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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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Optimizing the Execution Time of JOIN Queries and Subqueries Using MySQL Yahya, Muhammad Hamdi; Satriaji; Gathan; Zaki
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.v5i2.1872

Abstract

Relational database systems form the backbone of modern information management. However, the escalating volumes of data and increasing complexity of queries present substantial performance challenges in data retrieval operations. This study investigates the execution time differences between Subqueries and five join methods: Inner Join, Left Join, Right Join, AsOf Join and Lateral Join, in MySQL environments. An experimental methodology was employed, utilising two simulated relational tables containing 100, 1,000, and 10,000 rows of data. Each query method was executed three times under identical system conditions to establish reliable average execution times. The findings demonstrate that join operations substantially outperform subqueries across all tested datasets. Inner Join, Left Join and Right Join maintained execution times below 0.04 seconds, even with the most extensive dataset. Conversely, subqueries exhibited significant performance degradation, with execution times increasing to tens of seconds as the data volume increased. This performance disparity stems from the iterative processing inherent to subqueries, which intensifies proportionally with dataset scale, whereas join operations leverage more efficient simultaneous data processing and merging algorithms. The research concludes that join methods constitute the more appropriate choice for medium to large-scale data scenarios, offering practical optimisation guidance for database developers and administrators implementing MySQL-based systems.
IoT Application in Cashier Systems to Help People with Disabilities (Deaf) Muhammad Agung Nugroho
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.v5i2.1875

Abstract

The rapid development of the Internet of Things (IoT) has brought significant impact across various aspects of human life, including information systems and public services. One of its important applications lies in supporting inclusivity for people with disabilities. This research focuses on the implementation of IoT in a cashier system specifically designed to assist individuals with hearing impairments in conducting payment transactions more easily, independently, and equally. In conventional cashier systems, most transaction information is delivered through audio signals, which creates a barrier for hearing-impaired users in fully understanding the payment process. To address this issue, this study develops and implements a prototype of an IoT-based cashier system that utilizes visual notifications and digital indicators as the main medium for delivering transaction information.The results of testing indicate that the IoT-based cashier system functions effectively in delivering transaction information, reducing communication errors, and improving the independence of hearing-impaired users during the payment process. Therefore, this research contributes not only to the development of modern cashier systems but also to the advancement of inclusive and accessible technology that supports equal opportunities for all members of society.
Optimization of Convolutional Neural Networks Using Resizing Techniques for Banana Leaf Disease Classification Kurniawan, Aldiyansyah; Purnamasari, Ade Irma; Pratama, Denni; Tohidi, Edi; Wahyudin, Edi
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.v5i2.1876

Abstract

Early and accurate identification of banana leaf diseases is essential for supporting digital agriculture, as visual symptoms often require rapid and reliable analysis. This study investigates the impact of three image resizing techniques squashing, letterboxing, and random resized crop on the performance of the MobileNetV2 architecture in classifying four categories of banana leaf images using the Banana Leaf Disease Dataset v4 consisting of 4,675 samples. The experiments were conducted using a transfer learning approach with an 80:10:10 data split, standardized normalization, and data augmentation. The results show that all resizing techniques achieved test accuracies above 92%. Squashing produced the highest accuracy and fastest training time, letterboxing demonstrated the most stable performance with the lowest validation loss, and random resized crop improved generalization to variations in object position. These findings confirm that resizing strategies significantly influence the stability and effectiveness of CNN models. Overall, MobileNetV2 proves capable of delivering accurate and efficient classification of banana leaf diseases when supported by an appropriate preprocessing pipeline. This study provides empirical evidence for developing image-based plant disease diagnosis systems within smart agriculture.
Comparison of Logistic Regression and XGBoost Model Performance in Predicting Credit Scores Surianto, Stacyana Jesika; Chairunisah
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.v5i2.1877

Abstract

Credit Scoring is a mathematical approach used to assess the creditworthiness of individuals or companies by classifying debtors into certain categories based on their risk profiles. This study aims to compare the performance of the Logistic Regression and XGBoost machine learning algorithms in predicting credit scores (credit scoring) to reduce the risk of Non-Performing Loan (NPL) risk at PT Graha Mazindo Mandiri. The secondary dataset used contains 1,533 car loan debtor data with 17 variables, including 1dependent variable and 16 independent variables. The research process includes data preprocessing (cleaning, handling outliers, encoding, normalization, and class balancing with SMOTE), modeling, and evaluation using the Accuracy, Precision, Recall, F1-score, and ROC-AUC metrics. The results show that XGBoost excels with 96% accuracy and ROC-AUC of 0.99 compared to Logistic Regression with an accuracy of 88% and ROC-AUC0.94, due to XGBoost ability to capture non-linear patterns and handle data imbalance. This study provides insights into credit risk factors and supports more accurate credit decision-making, with recommendations for hyperparameter optimization and model integration into operational systems.
Sentiment Analysis of TikTok User Comments on The Free Nutritious Meal Program Using Support Vector Machine Lina Nur Afifah; Sri Rahayu; 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.v5i2.1879

Abstract

This study aims to analyze user sentiment when leaving comments on TikTok about the Free Nutritious Food Program (MBG) to understand how the public views the program. Comment data was obtained through online collection and then divided into three groups: positive, negative, and neutral. Before further processing, the data went through a text cleaning and stemming stage to reduce word variation. The data was then represented using the TF-IDF method before being classified with a Support Vector Machine algorithm. The evaluation results showed that using stemming provided more accurate results than without using stemming, thereby improving the model's ability to recognize sentiments contained in comments using informal language. Additional analysis using word clouds, n-grams, and topic modeling provided an overview of words and issues frequently appearing in public discussions regarding the program.
Automatic Bell Using Esp8266 and Telegram Method as a Reminder for Laboratory Time at the AMIKOM Purwokerto University Assistant Forum Aulia Suryaning Tyas; Putri, Refida; 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.v5i2.1880

Abstract

The purpose of this research is to create an automatic bell system that uses an ESP8266 microcontroller integrated with Telegram as a reminder for practical sessions at the Amikom Purwokerto University Assistant Forum. This system is necessary because assistants need to balance laboratory responsibilities and academic activities. Using an Internet of Things-based approach, this system combines NodeMCU ESP8266, DS3231 Real-Time Clock (RTC) module, buzzer, and Telegram Bot notification service. The research process includes identifying needs, reviewing literature, designing the system, implementing, and testing. The bell operates automatically according to the schedule stored in the RTC, while the Telegram bot sends reminders 15 minutes before the practicum begins. Test results show that the bell consistently activates at the right time without delay, and that Telegram notifications are sent according to the configured schedule. These results indicate that the proposed system can meet the functional requirements for accuracy, reliability, and effective communication. Potential for further development in this system includes integration with an automatic attendance feature.
Sentiment Analysis of “Cek Bansos” Application Reviews on Google Play Store Using the Naïve Bayes Algorithm Aini, NoviFirda; Nurdiawan, Odi; Suprapti, Tati; Dikananda, Arif Rinaldi; 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; Purnamasari, Ade Irma; Pratama, Denni; Marta, Puji Pramudya; Wijaya, Yudhistira Arie
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.

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