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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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Prediction of Peritonitis Infection Risk in CAPD Patients using Random Forest Algorithm Silviani Gustaman
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.v5i3.2249

Abstract

Peritonitis is a serious complication frequently experienced by patients undergoing Continuous Ambulatory Peritoneal Dialysis (CAPD) and may worsen patient outcomes if not detected early. This study aims to develop a machine learning model to predict peritonitis risk using the Random Forest algorithm and to interpret prediction results using Explainable Artificial Intelligence (XAI). The study utilized a secondary dataset obtained from Kaggle consisting of 20,538 clinical records that were transformed to represent CAPD-related clinical parameters. The research stages included data preprocessing, feature selection using SelectKBest (f_classif), dataset splitting into training and testing sets, model development using Random Forest, and performance evaluation using accuracy, precision, recall, F1-score, and Area Under Curve (AUC). Model interpretability was analyzed using SHAP to identify feature contributions. The experimental results demonstrate that the proposed model achieved an accuracy of 98.70%, precision of 98.22%, recall of 99.24%, F1-score of 98.73%, and AUC of 1.00. The findings indicate that Random Forest provides highly reliable predictive performance and interpretable insights into clinical features influencing peritonitis risk. The developed model has potential to support clinical decision-making systems for early detection of peritonitis risk in CAPD patients.
UI/UX Design of Laundry Pick-Up and Delivery Application using Prototyping Method Margaretha Natalia Simamora; Johanes Terang Kita Perangin Angin; Jackri Hendrik
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.v5i3.2253

Abstract

Digital transformation demands operational efficiency in the conventional laundry industry, which is still hampered by manual management and limited geographic reach.In response to this phenomenon, this research focuses on developing a UI/UX design for the Laundry Express mobile application with superior pickup & delivery service features. The main goal is to reduce the potential for data input errors while providing information transparency for users. Through an iterative prototyping method, the design process includes needs identification and continuous evaluation using Figma. The final product, a high-fidelity prototype, integrates order tracking features, automatic cost calculation based on weight, and a service assessment module. Evaluation using Likert scale for usability measurement demonstrated a high level of ease of navigation, allowing users to complete transactions without technical obstacles. This study concludes that the iterative prototyping approach is effective in producing intuitive application designs that meet the needs of modern society who require flexible laundry services.
Analysis of Student Errors in Solving Problems Involving Curved-Surface Geometric Shapes Based on Newman’s Error Analysis floricytha sihombing; Rifki Aidil Fikri; Amelia Putri; Sherlyta; Devina Zuhra Utami; Kairuddin
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.v5i3.2255

Abstract

This study aims to evaluate the errors made by students when solving problems involving three-dimensional shapes with curved sides, using Newman’s error analysis approach. The research employed a descriptive qualitative method and was conducted at Imelda Private Junior High School in Medan during the second semester of the 2025/2026 academic year, with the participation of 18 students selected through purposive sampling. Data collection tools consisted of written tests, interviews, and documentation. Data analysis was conducted by referring to Newman’s five stages of error: reading, comprehension, transformation, process skills, and coding. The research findings indicate that the most common errors were process skill errors, accounting for 25.9%, and transformation errors, accounting for 20.3%, while errors in the reading, comprehension, and coding stages were not identified. Students with low ability typically struggle to find the formula and proceed with the problem-solving process; students with moderate ability tend to make errors during calculations; whereas high-ability students successfully solve problems accurately and in an organized manner. Thus, it can be concluded that most student errors are caused by an inability to select the correct formula and a lack of precision during calculations. Therefore, it is crucial to implement teaching methods that focus on conceptual understanding and procedural skills to minimize the errors students make when tackling mathematical problems.
Decision Support System Using the Analytical Hierarchy Process Method in Determining Credit Recipient Eligibility Erika Nia Devina Br Purba; Arnita; Hermawan Syahputra; Lasker P Sinaga; Adidtya Perdana
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.v5i3.2256

Abstract

Banks play a fundamental role in improving public welfare by collecting funds through savings and redistributing them as credit. Although credit is the primary source of bank revenue, it carries significant risks if the feasibility analysis of prospective borrowers is flawed, potentially leading to non-performing loans that disrupt financial stability. BPR Nusantara Bona Pasogit 17 faces this challenge as it currently lacks an automated decision support system, resulting in assessments that are often inconsistent or subjective. This research aims to develop a web-based decision support system using the Analytical Hierarchy Process (AHP) method to determine credit recipient eligibility. Developed using PHP and MySQL, the system incorporates criteria management, AHP calculation processing, and automated eligibility ranking. Comprehensive validation through black-box and white-box testing confirmed that all functional components performed correctly with consistent "PASS" results. The AHP implementation produced a Consistency Ratio (CR) of 0.03797, indicating high reliability in decision-making. Criterion priority weights were identified as: Income (0.386), Character (0.219), Loan Amount (0.162), Collateral (0.103), Loan Term (0.07), and Age (0.06). System testing on 100 customer records resulted in a maximum eligibility score of 0.93501 and a minimum of 0.41839.
Developing a Web-Based E-Commerce Application for Toko Oleh-Oleh Khas Prabumulih Ivan Mei Dwintara; Fajriah; Phinton Panglipur
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.v5i3.2258

Abstract

Prabumulih Typical Souvenir Shop is a business unit selling various pineapple-based processed products that still faces constraints in promotional reach and manual transaction efficiency. This study aims to design and build a web-based e-commerce information system as a solution for digital marketing and sales. The system development method used is Rapid Application Development (RAD), consisting of requirements planning, user design, construction, and cutover phases. The application was built using PHP programming language and MySQL database. The results show that the application successfully facilitates online transactions, real-time stock management, and automated sales reporting, which significantly enhances the shop's operational efficiency.
Implementation of Convolutional Neural Network for Emergency Sound Detection for Hearing-Impaired Individuals on Android Muhammad Akram Fais; Insan Taufik; Mansur AS; Debi Yandra Niska; Hanna Dewi Marina Hutabarat
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.v5i3.2262

Abstract

Hearing impairment is a condition characterized by partial or total loss of hearing ability, which may occur congenitally or be caused by factors such as injury, disease, or prolonged exposure to excessive noise. This study aims to develop an Android-based emergency sound detection system using the Convolutional Neural Network (CNN) method. The research workflow includes problem identification, data collection, data preprocessing, CNN model training, model evaluation, Android application development, and system testing. Experimental results show that the best-performing model achieved an overall accuracy of 93%. The trained model was then implemented into an Android application to enable real-time sound classification and to provide visual notifications when emergency sounds are detected. The evaluation results indicate that the CNN model is capable of accurately classifying emergency sounds and operates effectively on Android devices.
Eye Disease Classification System Based on Fundus Images Using the InceptionV3 Architecture Annisa Aulia; Hermawan Syahputra; Yulita Molliq Rangkuti; Insan Taufik; Kana Saputra S
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.v5i3.2263

Abstract

This study aims to develop an automated eye disease classification system based on retinal fundus images using the InceptionV3 deep learning architecture. The dataset consists of four classes: cataract, diabetic retinopathy, glaucoma, and normal, collected from public sources and clinical data. The proposed method applies several preprocessing techniques, including background segmentation, data augmentation, data normalization, and an 80:20 data split to improve model performance and generalization. Transfer learning is implemented by utilizing pretrained ImageNet weights and modifying the final layers to suit the classification task. The model is trained using the Adam optimizer with a learning rate of 0.001 and categorical cross-entropy loss function. Evaluation results show that the model achieves an accuracy of 96%, with average precision, recall, and F1-score values of 0.97, 0.96, and 0.97, respectively. The confusion matrix analysis indicates that most predictions are correctly classified, demonstrating strong performance across all classes. Furthermore, the model is successfully integrated into a web-based system that enables users to upload fundus images and obtain classification results automatically. These findings indicate that the proposed system can effectively assist in early detection of eye diseases and support clinical decision-making.
UI/UX Design of an Incoming and Outgoing Mail Information System using the Design Thinking Method Nurhayati; Zulfi Karman; Manja Purnasari
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.v5i3.2264

Abstract

Kantor Inspektorat Kota Jambi saat ini mengelola surat masuk dan keluar secara manual menggunakan buku catatan, yang menyebabkan risiko kehilangan dokumen dan inefisiensi dalam pengambilan data. Penelitian ini bertujuan untuk mendesain Antarmuka Pengguna (UI) dan Pengalaman Pengguna (UX) untuk Sistem Informasi Surat Masuk dan Keluar (SIMAK) yang ramah pengguna untuk meminimalkan kendala operasional tersebut. Metode yang digunakan adalah Design Thinking , yang terdiri dari lima tahapan: empati, definisi, ide, prototipe, dan pengujian. Hasil penelitian ini adalah prototipe aplikasi berbasis web yang dirancang menggunakan Figma, yang menampilkan alat manajemen untuk surat masuk, surat keluar, disposisi, dan laporan. Pengujian yang dilakukan menggunakan metode System Usability Scale (SUS) dengan 10 responden menghasilkan skor rata-rata 89,75 . Skor ini menempatkan desain aplikasi dalam kategori " Diterima " dengan peringkat " Baik ", yang menunjukkan bahwa sistem mudah dipahami dan secara efektif memenuhi kebutuhan pengguna.
Analysis of Mathematics Percentage Calculation Strategies Quickly and Accurately Based on a Literature Review Adi Sinaga; Dinda Alexa Nabila Utomo; Dwi Octa Marcellita Girsang
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.v5i3.2265

Abstract

Percentages are one of the important concepts in mathematics that are widely used in various contexts of daily life such as economics, commerce, and decision-making. However, various studies show that the concept of percentage is still a material that is quite difficult for students and prospective mathematics teachers to understand. These difficulties are generally related to the understanding of the basic concept of percentage, the use of the percent symbol (%), and the ability to relate the percentage value to the reference value in a problem. This study aims to analyze various percentage calculation strategies that can be carried out quickly and accurately based on the results of previous research. The method used in this study is a literature study by reviewing various scientific articles from SINTA-accredited national journals and international journals that are relevant to the topic of percentages in mathematics learning. Data is collected through documentation techniques by examining and analyzing research findings related to the percentage calculation strategy. The results of the study show that the use of mental calculation strategies, understanding the basic concept of percentages, and the use of visual models can help improve students' ability to calculate percentages more effectively and efficiently. Therefore, the right calculation strategy is essential to support the understanding of the concept of percentages in mathematics learning.
Sentiment Analysis of Film Audience for IPAR ADALAH MAUT Using Support Vector Machine Surya Agung Agan Saputra; Siti Mujilahwati; Azza Abidatin Bettaliyah
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.v5i3.2266

Abstract

This study aims to analyze user sentiment on social media X (formerly Twitter) toward the film Ipar Adalah Maut using the Support Vector Machine (SVM) method. The data were collected through a crawling process using the snscrape library, focusing on tweets containing keywords related to the film title. The preprocessing stages included data cleaning, case folding, tokenization, stopword removal, and stemming, while feature extraction was performed using Term Frequency Inverse Document Frequency (TF-IDF). Sentiment was classified into two categories, namely positive and negative, using the SVM algorithm. The results showed that the model achieved 100% accuracy on the training data and 82% accuracy on the testing data, indicating good generalization performance, although there is a potential risk of overfitting due to the gap between training and testing results. These findings demonstrate the effectiveness of SVM in analyzing sentiment related to film discussions on social media and provide a basis for future research by incorporating larger and more balanced datasets.

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