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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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Mitigating Imbalanced Citrus Disease Image Datasets with Oversampling Arya Gunawan; Nana Suarna; Agus Bahtiar; Indra Wiguna Marthanu; Kaslani
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.v5i2.1862

Abstract

Dataset imbalance is a critical challenge in plant disease image classification because it causes bias towards the majority class. This study evaluates the effectiveness of augmentation-based oversampling techniques on the classification performance of citrus leaf images using the MobileNetV2 architecture. The four leaf disease classes classified include Greening, Fresh, Canker, and Blackspot. The dataset was obtained from a public repository and processed through preprocessing (resize, normalization) and augmentation (rotation, flipping, zoom) stages. The model was trained and tested in two scenarios: baseline (unbalanced data) and mitigation (data balanced through augmentation). The experimental results show that the mitigation approach was able to increase accuracy from 91.92% to 93.94%. The F1-score, precision, and recall values also increased significantly, especially in the minority class. Evaluation using a confusion matrix reinforced the finding that augmentation-based oversampling is effective in reducing classification errors. This study shows that the integration of augmentation techniques and MobileNetV2-based transfer learning can significantly improve classification performance and contribute to the development of early detection systems for plant diseases in precision agriculture.
Segmentation of Coffee Purchasing Behavior Based on Transaction Time Using the K-Means Algorithm Yuslia Devitri; Nining Rahaningsih; Irfan Ali; Willy Prihartono
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.v5i2.1863

Abstract

This studyaims to identify customer behavior patterns based on the time of purchaseof beverages at a coffee shop using the K-Means method.Transaction data includes purchase time, payment type, product name,time category, day, and month. The research stages include data cleaning, time attribute transformation, and numerical feature normalization. The optimal number of clustersis determined through testing k = 2–10 with four evaluation metrics,namely Inertia, Silhouette Score, Davies–Bouldin Index, and Calinski–HarabaszIndex. Based on the validation results, k = 3 was selected because it provided the best balancebetween compactness and cluster separation. The clustering results showedthree main customer groups based on transaction time trends:nighttime buyers with a peak around 10:27 p.m., afternoon to early evening buyerswith a centroid of 7:01 p.m., and morning to noon buyers with a centroid11:13. The frequency distribution indicates that the morning–afternoon buyer groupis the largest, while the early evening–night group is thesmallest. Visualization of scatter plots, boxplots, and time category graphsemphasizes the differences in characteristics between clusters. Overall,this study proves that K-Means is effective in mapping the temporal patternsof customer behavior. These findings can be used to develop time-based marketing strategies, operational arrangements, and product stock management,as well as form the basis for further analysis in the industry.
Comparative Analysis of Durian Leaf Disease Classification Using Transfer Learning VGG16, InceptionV3, and U-Net Nafisa Maysa Salma; Rudi Kurniawan; Bani Nurhakim; Agus Bahtiar; Riri Narasati
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.v5i2.1864

Abstract

Image-based durian leaf disease detection presents challenges due to high visual similarity among symptoms and the limited, imbalanced dataset. This study compares three deep learning architectures VGG16, InceptionV3, and U-Net encoder-based—using transfer learning for classifying five durian leaf conditions. The dataset of 4,437 images underwent preprocessing, augmentation, and preliminary segmentation using U-Net to enhance focus on leaf regions. Fine-tuning was applied to the upper layers of each model to adapt feature representations to tropical leaf characteristics. The results indicate that InceptionV3 achieved the most stable and accurate performance with an accuracy of approximately 0.66, while VGG16 showed balanced results but was more prone to overfitting. U-Net proved effective for segmentation but less optimal as a classifier due to loss of small-scale lesion details. Overall, the findings demonstrate that combining U-Net segmentation with CNN-based transfer learning improves disease identification performance, particularly under limited data conditions.
Comparison of Graph-Based Filtering and Non-Local Means Techniques in Diabetic Retinopathy Classification Gita Antar Wulan; Bambang Irawan; Ahmad Faqih; Aris Pratama Putra; Bani Nurhakim
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.v5i2.1869

Abstract

Classification of diabetic retinopathy (DR) based on retinal images is important for early detection, but is often hampered by poor image quality such as noise, uneven lighting, and low contrast. This study analyzes the effect of applying three image filtering techniques, namely Graph Laplacian Filtering (GLF), Graph Convolutional Network (GCN), and Non-Local Means (NLM), on improving the performance of Diabetic Retinopathy classification. The three methods were compared with a baseline model without filtering using VGG16 and evaluated through accuracy, AUC, loss, and image quality metrics such as PSNR, SSIM, MSE, and RMSE.The results showed that graphical and spatial filtering did not always improve classification performance, as VGG16 Fine-Tuning without filtering achieved the highest accuracy of 97.84%. Combinations with NLM, GCN, and Graph Laplacian resulted in lower accuracy due to the smoothing effect that removed important microfeatures on the retina. However, NLM remained effective in reducing noise without disturbing edge structures. These findings confirm that improving image visual quality does not always correlate with CNN accuracy, so preprocessing must focus on preserving diagnostic features.  
Optimizing the Execution Time of JOIN Queries and Subqueries Using MySQL Muhammad Hamdi Yahya; 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 Aldiyansyah Kurniawan; Ade Irma Purnamasari; Denni Pratama; Edi Tohidi; Edi Wahyudin
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 Stacyana Jesika Surianto; 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; Refida Putri; 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.

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