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Journal Of Artificial Intelligence And Software Engineering
Published by Politeknik Negeri Lhokseumawe
ISSN : 2797054X     EISSN : 2777001X     DOI : http://dx.doi.org/10.30811/jaise
Core Subject : Science,
Computer Science & IT
Artificial Intelligence Natural Language Processing Computer Vision Robotics and Navigation Systems Decision Support System Implementation of Algorithms Expert System Data Mining Enterprise Architecture Design & Management Software & Networking Engineering IoT
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 215 Documents
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A Classification Model of Children’s Digital Device Dependency Based on the Learning Vector Quantization (LVQ) Algorithm Urva, Gellysa; Nazir, Refdinal
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8244

Abstract

Digital device dependency among children has become a critical issue in the modern era, influencing cognitive, social, and health aspects. Excessive use of digital devices may lead to decreased concentration, academic performance, and social interaction. The identification of children's digital dependency levels has often relied on manual observation by parents or teachers, which tends to be subjective. Therefore, this study aims to develop a classification model for children's digital device dependency using the Learning Vector Quantization (LVQ) algorithm. The data were collected through a questionnaire distributed to 110 respondents, consisting of parents of elementary school students in Dumai City. The questionnaire contained 34 items measured using a five-point Likert scale (1–5). The data were processed using Python with supporting libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, and Neupy. The experimental results showed that the LVQ algorithm successfully classified children's dependency levels into three categories low, moderate, and high with an accuracy of 87.5%, an average precision of 85.4%, and an average recall of 86.2%. The findings revealed that most children belong to the moderate dependency category, with an average score of 3.03. The main factors influencing digital dependency include usage duration, habits of using devices while eating or before sleeping, and decreased social interaction. The application of the LVQ algorithm proved effective in identifying children’s digital usage patterns and can serve as a foundation for developing early detection systems and promoting digital literacy policies within elementary education environments
Klasifikasi Citra Bakteri Tuberkulosis pada Sampel Sputum Menggunakan Metode Backpropagation Neural Network Munawir, Munawir; Halimah, Nur; Akram, Rizalul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8399

Abstract

Tuberculosis (TB) is a disease caused by the bacterium Mycobacterium tuberculosis, which was discovered by Robert Koch in 1882. The bacterium is rod-shaped, with a width of 0.3–0.6 μm and a length of 1–4 μm. It is transmitted through the air, for example, when an infected person coughs or sneezes. TB diagnosis is typically performed through microscopic analysis of sputum samples. TB is a serious infectious disease and remains a global health concern. Rapid and accurate diagnosis is crucial for effective treatment, yet conventional methods are often time-consuming and less precise. This study developed a TB bacterial image classification system for sputum samples using a Backpropagation Neural Network (BPNN). The system differentiates between single and clustered bacteria using length, endpoints, and branching features. The dataset consisted of 120 images, divided into 60 training and 60 testing samples. All images were processed using preprocessing techniques to enhance image quality. The length, endpoints, and branching features were extracted from the images and used as input to the BPNN. The results showed that the BPNN method could classify TB bacterial images with an accuracy of 86%. The system was also able to distinguish single and clustered bacteria more accurately, potentially contributing to improved TB diagnosis.
Implementation of Server Up-Scaling to Improve the Performance and Reliability of the ZIS Information System Azzahari, Muhammad; Safriadi, Safriadi; Putra, Arwin; Ridha, Risky; Rozaliana, Rozaliana
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8725

Abstract

The management of zakat, infaq, and shadaqah (ZIS) using information systems requires reliable server infrastructure to ensure system performance and service availability. The ZIS Information System experienced performance degradation and service instability due to increasing user access and system activities. This study aims to implement server up-scaling to improve the performance and reliability of the ZIS Information System. The research method employed is Research and Development (RD) using a prototyping approach. Server up-scaling was implemented by increasing server resource capacity, including memory and processor, without modifying the existing system architecture. System testing was conducted using black box testing to verify functional correctness, along with performance testing based on response time and system downtime before and after up-scaling. The results show that server up-scaling significantly reduces response time and eliminates system downtime during the testing period. Therefore, server up-scaling is proven to be an effective approach for improving the performance and reliability of web-based ZIS information systems.
Implementation of Website-Based Graduate Learning Outcomes Measurement System Erdiansyah, Umri; Syahputra, Guntur; Rudi, Fachri Yanuar
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8584

Abstract

Digital transformation in higher education quality assurance has become imperative following the enactment of the Minister of Education, Culture, Research, and Technology Regulation No. 53 of 2023. This study proposes the development of a web-based Graduate Learning Outcomes (GLO) measurement system to accelerate academic evaluation effectiveness at Politeknik Negeri Lhokseumawe. Employing a Research and Development (RD) approach with the Waterfall development model, this research designs the system architecture using the Model-View-Controller (MVC) pattern to ensure application scalability and modularity. The system integrates academic data management features, automated GLO calculations, and analytical data visualization to support data-driven decision-making. System testing involved User Acceptance Testing (UAT), yielding a System Usability Scale (SUS) score of 82.3, indicating an 'excellent' user acceptance level. The results confirm that this platform not only meets national regulatory compliance standards but also enhances transparency and objectivity in graduate quality reporting. This implementation makes a strategic contribution to the modernization of institutional academic governance.
Analisis Perbandingan Kinerja Algoritma You Only Look Once (YOLOv8) Dan Single Shot Detector (SSD) dalam Pengenalan Nominal Uang Kertas Ulfah, Julia; Ula, Munirul; Fajriana, Fajriana; Nurdin, Nurdin
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.7471

Abstract

The advancement of technology in the field of image recognition has significantly facilitated and improved the effectiveness of object detection in computer-based banknote recognition systems. This study aims to automatically identify banknotes based on their denominations, with the objective of minimizing human errors—such as lack of concentration, fatigue, and other factors—and enabling its application in ATMs and automated payment systems. This research compares the accuracy levels and detection success rates between the YOLO and SSD algorithms in recognizing the denominations of banknotes. The YOLO model operates by dividing the image into grids and predicting bounding boxes along with object classes in a single step, resulting in fast and consistent detection. In contrast, the SSD model employs a multi-scale approach by utilizing feature maps from multiple levels to generate predictions. The parameters used in this study include 7 classes of Indonesian banknotes: Rp1,000, Rp2,000, Rp5,000, Rp10,000, Rp20,000, Rp50,000, and Rp100,000. A total of 353 images were used in the dataset, and three images from each class were selected for testing purposes. The results of the study indicate a significant performance difference. The YOLO algorithm achieved a 100% accuracy rate under both normal and low-light conditions, while the SSD algorithm achieved an accuracy rate of 87.2% under normal lighting and 91.4% under low-light conditions.

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