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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
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The Effect of Mobile Banking Usage on Banking Customer Satisfaction Ayu Maulidia; Silvia Anita Dewi; Moh.Yogi Nuruzzalam; Achmarul Fajar
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.v5i3.2347

Abstract

The rapid development of information technology has encouraged the banking sector to innovate through digital-based services, one of which is mobile banking. This service provides convenience for customers in conducting banking transactions quickly, effectively, and efficiently through smartphones without having to visit bank offices. This study aims to determine the effect of mobile banking usage on customer satisfaction in banking services. The research method used is a quantitative descriptive approach with data obtained from previous journals and supporting literature related to mobile banking and customer satisfaction. The results indicate that the use of mobile banking has a positive and significant effect on customer satisfaction. Factors such as ease of use, transaction speed, security, service quality, trust, and digital banking innovation are proven to influence customer satisfaction in using mobile banking services. In addition, digital banking services are able to improve customer convenience and efficiency in carrying out financial transactions. Therefore, banks are expected to continuously improve the quality of digital services, strengthen transaction security systems, and provide sustainable innovations to maintain customer satisfaction and loyalty in the digital era.
Public Sentiment Analysis on the Issuance of Panda Bonds as an Effort for Rupiah Stability using SVM Algorithm on Youtube Social Media Junjung Rahmat Santosa; Rangga Apriwijaya; Ilham Ardiasyah; Rangga Apriansyah; Destiarini
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.v5i3.2350

Abstract

The stability of the Rupiah exchange rate is a crucial indicator of Indonesia's economic health, one of which is pursued through the issuance of Panda Bonds. However, this policy has triggered dynamic discourse on social media, particularly YouTube. This study aims to map public perception and test the performance of the Support Vector Machine (SVM) algorithm in classifying sentiments related to this issue. The research methodology includes scraping YouTube comment data, text preprocessing, automated labeling using the Lexicon-based method, and classification using SVM with a Linear kernel. From a total of 659 collected data, the results show that public sentiment is dominated by positive responses at 51.9%, followed by neutral sentiment at 29.0%, and negative sentiment at 19.1%. While public concerns focus on the debt burden and foreign currency dependence, there is overall support for economic stability efforts. The model evaluation demonstrates excellent performance, achieving an accuracy rate of 87.86%, precision of 88.79%, and an F1-score of 87.96%. This proves that a hybrid approach between Lexicon-based and SVM is effective in analyzing complex public opinions within the economic domain on social media.
Design of a Multi-Tenant SaaS-Based Centralized Financial System Using a Silent Accounting Approach Rizki Cahya Putri cahyaputri; Azhari Shouni Barkah; Aulia Suryaning Tyas; Intan Nur Sifa; Purnia Setiawati; Mayza Nurul Khasanatun Nisa; 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.v5i3.2356

Abstract

Village financial management faces various fundamental challenges, including transaction recording that is still manual, a lack of integration between financial systems and village operational activities, and the absence of a platform capable of serving multiple villages within a single efficient infrastructure. These conditions result in financial reporting processes that are inefficient, error-prone, and difficult to account for. This study aims to design a centralized financial system based on a multi-tenant Software as a Service (SaaS) architecture using the Silent Accounting approach, defined as an automated transaction recording mechanism triggered by operational module activities without manual intervention. This study employs a qualitative descriptive method with a literature review approach. The design yields three main artifacts a system flowchart illustrating the workflow from user authentication, role assignment, and transaction validation through to automatic journal entry and posting to general ledger an Entity Relationship Diagram (ERD) modeling the database structure consisting of seven entities and a Data Flow Diagram (DFD) breaking down the system into five main processes A multi-tenant architecture with a ‘tenant_id’ column ensures data isolation between villages while allowing a single platform to serve multiple village simultaneously. The Silent Accounting mechanism ensures that all village financial activities are recorded consistently, accurately and in real time. The design is expected to serve as the foundation for the development and scalable village financial management platform.
Evolution and Impacts of AI-Based Rainfall Prediction Systems on Agricultural Management in Tropical Regions: A 20-Year Systematic Review Safrizal; Ika Safitri Windiarti
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.v5i3.2357

Abstract

Global climate change has significantly disrupted rainfall patterns in tropical regions, posing major challenges to agricultural productivity and food security. Accurate rainfall prediction has become a critical component of data-driven agricultural management. This study conducts a systematic literature review (SLR) following the PRISMA 2020 guidelines to analyze the evolution of AI-based rainfall prediction systems and their multidimensional impacts on tropical agricultural management over the period 2008–2026. Data were sourced from Scopus using three Boolean search strings, yielding 239 records, of which 235 articles were retained after duplicate removal and quality assessment using the Mixed Methods Appraisal Tool (MMAT) with a threshold score of ≥5. Bibliometric analysis was conducted using VOSviewer and Bibliometrix (R), while thematic narrative synthesis was performed using NVivo 14. Results reveal a clear four-phase technological evolution: conventional methods (2008–2015), machine learning adoption (2016–2020), deep learning and IoT integration (2021–2023), and multimodal and large language model era (2024–2026). Technical impacts dominated the corpus (accuracy improvements of 18–35%), while social and economic impact studies remain critically underrepresented (2.6% and 0.9%, respectively). Key research gaps identified include poor model interpretability (black-box problem), limited integration with decision support systems (DSS), inadequate tropical-specific model development, and the near-total absence of longitudinal impact evaluations. This study contributes a holistic synthesis integrating technological evolution with multidimensional impact analysis, offering strategic recommendations for developing more adaptive, transparent, and equitable AI rainfall prediction systems aligned with SDG 2, SDG 13, and SDG 15
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.

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