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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 525 Documents
 45   46   47   48   49 
Application of K-Means Clustering for Urban Transportation Pattern Analysis Using Big Data Trip Dataset Tegas Ramadhan; Hafizh Ariiq; Muhammad Dzaki Arjun; Muhammad Ridho Ananda Aditya
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.v5i3.2237

Abstract

The rapid growth of urban transportation systems has led to the generation of massive amounts of data, commonly referred to as big data. This study aims to analyze transportation patterns using large-scale data obtained from the NYC Taxi Trip Records. The dataset exhibits key big data characteristics, including volume, velocity, and variety. This research applies the K-Means clustering algorithm to group taxi trip data based on features such as trip distance, fare amount, and trip duration. Several preprocessing techniques are performed, including data cleaning, feature engineering, sampling, and normalization. The optimal number of clusters is determined using the Elbow Method and Silhouette Score. The results show that the dataset can be effectively grouped into three clusters representing distinct transportation patterns. These findings demonstrate the capability of clustering techniques in extracting meaningful insights from large-scale datasets and highlight their potential application in urban transportation planning.
Development of a Web-Based System for Recording and Reporting Palm Weights Using Laravel at PT. Graha Prima Lestari Fredynand Marcos; Wilson; 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.v5i3.2241

Abstract

This research was initiated by operational problems in the palm oil weighing process, which was conducted manually. The manual method often caused calculation errors, delays in making report , and risk of data loss. To address these issues, a web-based palm oil weighing application was developed using the Laravel framework and the Waterfall development method, supported by a relational database to manage data in an integrated manner. The application implements a role-based access system to manage permissions for administrators, weighing operators, and management. The system records gross weight, tare weight, and automatically calculates net weight while generating accurate reports efficiently. With this system, the weighing process is expected to become more efficient , precise and structured.
Designing a Web-Based Financial Information System at GKS Palindi using the Rapid Application Development Method Serlince Pindi Kualak; Arini Aha Pekuwali
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.v5i3.2245

Abstract

In the rapidly evolving information age, technology plays an important role in improving the efficiency of data management, including in church institutions. The Church, as a religious institution with an important role in the spiritual and social life of the people, often faces challenges in financial management, especially in recording congregation donations, operational expenses, and making transparent and accurate financial statements. Good financial management is needed to ensure accountability and transparency, as well as facilitate reporting to the congregation and other related parties. GKS Palindi, a church in Kawangu District with 335 congregations, faces problems in the financial recording system that is still manual using books. This causes data corruption, which risks disrupting the smooth flow of the financial management process. Therefore, this church needs the implementation of a technology-based information system that can facilitate the recording and management of financial transactions efficiently, especially for the six main posts: tithe, thanksgiving, part (household worship), monthly dependents, offerings, and miscellaneous posts. With this system, the church can reduce the potential for human error, monitor cash flow more easily, and provide more accurate and timely financial reports. The right information system can help GKS Palindi in maintaining the continuity of church operations and increasing the congregation's trust in the transparency of financial management. The system development method used is Rapid Application Development (RAD), which allows the creation of a system quickly and responsively to user needs. The implementation of a technology-based recording system is expected to overcome existing problems, as well as support the smooth running of church activities in the long term.
Clusterization of Family Planning Participants Based on Pregnancy Risk Using K-Means Algorithm in Ciherang Village Melva Regina Arpratika; Nana Suarna; Agus Bahtiar; Martanto; Odi Nurdiawan
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.v5i3.2248

Abstract

This study aims to group family planning (KB) participants in Ciherang Village based on pregnancy risk levels using the K-Means clustering algorithm. The identification of pregnancy risk is still performed manually, resulting in less effective analysis. Therefore, a data mining approach is applied to improve decision-making accuracy. The data used in this study were obtained from KB cadres, including variables such as age, number of children, education, occupation, and contraceptive methods. The research method follows the Knowledge Discovery in Database (KDD) stages: data selection, preprocessing, transformation, data mining, and evaluation. The K-Means algorithm is used for clustering, while the Davies–Bouldin Index (DBI) is applied to evaluate clustering quality. The results show that the optimal number of clusters is K = 2 with a DBI value of 0.721. The first cluster represents low pregnancy risk participants, while the second cluster represents high pregnancy risk participants. Age and number of children are identified as the most influential factors. This study provides useful insights for healthcare providers in developing targeted strategies for family planning programs. Keywords: Data Mining; Davies–Bouldin Index; K-Means Clustering; Pregnancy Risk; Family Planning
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.

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