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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 430 Documents
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Sentiment Analysis of Public Opinion on RUU KUHAP 2025 Using Multinomial Naïve Bayes and Random Oversampling Muhammad Aqshal Anindya Tratama; Fadli Santoso Murmita; Dimas Arsya Maulana; Cindy Renata; Raras Ailsa
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1895

Abstract

The ratification of the Draft Criminal Procedure Code (RUU KUHAP) in 2025 has triggered a significant wave of public reaction on social media, particularly on YouTube. Understanding these public sentiments is crucial for evaluating the legislative performance of the House of Representatives (DPR). This study aims to classify public opinion into positive and negative sentiments using the Multinomial Naïve Bayes algorithm. The dataset consists of 2,370 user comments collected from YouTube. To address the chal lenge of unstructured text, a comprehensive pre - processing pipeline was implemented, including cleaning, normalization, and stemming. Furthermore, this research addresses the issue of class imbalance , where negative comments dominated (73.9%) by applying the Random Oversampling (ROS) technique to the training data. The feature extraction was performed using TF - IDF. The experimental results demonstrate that the proposed model achieved an overall Accuracy of 87.22%. Detailed evaluation shows a Pr ecision of 0.9 1 and Recall of 0.93 for the negative class, confirming the model's robustness. These findings indicate that the majority of public sentiment is critical of the RUU KUHAP , focusing on issues of corruption and trust. This research contributes to the field of text mining by demonstrating the effectiveness of oversampling in improving Naïve Bayes performance on imbalanced social media data.
Measurement of Digital Service Quality and Success of District Library Information System (SIPERKA) Implementation: Integration of HOT-Fit Model and Service Quality Dimensions Piarna, Rian; Masesa Angga Wijaya; Agin Sugiwa; Liandy L Tobing; Wulan Siti Nurul Masriah; Muthiah Wahyuliana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1896

Abstract

This study evaluates the success of District Library Information System (SIPERKA) implementation in Subang Regency using the integration of HOT-Fit (Human, Organization, Technology-Fit) model and service quality dimensions. Employing SEM-PLS methodology with 115 village librarians and library operators, the research examines how Technology components (system quality, information quality, service quality), Human factors (user satisfaction, user competency, system use), and Organizational aspects (organizational structure, leadership support, environment) collectively influence net benefits. Results demonstrate that service quality exerts the strongest influence (β=0.312) on user satisfaction among technological dimensions, while system use (β=0.468) emerges as the primary determinant of net benefits. The integrated model explains 71.5%-76.8% variance in endogenous variables with Goodness of Fit (GoF) of 0.719, indicating excellent model performance. All ten hypotheses received empirical support (p<0.05). This research contributes theoretically by demonstrating the critical importance of service marketing perspective in public sector information systems evaluation, revealing that service quality supersedes technical quality in determining user satisfaction. Practically, it provides evidence-based recommendations for improving digital service quality in village libraries, with documented Return on Investment (ROI) of 630.8% demonstrating SIPERKA's success in elevating village library data achievement from below 40% to 87%.
Sentiment Analysis of Honda Esaf Frame Quality Based on Reviews on Platform X using Support Vector Machine Algorithm Setyawan, Jefri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1901

Abstract

This study analyzes public sentiment towards Honda's eSAF frame through 1513 reviews on Platform X during the period of January 2023-October 2025, which was triggered by crucial issues related to the potential for rust, corrosion, and fracture in motorcycle frames. Using a quantitative method with a computational approach, this study applies the Support Vector Machine (SVM) Algorithm with data preprocessing (Case Folding, Cleaning, Tokenizing, Stopword Removal, Stemming), TF-IDF weighting, and Lexicon-based sentiment labeling to classify positive and negative perceptions. The evaluation results show that the SVM-TF-IDF model achieved 98% accuracy on the test data, with negative sentiment dominated by the keywords "rust" and "damaged", while positive sentiment centered on "strong" and "safe", providing an objective picture of public perception as a basis for evaluating product quality and improving corporate communication strategies.
The Relationship Between Hedonism Lifestyle and Student Consumer Behavior in Pamekasan District Fadali, Fadali Rahman; Dera Damayanti; Dwi, Dwi Indah Ria Astari; Ilmi, Yulia Ilmi Qur'ani; Istianah, Mohammad Raihan Alghifari; Raihan, Istianah Asas
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1904

Abstract

Technological developments and globalization have encouraged the emergence of a hedonistic lifestyle among college students, characterized by a tendency to pursue pleasure, luxury, and trend-driven consumption. This situation has the potential to influence student consumer behavior, including excessive purchasing and a lack of consideration for real needs. This study aims to analyze the relationship between a hedonistic lifestyle and student consumer behavior in Pamekasan Regency. The study used a quantitative approach with a correlational approach. The sample consisted of 104 students selected through purposive sampling. Data collection was conducted through an online questionnaire with a Likert scale. Validity tests using Pearson Product Moment correlation showed all items were valid, while reliability tests yielded a Cronbach's Alpha value of 0.956, indicating high reliability of the instrument. Normality tests showed the data were normally distributed (sig. X = 0.080; Y = 0.070). Spearman's Rho correlation test yielded a coefficient value of 0.780 with a significance level of 0.000. The results of this study indicate a strong, positive, and significant relationship between a hedonistic lifestyle and student consumer behavior. This means that the higher the level of hedonism in students, the higher their tendency to engage in consumer behavior. Therefore, a hedonistic lifestyle is a significant factor influencing student consumption patterns in Pamekasan Regency. This study concluded that the higher the level of hedonism in students, the higher their tendency to engage in consumer behavior. These findings are expected to serve as a guide for universities, parents, and students in understanding and managing consumption patterns to be more rational and based on priority needs.
Sentiment Analysis of Mie Gacoan Pemuda Cirebon Restaurant Reviews Using Support Vector Machine Darusman, Aditya
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1905

Abstract

The growth of digital platforms has increased the use of sentiment analysis to understand public perceptions of business services. Customer reviews on Google Maps provide valuable insights but are unstructured and linguistically diverse, requiring robust analytical methods. This study conducts sentiment analysis on reviews of Mie Gacoan Pemuda Cirebon using a Support Vector Machine (SVM) classifier. The research focuses on designing an effective text preprocessing pipeline, identifying sentiment distribution, and evaluating SVM performance. The methodology includes web scraping, manual labeling, text preprocessing, TF-IDF feature extraction, dataset splitting, model training, and evaluation using accuracy, precision, recall, and F1-score. The results show that the majority of reviews are positive, and the SVM model achieves strong performance with an accuracy of 0.82. These findings provide an objective overview of customer perceptions and demonstrate the effectiveness of SVM for Indonesian-language sentiment classification. The model can support businesses in improving service quality based on customer feedback.
Classification of Pneumonia Using CNN and Vision Transformer Shomsomi, Ma`dan; Triawan, Widhaksa; Purwadi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1906

Abstract

Pneumonia remains one of the leading causes of mortality among children worldwide. This study aims to evaluate the performance of two deep learning architectures, Convolutional Neural Network (CNN) and Vision Transformer (ViT), for pneumonia classification using chest X-ray images. Four training scenarios were examined, consisting of MobileNetV2 baseline, MobileNetV2 fine-tuned, ViT baseline, and ViT fine-tuned models. The dataset was obtained from the Chest X-Ray Images (Pneumonia) collection and was processed through augmentation and preprocessing to produce a balanced set of 9,000 images. Baseline models were trained using a feature extraction approach, while fine-tuning was conducted by selectively unfreezing internal layers. Experimental results show that all models achieved accuracy above 95%. The MobileNetV2 baseline reached 97.63%, while its fine-tuned counterpart did not yield further improvement, achieving 97.41%. In contrast, the Vision Transformer demonstrated substantial performance gains, where partial fine-tuning produced the highest accuracy of 98.59% with an f1-score of 0.99. These findings indicate that ViT with targeted fine-tuning is more effective in capturing global representations within X-ray images, making it a strong candidate for computer-aided pneumonia detection systems supported by artificial intelligence.
Basic Analysis of Cybersecurity in Facing Digital Threats in the Industrial Era 5.0 Elsa Wardani; A. Hamdani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1911

Abstract

Industri 5.0 fokus pada kerja sama yang lebih mengutamakan manusia, serta keberlanjutan dan ketahanan. Teknologi canggih seperti kecerdesan buatan (IA), internet of things industri (IIOT), dan robot yang bekerja bersama manusia (cobot) terintegrasi ke dalam industri ini. Karena adanya keterhubungan yang lebih baik antara dunia fisik dan dunia digital, maka peningkatan ini berdampak lebih besar pada peningkatan risiko serangan digital, sehingga mengancam data, sistem, dan bahkan keselamatan manusia. Penelitian ini bertujuan untuk menganalisis secara mendalam fondasi keamanan siber dalam konteks industri 5.0, serta menemukan strategi yang bisa diterapkan dalam menghadapi ancaman digital yang terus berkembang. Metode yang digunakan adalah observasi bahan bacaan secara sistematis dan analisis deskriptif kualitatif terhadap kerangka kerja keamanan siber yang ada, seperti NIST, ISO 27001, dan IEC 62443, terutama dalam konteks teknologi industri 5.0. hasil penelitian menunjukkan bahwa perlu ada perubahan dari perlindungan yang hanya di sekitar batas fisik ke model keamanan yang lebih proaktif, terdistribusi, dan berdasarkan risiko. Model ini menekankan pentingnya arsitektur zero trust, Perlindungan data yang menyeluruh, dan pemantauan ancaman yang ditingkatkan oleh IA. Selain itu, kesadaran dan pelatihan tenaga kerja juga ditemukan sebagai bagian penting dari kemanan siber.
The Effect of E-Wallet Usage on Personal Cash Flow and Net Worth Ratio in Generation Z Nur Safitri, Wanda; Fadali, Fadali Rahman; Zuhal, Zuhal Thoriq; Aisyah , Aisyah Rievliani; Isnain , Isnain Bustaram
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1915

Abstract

The rapid development of financial technology has significantly transformed individual digital financial behavior, particularly through the increasing use of electronic wallets (e-wallets) among Generation Z. As digital natives, this generation is highly exposed to online transactions, yet their financial management capabilities remain varied. This study aims to analyze the effect of e-wallet usage on personal financial stability, specifically measured through personal cash flow and net worth ratio. Additionally, technological adaptation patterns within modern student financial activities significantly increase complexity, influencing how digital tools are utilized. A quantitative survey method was employed, involving 30 respondents who are active e-wallet users and university students in Pamekasan, Madura. Data were collected through a structured questionnaire and tested for reliability, yielding a Cronbach’s Alpha value of 0.761, indicating acceptable internal consistency. The Kolmogorov–Smirnov normality test showed that some variables met the normal distribution criteria. Results of multiple linear regression revealed that the intensity of e-wallet use, perceived usefulness, and perceived financial impact did not have a significant effect on cash flow, with a significance value greater than 0.05. The model’s R Square value of 0.087 further suggests that only 8.7% of changes in cash flow can be explained by the examined variables, while the remaining 91.3% is influenced by factors such as income level, spending behavior, and financial literacy. These findings indicate that although e-wallets have become an integral part of students’ daily transactions, their impact on overall financial stability remains limited. Strengthening digital financial literacy is recommended to promote wiser and more responsible e-wallet usage.
Analysis of the Effectiveness of Manual Deployment and CI/CD Github Actions in the Braisee Application Seputra, Nenda Alfadil; Nurdiawan, Odi; Dikananda, Arif Rinaldi; Pratama, Denni; Kurnia, Dian Ade
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1916

Abstract

In the modern cloud-based software development ecosystem, the speed and reliability of the deployment process are critical elements. This study aims to evaluate the effectiveness of implementing Continuous Integration/Continuous Deployment (CI/CD) using GitHub Actions compared to manual methods for the machine learning API of the Braisee application hosted on Google Cloud Run. Using a quantitative approach with a comparative experimental design across ten testing iterations, this research measures deployment time efficiency, error rates, and system stability. The experimental results show a significant performance disparity, where the automated method based on GitHub Actions is considerably more efficient, with an average total duration of 111–167 seconds, reducing operational time by 40–60% compared to the manual method, which requires 297–364 seconds. In terms of reliability, the automated method achieves a 100% success rate with high consistency, whereas the manual method demonstrates substantial vulnerability to human errors such as mistyped project IDs and inconsistent image tagging. It is concluded that implementing CI/CD through GitHub Actions is a superior solution that improves time efficiency and ensures the stability of cloud-based applications compared to manual procedures.
Application of Machine Learning in Predicting FIFA World Cup Matches Zulfikar Ismaya Ramadhani; Syaifudin; Beldi Sahfitda; Seprianata Kusuma; Ardiyansyah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1918

Abstract

Football is one of the world’s most widely followed sports, making it an appealing subject for predictive analytics using modern data technologies. This study aims to build a predictive model for international football match outcomes by applying the CRISP-DM methodology as the analytical framework. The dataset used is international_matches.csv covering the period 1993–2022, which underwent a series of preprocessing steps including data cleaning, feature engineering, encoding, imputation, and scaling. Several machine learning algorithms were evaluated, namely Logistic Regression, Random Forest, and HistGradientBoostingClassifier (HistGBM). The best model was obtained using the optimized HistGBM, which demonstrated superior capability in identifying home-team victories, achieving a Recall of 78%. This high sensitivity indicates that comparative features—such as rank difference and squad strength disparity across goalkeeper, defense, midfield, and attack attributes—play a crucial role in predicting dominant match outcomes. The trained model was subsequently deployed into an interactive Streamlit-based web application that enables users to input match-related information and obtain real-time predictions. Overall, this study shows that machine learning methods can be effectively utilized to support data-driven analysis of international football match outcomes.

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