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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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K-Means Algorithm for Clustering High-Achieving Student at Madrasah Tsanawiyah Yami Waled Muhammad Hilman; Martanto; Arif Rinaldi Dikananda; Ahmad Rifai
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.v4i3.771

Abstract

This study aims to apply the K-Means algorithm to cluster students based on their mathematics grades at Madrasah Tsanawiyah Islamiyyah Yami Waled. By categorizing students into clusters of low, medium, and high academic achievement, the institution can develop more effective and targeted learning strategies. The data consisted of semester mathematics grades from 112 students, analyzed using the K-Means clustering algorithm. Clusters were evaluated using the Davies-Bouldin Index (DBI), with results showing three distinct clusters: Cluster 0 (low achievers, 54 students), Cluster 1 (medium achievers, 37 students), and Cluster 2 (high achievers, 21 students). The DBI score of 0.893 indicates good clustering quality, providing valuable insights for personalized learning approaches.
Optimization of Kebaya Product Grouping Using K-Means Algorithm for Marketing Strategy of Rental Services at Gifaattire Store Nuraeni; Martanto; Arif Rinaldi Dikananda; Ahmad Rifai
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.v4i3.776

Abstract

This study aims to implement the K-Means algorithm to improve the kebaya clustering model to support the rental marketing strategy at Gifaattire Store. The K-Means algorithm was used to analyze eight months of historical kebaya rental data, focusing on the attributes of kebaya type and color. Using the Knowledge Discovery in Database (KDD) approach, the research conducted data selection, preprocessing, transformation, data mining, and evaluation of clustering results. Davies-Bouldin Index (DBI) was utilized to assess the quality of clustering, resulting in an optimal value of 6 clusters with a DBI of 0.580. The results showed that each cluster has unique characteristics that reflect customer demand patterns. Cluster 0, the largest cluster, indicates kebayas with high demand but limited color variations. In contrast, Cluster 1 indicates kebayas with a wide variety of colors but specific demand. This information enables Gifaattire Store to design more targeted data-driven marketing strategies and improve stock management efficiency. The research contributes to the development of literature on the application of K-Means in the fashion rental sector and offers practical insights into understanding customer preferences.
Application of K-Means for Product Grouping Best Sellers at Planet Tire Jatibarang Branch Risnawati; Rini Astuti; Willy Prihartono
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.v4i2.845

Abstract

This research aims to identify the best-selling products at Planet Tire Workshop Jatibarang Branch using the K-Means Clustering method. Understanding product sales patterns is important in designing effective marketing strategies and managing stock efficiently. This research uses sales transaction data for one year, including the number of sales, product types, and total transaction value. The analysis process includes data preprocessing, selection of relevant attributes, application of the K-Means algorithm, and validation of the optimal number of clusters with the Elbow method. As a result, products were grouped into three categories: high, medium, and low sales. The high sales cluster contributes significantly to revenue, while the medium sales cluster shows potential for improvement through promotion, and the low sales cluster requires further evaluation. This research helps management manage stock, prioritize promotions, and optimize resource allocation. However, the research has limitations as it has not considered external factors such as seasonal trends and promotions, and focuses on one branch. Development of the research in other branches can expand its benefits. The results of this study are expected to improve operational efficiency, support data-driven strategies, and enrich academic literature related to the application of K-Means in retail management and sales data analysis.
Optimizing the Social Assistance Recipient Model in CangkringVillage Using the Naïve Bayes Algorithm Rotika; Nana Suarna; Irfan Ali; Dendy Indriya Efendi
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.v4i2.849

Abstract

Social assistance is one of the methods used by the government to help the underprivileged. Cangkring Village is a village in Cirebon Regency that has inaccurate data on recipients of social assistance or underprivileged people. The Naive Bayes algorithm is one of the most effective techniques in machine learning for classifying data, in determining the eligibility of recipients of social assistance. The method works with a probabilistic approach to analyze data efficiently and accurately, can group data based on attributes and produce high accuracy. The problem in Cangkring Village, namely the accuracy of data on recipients of social assistance, is still a problem that requires special attention. This inaccuracy not only reduces the effectiveness of social assistance programs but also creates injustice for people in need. Invalid and inappropriate data causes the distribution of social assistance to be suboptimal. The purpose of this study is to optimize the accuracy model of social security recipients using the Naive Bayes algorithm, which can help improve the accuracy in determining eligible recipients.The method used in the study is secondary data processing taken from social assistance recipient data in Cangkring Village. This process includes data preprocessing stages, training and testing data distribution, and implementation or application of the Naive Bayes algorithm to perform classification. The results of the study show that the Naive Bayes algorithm is able to increase the accuracy of the classification of social assistance recipients with an accuracy rate of 90%, compared to the conventional method used previously. This study contributes to providing a more efficient and targeted method in selecting social assistance recipients, so that it can improve the social assistance distribution system in the future. Thus, the Naive Bayes algorithm can be an effective method for data-based decision making in the context of social policy.
Book Detection System At Bogor Library Using Teachable Machine Eka Kusuma Pratama; Mohamad Ridwan Apriyadi
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.v4i2.852

Abstract

This research aims to develop a book detection system at the Bogor Library using Teachable Machine technology, focusing on improving efficiency in automatically searching and identifying books. The system is designed to replace the barcode-based search method, which is considered less flexible, especially since users often experience difficulties in returning books to their original places after reading. Through the application of machine learning, users can detect books based on their cover images with high accuracy, without needing to adjust the barcode’s position. This research involves collecting book data by capturing images from various angles to train the machine learning model. The developed model was tested under various conditions, with results showing detection accuracy above 80%, meeting the research targets. The application was developed using Flutter, with an interface designed to facilitate users in accessing book scanning and search features. The test results show that the system can detect books with high accuracy and provide information about the location of books in the library, such as shelf numbers and floors. This system is expected to improve the efficiency of book management in the library and assist users in finding and returning books to the correct location.
Information System Audit on Employee Claim Application System Using COBIT 5 Framework Syaifur Rahmatullah Abdul Rojak; Irmawati
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.v4i2.855

Abstract

PT ABC President Indonesia is a manufacturing company engaged in consumer goods, producing instant noodle and beverages packaged in ready-to-drink bottles with green tea and milk tea as the main ingredients. Sales promotions tailored to customer needs are expected to help achieve the company's goal of obtaining the best profits. Sales promotions such as giving rewards or discounts to customers with applicable terms and conditions. In the business process, the company has implemented information systems, one of which is the employee claim application menu advance, a system used for submitting payment claims for reward purchases. In implementation, advance submission from the user department is connected to the accounting and treasury departments. However, there are still shortcomings in the submission input process because supporting documents are still sent manually outside the system. There are no definite provisions regarding the completeness of the required documents to expedite the advance submission process, and there is no report menu that can be generated from the system. Based on this, an information system audit of the employee claim application system is needed using the COBIT 5 framework as the compatible method. Based on the results of the research, the current capability process values are as follows: DSS01 at 3.53, which is at level 4; DSS02 at 3.58, which is at level 4; MEA01 at 3.76, which is at level 4; and MEA02 at 3.40, which is at level 3. Therefore, the researcher provides recommendations for the selected processes so that the vision, mission, and objectives can be achieved according to the expected capability levels.
Implementation of Gamification in Data Processing with Statistical Visualization for using Google Cloud for Monitoring Performance and Improvement Evaluation of Personnel at PT. Surveyor Indonesia Fajri Arvandi; Pariyadi
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.v4i2.857

Abstract

Efficient data management is a crucial requirement for large companies like PT. Surveyor Indonesia to enhance performance and make strategic decisions. This research aims to develop an interactive web-based system with a gamification concept, utilizing technologies such as Laravel, MySQL, and Google Cloud Compute Engine. The system is designed to visualize personnel performance data, provide statistical information, and motivate employees through the implementation of a leveling and points system. By leveraging Google Cloud, the developed system is capable of offering high scalability, flexibility, and ease of access. This research is expected to significantly contribute to the operational data management at PT. Surveyor Indonesia and serve as a model for implementing similar systems for other needs.
A Smart Control System Model for Pharmaceutical and Medical Equipment Storage Using Fuzzy Logic and IoT Didi Rahmat Saputra; Fadhila Azzahra; Lahuddin; Ade Octaviansyah
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.v4i2.866

Abstract

The primary goal of this study is to develop an innovative Smart Control System designed to maintain optimal temperature and humidity levels within a medical storage environment. By integrating Fuzzy Logic with Internet of Things (IoT) technology, we aim to enhance/optimize environmental control. Our experimental approach involved constructing an IoT-based prototype utilizing an Arduino Uno board equipped with high-precision temperature and humidity sensors, and DHT22 components for automated temperature and humidity stabilization. A fuzzy logic algorithm was employed/utilized to analyze real-time sensor data and generate/produce adaptive control outputs in response to environmental fluctuations. This smart control system is expected to significantly enhance/make a significant contribution to medical inventory management by reducing product damage and ensuring the safety of medical supplies. This research paves the way for future advancements in applying advanced technology for environmental control in healthcare settingsKeywords: Smart Control System, Fuzzy Logic, Internet Of Things (IoT)
Clustering Analysis of Administrative Service Types Using K-Means (Study Case: Village bojongsalam) Wafiq Azizah; Ade Irma Purnamasari; Agus Bahtiar; Kaslani
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.v4i2.867

Abstract

Advances in information technology present significant opportunities for the improvement of public services, especially in relation to the administrative functions of Bojongsalam Village. Reliance on traditional methods often leads to inefficiencies and inaccuracies in administrative processes. This research uses the K-Means algorithm to categorize administrative service data based on service type, document number, printing date, and accompanying remarks. Utilizing the Knowledge Discovery in Databases (KDD) framework, the analysis includes data selection, pre-processing, transformation, and clustering analysis conducted through RapidMiner software. The dataset consisted of 718 administrative records that had undergone a rigorous cleaning process, including attribute normalization. The analysis resulted in an optimal Davies-Bouldin Index (DBI) value of -0.498 at K = 4, with each cluster representing a different service utilization pattern. The issuance of Family Cards (KK) and Birth Certificates showed higher demand compared to other available services. This classification promotes workload optimization, fair resource allocation, and formulation of effective operational strategies. The application of the K-Means algorithm demonstrated its effectiveness in data clustering and made a significant contribution to technology-based administrative management. The findings lay a basic framework for addressing the needs of the community in a timely manner.
Implementation of Logistic Regression Algorithm in Predicting Tsunami Potential on Earthquake Data Parameters Sofian Wira Hadi; Ibnu Alfarobi; Irmawati
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.v4i2.871

Abstract

This study presents the evaluation and testing of a logistic regression model for predicting earthquake-related features, including earthquake depth, magnitude, and tsunami potential. The model achieved high accuracy in predicting earthquake depth categories (99.82%) and earthquake magnitude (99.84%), but faced challenges with low recall for tsunami prediction (50%) due to class imbalance. Evaluation results showed that the model struggled to predict tsunami occurrence accurately, as the dataset contained a disproportionate number of 'no tsunami' instances. Despite these limitations, the model displayed high accuracy for earthquake depth and magnitude predictions. The testing phase revealed a series of prediction errors, particularly for the tsunami category, influenced by the imbalance in training data. The results emphasize the need for improved handling of imbalanced datasets and the potential for exploring other machine learning algorithms and techniques for better performance in multiclass classification problems. Future research could further refine these models by incorporating additional criteria and exploring other earthquake and tsunami prediction methodologies.

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