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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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Sentiment Analysis of “Cek Bansos” Application Reviews on Google Play Store Using the Naïve Bayes Algorithm NoviFirda Aini; Odi Nurdiawan; Tati Suprapti; Arif Rinaldi Dikananda; Fathurrohman
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.v5i2.1883

Abstract

The rapid development of digital public services requires a deeper understanding of user perceptions and experiences regarding government applications, including Cek Bansos. This study aims to identify the polarity of user reviews by applying the Multinomial Naïve Bayes algorithm to review data collected from the Google Play Store. The methodology includes text preprocessing, sentiment labeling, feature extraction using TF–IDF, and model training and evaluation based on accuracy, precision, recall, and F1-score. The results show that the model achieves an accuracy of 79.5%, with very high performance in the negative class (recall 0.97) but poor performance in the neutral class due to data imbalance. The dominance of negative sentiment in the dataset indicates that users face significant technical difficulties, particularly in registration, verification, and service access. These findings demonstrate that Multinomial Naïve Bayes is effective as a baseline model for sentiment analysis; however, improving data balance and quality is necessary to produce a more stable, accurate, and representative model for evaluating digital public services.
Application of Weighted Loss Function in Convolutional Neural Network for Acne Image Classification Abubakar Sidik; Ade Irma Purnamasari; Denni Pratama; Puji Pramudya Marta; Yudhistira Arie Wijaya
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.v5i2.1885

Abstract

Automated acne image classification using Convolutional Neural Networks (CNN) holds significant potential in dermatological diagnosis but faces a fundamental challenge of class imbalance. This phenomenon causes standard models to be biased towards majority classes and fail to recognize clinically important minority classes. This study aims to address this bias by applying a Weighted Loss Function to the EfficientNetB1 architecture. The research method employs a comparative experimental approach between two scenarios: the Baseline model (Standard Cross-Entropy) and the Proposed model (Weighted Cross-Entropy). The dataset consists of 5 acne classes with an imbalanced distribution. The results show that the Weighted Loss model significantly outperforms the Baseline model. Overall accuracy increased from 80% to 86%. The most significant improvement occurred in the minority class 'Papules', where the F1-Score surged by 0.10 points (from 0.71 to 0.81). It is concluded that the application of Weighted Loss Function effectively overcomes bias due to imbalanced data without the need for synthetic data augmentation, resulting in a fairer and more reliable model for clinical implementation.
Influence of AI Technology on the Development of Critical Thinking Skills in Education Fahmy Syahputra; Elsa Sabrina; Alya Rahmi; Ariyantika Br Ginting; Hanifah Mardhiyah; Hutri Ami; Laili Tanzila
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.v5i2.1891

Abstract

The development of Artificial Intelligence (AI) technology has brought significant changes to the educational process, especially in supporting the development of critical thinking skills in students. This study uses a qualitative approach with a systematic literature review method to analyze various findings regarding the influence of AI in education. The results of the study show that AI has the potential to improve critical thinking skills through the provision of analytical stimuli, adaptive feedback, and the facilitation of active learning that encourages reflection, evaluation, and data-based argumentation. However, excessive use of AI or use without teacher guidance can lead to dependence, reduce creativity, and weaken students' evaluative and independent thinking skills. Therefore, the integration of AI should be balanced with active learning strategies, digital literacy, and pedagogical guidance so that this technology functions as a cognitive partner that strengthens critical thinking processes, rather than as a substitute for students' reasoning.
Prediction of Clean Water Quality Using K-Nearest Neighbor (KNN) and Naïve Bayes at PDAM Kupang City Haliim Wila Supardi; Sumarlin
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.v5i2.1892

Abstract

Kupang City faces significant challenges in providing clean water due to its dry geographical conditions and extreme climate. Although it has various potential water sources such as watersheds and bore wells, clean water distribution remains suboptimal. This study aims to predict clean water quality using two machine learning algorithms, namely K-Nearest Neighbor (KNN) and Naïve Bayes, based on the Water Quality Dataset which includes parameters such as pH, hardness, total dissolved solids, and turbidity. The process involves data preprocessing, algorithm implementation, and model evaluation using classification metrics. The KNN model achieved an accuracy of 56%, with an F1-score of 0.67 for the “unsafe” class and 0.36 for the “safe” class. Meanwhile, the Naïve Bayes model achieved a higher overall accuracy of 61% but failed to detect the “safe” class, showing a precision and recall of 0.00. Overall, KNN performed more balanced across classes despite its moderate accuracy, while Naïve Bayes was biased toward the majority class. These findings highlight the importance of selecting appropriate algorithms and tuning parameters for water quality prediction. The implementation of predictive models is expected to assist PDAM Kupang in making data-driven decisions to improve clean water management sustainably.
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 Rian Piarna; 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 Jefri Setyawan
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 Rahman Fadali; Dera Damayanti; Dwi Indah Ria Astari Dwi; Yulia Ilmi Qur'ani Ilmi; Mohammad Raihan Alghifari Istianah; Istianah Asas Raihan
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 Aditya Darusman
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 Ma`dan Shomsomi; Widhaksa Triawan; 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.

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