragmatic aspect of Perspicuity (2.36), whilst recording the lowest score in the hedonic aspect of Novelty (1.47). Operationally, the system’s high ease of use has been shown to reduce data verification time by 75% (from 20 to 5 minutes) and lower the human error rate from 12% to below 2%. In conclusion, the PMB system operates very efficiently from a business perspective and is user-friendly; however, an update to the user interface (UI/UX) is urgently needed to enhance the novelty value, along with the integration of a more responsive technical support service.
Implementation of the K-Means Method for Developing an Air Quality Monitoring Information Faisal Rifky Nugraha; Adiat Pariddudin; Anggra Triawan; Fitria Rachmawati
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.v5i3.2370

Abstract

Air quality in Bogor City is becoming increasingly complex due to the rising number of motor vehicles, small-scale industrial activities, and seasonal dynamics that are difficult to analyze using conventional methods. The Environmental Agency of Bogor City routinely monitors air quality through the Air Quality Monitoring System (AQMS); however, data utilization remains confined to monitoring and reporting, necessitating advanced analysis to achieve a more comprehensive view of air quality patterns. This study aims to classify time periods based on air quality parameters using the K-Means clustering method to identify good and bad air pollution categories. The research data was obtained from the Bogor City Environmental Agency's AQMS for the period from January 2023 to June 2025. The results indicate that the K-Means method successfully clustered the data into two groups: good and bad air quality categories. Good air quality was identified in 2023 (January, February, March, April, November, and December), 2024 (January, February, March, April, October, November, and December), and 2025 (January to May). Conversely, poor air quality occurred in 2023 (May to October), 2024 (May to September), and 2025 (June). The findings of this research are expected to support pollution control strategies and early warning systems based on air quality data.
Designing the Foundation of a Multi-Tenant and Surrogate Key-Based Nusa Praja Village Government System on the Nusa Eka Platform Sri Rahayu; Aulia Hamdi; Lina Nur Afifah; Aulia Suryaning Tyas; Rizki Cahya Putri; Mayza Nurul Khasanatun Nisa; Intan Nur Sifa; Purnia Setiawati
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.v5i3.2371

Abstract

Village government administration in Indonesia is still largely manual, resulting in service inefficiencies and the potential loss of sensitive population data. The purpose of this research is to create the architectural foundation of a village government information system called NUSA PRAJA, which is part of the NUSAEKA multi-tenant Software as a Service (SaaS) platform. This research applies the Multi-Tenant Isolation concept to ensure data security and separation between villages, as well as the Surrogate Key concept to protect residents' National Identification Number (NIK) data from leaks. The research method used is Waterfall with the stages of requirements analysis, system architecture design, and database design. The results of the research are a multi-tenant system architecture design based on a shared database with tenant_id filters, an Entity Relationship Diagram (ERD) design with 14 main tables, and a flowchart design for three user roles. This project is expected to be a strong foundation for building a secure, scalable, and easy-to-use digital village system for village governments throughout Indonesia.

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