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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
 39   40   41   42   43 
Comparison of Random Forest and K-Nearest Neighbors in Heart Disease Prediction Erni; Alfarobi, Ibnu; Wawan Kurniawan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1942

Abstract

Heart disease is one of the leading causes of death worldwide, with a death toll reaching 17.9 million cases annually according to the World Health Organization (WHO) and a prevalence of 1.5% in Indonesia. This high mortality rate demonstrates the importance of early detection and accurate prediction to prevent more serious complications. The development of artificial intelligence technology, particularly machine learning, offers a new approach in the medical field through the ability to analyze clinical data quickly and efficiently. This study was conducted to compare the performance of two machine learning algorithms, namely Random Forest and K-Nearest Neighbors (KNN), in predicting heart disease using a clinical dataset from Kaggle containing 20 samples and 9 attributes related to the patient's physiological condition. The parameter optimization process in both algorithms was carried out using grid search techniques with cross-validation to obtain the best model that can perform optimally on a limited dataset. Performance evaluation was carried out using accuracy, recall, and precision metrics to comprehensively measure the quality of the model predictions. The results of the study showed that the Random Forest algorithm provided superior performance with an accuracy of 0.75, a recall of 0.88, and a precision of 0.86, compared to KNN which only achieved an accuracy of 0.50, a recall of 0.67, and a precision of 0.67. These findings indicate that Random Forest is more effective in identifying the presence of heart disease, especially in terms of sensitivity to positive cases and prediction consistency. Thus, Random Forest has the potential to be a more appropriate algorithm for implementation in machine learning-based clinical decision support systems, to support the process of diagnosing heart disease more accurately and efficiently.
Implementation of Prototyping Method in Developing a Web-Based Cos Management System using Laravel Yudistio Izza Al Farisi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1943

Abstract

This study develops a web-based boarding house management system using the Prototyping method to address administrative issues at Kos D'Rosse, where data was previously managed manually through Google Form, Excel, and WhatsApp. The Prototyping model enabled iterative requirements gathering and user evaluation to refine system features. The system was built using the Laravel framework with an MVC architecture and includes modules for tenant management, room monitoring, payment processing, financial reporting, and a real- time dashboard. Blackbox testing confirmed that all features functioned according to user needs, while whitebox testing produced low Cyclomatic Complexity values, indicating simple and maintainable program logic. User Acceptance Testing (UAT) showed improvements in operational efficiency, data accuracy, and decision-making speed. The results demonstrate that the system integrates all management activities into a single platform, reduces administrative workload, and provides accurate, real-time information. Overall, the Prototyping approach and Laravel MVC support structured development and effective system performance. Keywords: Laravel, Prototyping, Web-based System, Boarding House Management, MVC
Smart Safety Room: ESP32 Decision Tree-Based Multi-Hazard Detection System Purba, Jogi; Kiswanto, Dedy; Henrydunan, John Bush; Dly, Revidamurti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1947

Abstract

Physical space security and safety remain fundamental challenges in various sectors, ranging from residential buildings to critical server rooms. Conventional security systems often rely on single sensors or passive alarms that cannot respond comprehensively to multiple simultaneous threats. This research proposes a Smart Safety Room, an ESP32-based integrated multi-sensor security system that combines gas sensors (MQ-2), fire sensors (flame sensors), PIR sensors, and visual-audio output components including OLED displays, RGB LEDs, and buzzers. The system implements a decision tree algorithm with hierarchical priorities to classify room conditions into three categories: SAFE, ALERT, and DANGER based on a combination of sensor data. Testing was conducted through four main scenarios: normal conditions, fire detection, intrusion detection, and dual threat conditions. The results show that the system achieved an overall accuracy of 96.5% with detailed performance of 96% for the fire sensor, 94% for the gas sensor, and 98% for the PIR sensor. The average response time was under 300 milliseconds for all types of detection, meeting the real-time system requirements. The decision tree showed excellent classification performance with an F1-score ranging from 95-97% for all categories. The web-based real-time monitoring dashboard successfully displayed sensor status with auto-refresh every 1 second and a data loss rate of only 0.8% during continuous operation.
Streamlit Based Network Intrusion Detection System Prototype with Machine Learning Algorithm Tiara Maulida; Muhammad Nandi Buchari; Teofilus Tirta Jumata; Putra Pratama Syahrival; Ali Mustopa
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1950

Abstract

Computer network security has become a crucial elemen in the digital era, with the increasing risk of attacks that could potentially disrupt systems and access critical data. An Intrusion Detection System (IDS) powered by Machine Learning is one effective way to automatically detect suspicious network activity. This study aims to create a prototype of a network Intrusion Detection System using Streamlit that applies Machine Learning algorithms, including Naïve Bayes and Random Forest, to classify normal network activity as an attack. The method used in this study is a quantitative approach with an experimental design utilizing a public dataset of labeled network traffic. The research process includes the stages of initial data processing, feature selection, model creation, performance evaluation, and implementation of the Streamlit interface. Test results show that the Naïve Bayes algorithm has the best performance, with an accuracy level reaching 0.8000, an error rate of 0.2000, and an F1 Score of 0.7273. Random Forest recorded an accuracy level of 0.7333, an error rate of 0.2667, and a lower F1 Score of 0.3333. These findings demonstrate that Naïve Bayes is more effective at detecting intrusions and recognizing anomalous network traffic patterns. The Streamlit based system implementation successfully provides an interactive and userfriendly interface, allowing users to perform analysis and understand classification result without in-depth technical expertise. Given the foregoing, the network intrusion detection system prototype built with Streamlit and a Machine Learning algorithm is considered suitable as a simple, informative, interactive, and efficient network security support tool. This research paves the way for future developments, such as the implementation of Deep Learning models and the integration of live network monitoring.
Analysis of the Causes of Multifilament Thread Defects in PT X Using Seven Tools Nafis, Maulida Durrotun; Joumil Aidil Saifuddin Zuhri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1955

Abstract

PT X, a leading plastic packaging manufacturer in Indonesia, faces the problem of high defect rates in the production of multifilament yarns that hinder output optimization. This research aims to analyze and improve product quality using the Seven Tools method. Data for the May-October 2025 period shows a total production of 214,045.5 kg with an accumulated defect of 5,029 kg. The results of the Pareto analysis identified two main defects (vital few), namely brittle yarn (38%) and easily broken yarn (31%). Analysis via the control map (p-chart) showed the process was in an uncontrolled condition, especially in August which exceeded the upper control limit (UCL 0.0242). Based on the fishbone diagram, the root cause of the problem comes from the instability of the engine temperature (godet), operator negligence, non-standard SOP, and variations in material quality. To overcome this, it is recommended that companies carry out routine machine maintenance (PPM), install automatic temperature monitoring systems, standardize SOP, and hold periodic training for operators to create process stability and minimize defects on an ongoing basis.
Design of a Software Requirements Specification for a Parental Partnership Assistance Management System in Elementary School in Malang Regency Christian Difae Klemens; Meme Susilowati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1958

Abstract

This research is motivated by the need for a more efficient and transparent aid management system in primary education institutions, particularly at SDK Yos Sudarso Kepanjen, where the processes of application, verification, and reporting are still conducted manually. Such conditions lead to various issues, including delayed distribution, data duplication, and difficulties in monitoring assistance. To address these problems, a Software Requirements Specification (SRS) document was designed as a reference for developing a web- and mobile-based Parent Partnership Aid Management Information System. This study employed a system engineering approach consisting of three main phases: analysis, design, and implementation. These phases include problem identification, system workflow redesign, and the development of an initial user interface prototype using HTML and CSS (Bootstrap framework). The results indicate that the SRS document successfully defines the system’s functional and non-functional requirements, including user authentication, aid application, digital verification, automated reporting, and a GPS-based needs mapping feature. It is expected that this SRS document can serve as a guideline for developing collaborative, efficient, and accountable educational information systems in the future.
Cybersecurity Awareness: A Literature Review on Internet Users' Awareness and Safe Behavior Ananda Amalia Putri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1959

Abstract

The rapid development of information technology has facilitated various aspects of human life, from communication and education to financial transactions. However, this progress has also been accompanied by the growing threat of cybercrime, such as data theft, hacking, and digital fraud. One of the most influential factors contributing to this growing threat is the low level of cybersecurity awareness among internet users. This article aims to review the various literature related to cybersecurity awareness and user safety behavior in the online world. The method used is to review literature from various scientific sources from 2022 to 2025 that discuss cybersecurity awareness, behavior, and education. The results of the study show that while security technology continues to evolve, human awareness remains the weakest point in cyber defense. Therefore, improving education and digital culture is a key strategy in developing safe behavior among internet users.
Classification of Spending Segmentation in Mobile Game Applications Using Random Forest and Decision Tree Algorithms Putra Wicaksana, Dewa Restu; Anom, Rangga; Musyarafah, Syahrina; Giatika Chrisnawati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1961

Abstract

This research aims to classify spending segmentation in mobile game users using Random Forest and Decision Tree algorithms. The dataset consists of demographic attributes, gameplay behavior, session frequency, and historical spending records. Several preprocessing steps uwere applied, including missing value handling, label encoding, one-hot encoding, and feature scaling. The data were divided into an 80:20 training-testing ratio, and hyperparameter tuning was performed using GridSearchCV. The results indicate that Random Forest achieved higher accuracy compared to Decision Tree, demonstrating better generalization for multiclass segmentation (Low, Medium, High spenders). This study shows the potential of machine learning in predicting user spending behavior to support data-driven monetization strategies in mobile game applications.
Administrative and Field Risk Analysis in the PGN Sales Division Using the FMEA Method Ainur Zaki Yamami; Sumiati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1963

Abstract

This study analyzes administrative and field operational risks in the Sales Operation Regional (SOR) III of PT Perusahaan Gas Negara (PGN) using the Failure Mode and Effect Analysis (FMEA) method. The objective of this research is to identify potential failure modes in customer service processes and determine risk priorities that may affect service quality and operational safety. Data were collected through observations, interviews, and documentation during an internship period, covering administrative activities and gas pipeline installation processes. The analysis shows that potential failures are concentrated in three main stages: customer data input, data verification at the head office, and gas pipeline installation. The results indicate that the data input stage has the highest risk level, with the loss of customer data forms recording the highest Risk Priority Number (RPN) value. Technical constraints during data verification and safety-related issues during gas installation were also identified, although with relatively lower RPN values. Overall, the application of the FMEA method provides effective insights for prioritizing corrective actions and improving the reliability, safety, and efficiency of natural gas service operations.
Design and Implementation of a Web-Based Currency Converter System Using an Application Programming Interface Purwadi; Augst Nurandini; Gusnaeni Indah Pratiwi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1967

Abstract

This study aims to design and implement a web-based currency converter application that utilizes an Application Programming Interface (API) to provide real-time and accurate exchange rate data. The increasing intensity of global economic activities has created a growing need for fast and reliable currency conversion, while manual conversion methods are prone to errors and data inconsistencies. This research employs the Research and Development (R&D) approach using the waterfall development model, which includes requirement analysis, system design, implementation, testing, and maintenance. The developed application provides two main features: an exchange rate calculator that performs automatic currency conversion based on real-time data, and a currency exchange history feature that presents exchange rate trends in graphical form within a selected period. Testing results indicate that the application runs reliably, delivers fast responses, and consistently displays up-to-date exchange rate information. In conclusion, the proposed application serves as an effective web-based solution for accessing accurate currency exchange information to support international financial activities.

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