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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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Decision Support System for Selection of Achieving Students Using MetDecision Support System for Selection of Achieving Students Using Method Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Web Based Isra Pebrianti; Syarifah Putri Agustini Alkadri; Asrul Abdullah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.829

Abstract

The selection of outstanding students identifies the best students based on grades and achievements to recommend them for college entrance. This process often encounters challenges due to numerous determining factors, leading to potential biases in decision-making. A Decision Support System (DSS) helps address this by utilizing data and decision models to resolve structured and unstructured problems. This study applies the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) method, using criteria such as attendance, attitude scores, knowledge and skills component values, extracurricular/organizational involvement, and achievements. The DSS identified 40 outstanding students at SMA Negeri 1 Tayan Hulu, with the highest preference score of 0.0819 achieved by Indah Prasetyaning Tias. Functional testing was conducted using the black-box method with Equivalence Partitioning, and accuracy testing through MAPE showed a calculation accuracy rate of 2.79%.
Association Analysis of Printing and Photocopying Sales Data in Adzmi Art Shop Cirebon Uses the FP-Growth Algorithm Suteja; Rudi Kurniawan; Yudhistira Arie Wijaya
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.830

Abstract

In the digital era, transaction data analysis plays a crucial role in strategic decision-making, especially for SMEs such as Toko Adzmi Art in Cirebon Regency. This study aims to develop a sales data association model using the FP-Growth algorithm to identify product association patterns. Daily transaction data over a year were collected, processed through data cleaning, standardization, and transformation, and analyzed using RapidMiner software. Minimum support and confidence parameters were applied to evaluate the frequency and strength of product relationships. The results show that the combination of "Photocopy" and "Passport Photo" services has a confidence of 0.491 and a support of 0.061, with "Photocopy" as the most in-demand product (support 0.497). These findings open opportunities for bundling strategies and inventory optimization to enhance operational efficiency. This model provides an empirical foundation for SMEs to leverage data mining technology to improve competitiveness and customer satisfaction.
Design and Construction of a Web-Based Outpatient Data Management Information System (Case Study: UPTD Puskesmas Cikampek) Dian Ardiansyah; Nung Hayati; Walim; Dewi Yuliandari; Supriatin; Mareanus Lase
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.833

Abstract

The focus of this research is the design and development of an information system to manage outpatient data at the UPTD Cikampek Health Center which operates via the internet. The purpose of this system is to overcome the problem of inefficient manual recording at the cashier and to improve efficiency and accuracy in managing patient data. A prototype model is used in software development. This model includes steps such as needs analysis, system design, implementation, and testing. The results of this study are in the form of a web-based information system that includes login features, patient data management, payments, reports, and transaction history. It is expected that this system can help exchange information online throughout the scope of the Health Center, accelerate the distribution of reports, and facilitate the management of patient data. Therefore, this study offers an innovative and practical solution to managing health data at first-level health care facilities.
K-Means Algorithm for Grouping Models of Dengue Fever Prone Areas in Cirebon City Aida Safitri; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.834

Abstract

Dengue hemorrhagic fever (DHF) is an infectious disease transmitted through the Aedes aegypti mosquito. DHF cases in Cirebon City show a significant increase every year. This study aims to classify dengue prone areas based on case data per health center in 2020-2024 obtained from the Cirebon City Health Office. The method used is the K-Means algorithm with the Knowledge Discovery in Database (KDD) approach, which includes data selection, preprocessing, data transformation, data mining, evaluation, and knowledge. Evaluation using Davies-Bouldin Index (DBI) showed optimal results at k = 6 with a DBI value of -0.445. The clustering results produced six clusters: cluster 5 (437 dengue cases in 34 health centers) showed high risk; cluster 0 (244 cases), cluster 2 (129 cases), and cluster 3 (279 cases) showed medium risk; while cluster 1 (69 cases) and cluster 4 (86 cases) showed low risk. This study shows that the K-Means algorithm is effective in identifying DHF risk distribution patterns and provides a strategic basis for the Cirebon City Health Office to prioritize interventions and develop more effective prevention strategies.
Geographic Information System for Mapping the Area and Coconut Production in Kendal Regency Aan Kia Asshifa; Bambang Agus Herlambang; Ahmad Khoirul Anam
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.838

Abstract

Coconut fruit holds high economic value as it can be utilized for various products and significantly contributes to the local economy. This research aims to develop a Geographic Information System (GIS) to map land area and coconut production in Kendal Regency in 2023 as a foundation for more efficient resource management. By integrating spatial and non-spatial data from the Central Statistics Agency (BPS), this GIS visualizes the spatial distribution of coconut plantations and identifies production variations among sub-districts. The analysis reveals that Patebon Sub-district has the highest coconut production, while Cepiring Sub-district, despite having the largest land area, records relatively low production. Mapping was conducted using Quantum GIS (QGIS) software, allowing for accurate and efficient data digitization. This GIS output holds significant potential to support spatial planning, data-driven policy formulation, and the improvement of coconut farmers' welfare in Kendal Regency. The implementation of the map on a web-based platform facilitates public access to the presented spatial information.
K-Means Algorithm to Improve Leaf Image Clustering Model for Rice Disease Early Detection Gina Regiana; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.840

Abstract

This research aims to improve the accuracy of rice leaf image clustering in early disease detection using the K-Means algorithm. The approach used involves the Knowledge Discovery in Databases (KDD) method, which includes data selection, pre-processing, data transformation, data mining, evaluation, and presentation of results. The dataset used consists of images of healthy leaves and leaves infected with diseases such as Bacterial Leaf Blight, Brown Spot, and Leaf Smut. The images are processed through grayscale conversion, noise removal, size adjustment, and data augmentation. The K-Means algorithm is applied to cluster image features based on visual similarity. Evaluation results using Silhouette Score showed that the best clustering was obtained at K=2 with a score of 0.8340, resulting in two main clusters separating healthy and infected images. This study concludes that the K-Means algorithm is able to improve the efficiency and accuracy of rice disease detection, so that it can assist farmers in taking early preventive measures and increase agricultural productivity. This implementation shows significant potential in the development of smart agriculture technology.
Optimizing Grocery Sales Data Grouping Using the Fuzzy C-Means Algorithm: Case Study of Nafhan Mart Store Nafhan Khairuddin Fathin; Rudi Kurniawan; Saeful Anwar
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.842

Abstract

The sale of staple food products at Nafhanmart Store, Cirebon Regency, includes essential household items such as rice, cooking oil, sugar, and flour, which maintain stable demand as basic necessities. This study focuses on improving sales clustering models at Nafhanmart using the Fuzzy C-Means (FCM) algorithm, a prominent method in data mining. Key factors influencing sales include price, sales volume, demand, and remaining stock. Accurate clustering analysis is vital for strategic inventory management and profit maximization. The research applies the Knowledge Discovery in Database (KDD) methodology, encompassing data selection, preprocessing, transformation, FCM implementation, and evaluation using the Davies-Bouldin Index (DBI). Attributes analyzed include price, sales volume, demand, and remaining stock. The FCM algorithm clusters data based on patterns, with DBI evaluating clustering quality and determining optimal clusters. Data analysis and visualization were conducted using RapidMiner. Results show that the FCM algorithm achieves optimal clustering quality with a DBI score of 0.452 for two clusters, outperforming three clusters (DBI 0.474) and four clusters (DBI 0.536). Price and demand are identified as critical factors influencing clustering outcomes. These findings enhance the clustering model, offering actionable insights for inventory management and sales strategy, while showcasing the FCM algorithm's adaptability for other SMEs to support data-driven decision-making.
Implementation of Simple Additive Weighting (SAW) Method For Selecting a Tutoring Center Lilis Indrayani; Yuliana Sangka
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.843

Abstract

Abstract Tutoring Institutions (Bimbel) are non-formal educational institutions that work to maximize children's learning potential which may not be fully achieved through conventional education. There are many bilingual educational institutions that exist today, each with its own regulations and requirements designed to attract students, especially elementary school (SD) students. Standards that are often used as a guide to attract students are Tuition fees, distance from home, facilities. and teaching staff. Alternatives to bimbel institutions are Zefanya Bimbel, Study Star Bimbel, Camat Bimbel and Quantum Bimbel. Each bimbel institution has different requirements or policies, it presents a unique challenge for students to choose atutoring place that suits their expectations. One of the methods of problem solving that can be used is by building a computer-based system to help decision-making, the method used is the Simple Additive Weighting Method (SAW), The SAW method can select the best alternatives from several available alternatives because it is ranked after determining the weight of each feature. From this research, getting the results of Bimbel Camat becomes the recommendation of the best choice of bimbel. Keywords: Decision Support System, Simple Additive Weighting, Alternative
Implementation of GridSearchCV to Find the Best Hyperparameter Combination for Classification Model Algorithm in Predicting Water Potability Kurniasih, Aliyah; Previana, Cantika Nur
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.844

Abstract

Drinking water quality is an important factor in public health, so an accurate approach is needed to determine water potability. This research aims to create a water potability prediction model using machine learning methods, with a focus on model accuracy and testing. The dataset used includes various chemical parameters, as well as one radiological and acceptability parameter. In this study, various machine learning algorithms, such as Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression, were applied using GridSearchCV and their performance compared. Models were evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics, with cross-validation to ensure generalizability. The results showed that the Support Vector Machine algorithm provided the best performance with an accuracy of 70.43%, followed by Random Forest and Logistic Regression with accuracies of 70.12% and 62.20%, respectively. The Support Vector Machine-based model is able to provide reliable predictions and can be used as a tool to support decision-making in water quality management.
Improving the Education Development Contribution Payment Model at SMK Istiqomah Maruyung Using the C4.5 Algorithm Noviyanti; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 3 (2025): June 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i3.729

Abstract

  Payment of tuition fees is one of the important aspects of school financial management. At SMK Istiqomah Maruyung, the management of SPP payments is still done manually, which causes student non-compliance in paying on time. The purpose of the research is to improve the SPP payment model by using the C4.5 algorithm to classify the level of student compliance and identify the main factors that influence late payments. The method used is the Knowledge Discovery in Databases (KDD) approach which includes the stages of data selection, preprocessing, transformation, data mining, and result evaluation. The research data was taken from 206 students in the 2023/2024 academic year with attributes such as parental income, number of siblings, scholarship status, and academic grade point average. The C4.5 algorithm was applied to build a decision tree model, with evaluation using five-fold cross validation. The result of this study is that the C4.5 algorithm is able to classify student compliance levels with an average accuracy of 93.55%. The main factors that influence late payment are academic grade point average, class, and parental income. Although the model is very good at predicting compliant students (precision 95%, recall 98%), it shows weakness in predicting lateness (precision 67%, recall 40%). It is concluded that the C4.5 algorithm can improve the efficiency of managing tuition payments and provide data-driven insights for policy making. With further implementation, this algorithm is expected to be adopted by other educational institutions to address similar challenges in financial management.

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