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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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People Counting in Sample Video Footage Using CNN Integrated with YOLOv5 Ahmad Hasan Faqih Aulia; Carissa Fathinah Balti; Keisyah Zahra Anatasya; Gema Parasti Mindara; Endang Purnama Giri
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.v5i2.1933

Abstract

Accurate people counting in dynamic environments remains challenging due to variations in lighting, complex backgrounds, and occlusion. This study proposes a video-based people counting system leveraging a Convolutional Neural Network (CNN) integrated with the YOLOv5 object detection model. The system applies a structured preprocessing pipeline, including frame extraction, normalization, and noise reduction, to enhance data consistency before detection. The model was evaluated using ten real-world campus video sequences to assess detection reliability and counting accuracy. Experimental results demonstrate that the proposed method achieves high precision and recall for real-time detection across diverse scenarios. Performance degradation was observed in frames containing dense crowds or low illumination, indicating limitations under extreme conditions. These findings validate the feasibility of lightweight CNN-based detectors for surveillance and monitoring applications, while highlighting the need for larger datasets and optimized training strategies to improve robustness in more complex environments.
Development of a Complaint Application for the Education Agency Using the Agile Development Method Sinta Tumbo; Medi Hermanto Tinambunan
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.v5i2.1935

Abstract

The rapid advancement of information technology has driven changes in performance and problem solving in society and government agencies. In the education sector, the Education Office, as the main service provider, requires an effective complaint mechanism for students, teachers, employees, and the community. The current complaint method still often relies on face-to-face submission, which causes problems such as difficulty in tracking the status of complaints, poor documentation, and limitations in evaluating the quality of complaint handling. This study aims to develop a web-based complaint application to digitize and harmonize the complaint process at the Minahasa Regency Education Office. The Agile Development method was used due to its iterative, flexible, and collaborative nature, allowing for the gradual development of features based on direct feedback from stakeholders. Data collection techniques included observation, interviews, documentation studies, and literature studies. The system was designed using UML diagrams, including Use Case, Activity, Sequence, and Class diagrams. Development was carried out in sprints, focusing on core features: user registration with NIK verification, complaint submission and tracking, and an admin dashboard for complaint management. Functional testing using the Black-Box method confirmed that all key features operate correctly as required. The resulting application successfully transformed the manual complaint process into a more structured, transparent, and efficient digital system, thereby contributing to improved public service quality in the field of education.
Implementation of IoT and Machine Learning for Monitoring and Prediction of Tank Water Levels Rizky Wahyudi; Dedy Kiswanto; Windy Aulia; Selfi Audy Priscilia
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.v5i2.1936

Abstract

The availability and quality of clean water in household storage tanks are essential yet often overlooked until problems such as depletion or contamination occur. Manual monitoring methods that rely on physical inspection tend to be inefficient, prone to delay, and unable to support predictive decision-making. This study proposes an automated monitoring solution by integrating Internet of Things (IoT) technology with Machine Learning-based analysis. The system is developed using an ESP32 microcontroller that continuously collects real-time data from an ultrasonic sensor to measure water level and a turbidity sensor to assess water clarity. The time-series data obtained is then analyzed using two algorithmic approaches. Linear Regression is employed to model the water depletion rate and generate predictions regarding the estimated remaining duration before the tank reaches an empty state. In parallel, Random Forest is applied as a comparative model to validate prediction accuracy under non-linear consumption patterns. Experimental results demonstrate that the combined IoT–Machine Learning framework provides accurate, timely, and informative insights for users. The proposed system improves water usage efficiency and strengthens early warning capabilities, making it a practical solution for supporting effective household water management.
Classification of Program Keluarga Harapan Assistance Recipients Using a Website-Based Support Vector Machine Algorithm (Case Study: Panyabungan Kota Subdistrict) Khoirul Ahyar
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.v5i2.1941

Abstract

Program Keluarga Harapan (PKH) is a social assistance program aimed at reducing poverty by providing financial aid to eligible families. This research focuses on the development and implementation of the Support Vector Machine (SVM) algorithm to classify PKH recipients in Panyabungan Kota Subdistrict, Mandailing Natal Destrict. The classification process utilizes factors such as family income, number of family members, and the presence of elderly members. These three factors are chosen due to their availability from public records, ensuring the privacy of participants. The classification model developed in this study is implemented in a web-based system built with PHP and JavaScript, designed to facilitate the automatic classification of PKH recipients. This system helps streamline the registration to be more precise and effective, providing an efficient solution for local government officials to identify eligible families for the PKH program. The evaluation results show that this system can classify PKH recipients well with an accuracy of 93%, offering an automated approach that supports decision-making in the distribution of social assistance.
Comparison of Random Forest and K-Nearest Neighbors in Heart Disease Prediction Erni; Ibnu Alfarobi; 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 Jogi Purba; Dedy Kiswanto; John Bush Henrydunan; Revidamurti Dly
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.

Page 40 of 53 | Total Record : 524

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