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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 430 Documents
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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.
Geographic Information System for Data Collection of Cooperatives and Small Medium Enterprises in East Sumba Sasqia Adinda Amin; Fajar Hariadi2s; Erwianta Gustial Radjah
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.v4i2.874

Abstract

Cooperatives and Small and Medium Enterprises (SMEs) are important pillars in the regional economy, with the Cooperatives and SMEs Office playing a crucial role in its development. East Sumba Regency has great potential, but the lack of comprehensive spatial data hinders the making of appropriate policies. This research aims to develop a Geographic Information System (GIS) for data collection of Cooperatives and SMEs in East Sumba Regency. The research method used is waterfall, with data collection through interviews, observations, and documentation. The results are in the form of maps and information on the distribution of Cooperatives and SMEs. It is hoped that this GIS can assist the Agency and Regional Governments in formulating policies that are right on target, as well as supporting training and funding. The positive impacts of GIS include improved data accuracy and quality, transparency, and accountability in the development of Cooperatives and SMEs. The System Usability Scale (SUS) method provides a positive picture of user satisfaction, with an average score of 90 indicating the "acceptable" category and an "excellent" rating. This score reflects the effectiveness and ease of use of the system. Overall, this geographic information system has succeeded in supporting the efficient data collection of Cooperatives and SMEs.
Use of Natural Language Processing in Social Media Text Analysis Badry Ali Mustofa; Wawan Laksito Yuly Saptomo
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.v4i2.875

Abstract

Social media generates enormous volumes of text data, creating both opportunities and challenges for analysis. Natural Language Processing (NLP) enables in-depth analysis of public opinion, identification of trends and language patterns from social media texts. However, texts from social media often face problems with informal language, slang, and spelling errors. This research discusses the application of NLP techniques, such as sentiment analysis, tokenization, and text classification, and compares classical machine learning models (Naive Bayes and SVM) with deep learning models (BERT). Results show deep learning-based models excel at understanding informal language contexts, producing more accurate analysis. This study makes an important contribution in the development of AI-based applications for social media analysis.
Improving the Voter List Clustering Model Fixed(DPT) using the K-Means Algorithm in Girinata Village Rizki Aldi; Nana Suarna2; 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.v4i2.876

Abstract

Elections are one of the pillars of democracy that require accurate voter data to ensure transparency and fairness. The Permanent Voter List (DPT) is a crucial element in supporting the smooth running of elections, but there are often data validity problems such as duplicate data, voter location errors, or voter data that does not meet the requirements. This research focuses on the application of the K-Means algorithm to increase the accuracy and validity of the DPT at TPS 05, Girinata Village. The problem formulation in this research includes the accuracy level of the DPT, the effectiveness of the K-Means algorithm in identifying inaccuracies, as well as factors that influence the accuracy of voter data. This research aims to analyze the accuracy level of the DPT, evaluate the effectiveness of the K-Means algorithm in grouping data, and identify factors contributing to the validity of the DPT. The analysis results show that the K-Means algorithm succeeded in grouping voter data with good quality, with a Davies-Bouldin Index (DBI) value of 0.389, which indicates clearly defined clusters. The main factors that influence clustering are age, distance to TPS, and location (RT and TPS). This research shows that the K-Means algorithm can be used to detect inaccuracies in voter data, such as data that does not match the TPS location or age that does not meet the requirements as a voter. With these results, the K-Means algorithm makes a significant contribution to validating voter data, thereby supporting a more transparent and accountable election process.
Identify Rattan Sales Patterns Using the FP-Growth Algorithm on CV. Busaeri Rattan Robi; 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.v4i2.877

Abstract

This research was conducted to recognize the pattern of purchasing rattan products at CV. Busaeri Rattan by utilizing the FP-Growth algorithm. The rattan industry is faced with the challenge of understanding consumer habits in order to improve marketing strategies. The FP-Growth algorithm was chosen for its ability to efficiently identify frequent itemset patterns without requiring a lot of memory. This research includes collecting rattan sales transaction data for one year, data preprocessing, FP-Tree structure formation, and frequent itemset analysis. The analysis was conducted using RapidMiner software with a minimum support setting of 0.005 and confidence of 0.1. The processed data was then used to find combinations of products that are often purchased together. The results revealed some significant patterns, such as the products “Mandola 3/4” and “Jawit 8/11,” which are often purchased together with a confidence level of 100%. These findings provide important insights for CV. Busaeri Rattan in increasing sales through promotional strategies such as bundling or discount offers. In addition, the FP-Growth algorithm proved to be faster and more resource-efficient than traditional methods such as Apriori. The discussion shows that the discovered purchasing patterns can help CV. Busaeri Rattan better manage stock, minimize the risk of running out of goods, and design data-driven marketing strategies. The combination of products that are often purchased together can be utilized to improve customer satisfaction as well as operational efficiency. The conclusion of this research is that the FP-Growth algorithm is an effective tool for analyzing large-scale transaction data. Further research is recommended to explore the application of this algorithm to other types of products or compare it with other data mining algorithms.

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