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Machrus Ali
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INDONESIA
Journal Of Electrical Engineering And Technology
Published by Universitas Darul Ulum
ISSN : 29647320     EISSN : 27225321     DOI : 10.32492/jeetech.v7i1
Core Subject :
Jurnal JEETech: Journal Of Electrical Engineering And Technology. Journal of Darul Ulum University Jombang in collaboration with the Faculty of Engineering, Darul Ulum University Jombang with the Indonesian Electrical Engineering Higher Education Forum (FORTEI). Region VII. East Java. This journal contains research results in the fields of Power Engineering, Telecommunication Engineering, Computer Engineering, Control and Computer Systems, Electronics, Information Technology, Informatics, Data and Software Engineering, Biomedical Engineering. All submitted articles must report original, previously unpublished, experimental or theoretical research results that are not published and are being considered for publication elsewhere.
Arjuna Subject : -
Articles 105 Documents
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Implementation YOLOv5 algorithm for Detection Digital Image - Based Banana Diseases Anis Yusrotun Nadhiroh; Moh Jasri; Wali Ja’far Shudiq
Jurnal JEETech Vol. 7 No. 1 (2026): May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v7i1.7113

Abstract

Banana is one of the tropical fruit commodities with high economic value, but it is vulnerable to diseases such as anthracnose (spot), crown rot, and fruit rot, which negatively affect yield and fruit quality. The manual detection method, which is still commonly used, has limitations in terms of accuracy and efficiency. Therefore, this study aims to develop a banana disease detection system based on digital images using the You Only Look Once version 5 (YOLOv5) method. This research applies a quantitative experimental approach with a dataset consisting of 1,005 images that were labeled using the Roboflow platform. The training process was carried out in Google Colaboratory with four epoch configurations, namely 20, 50, 80, and 100. Model performance was evaluated using accuracy, precision, recall, F1-score, and mean Average Precision (mAP), as well as confusion matrix visualization. The best training results at 50 epochs achieved an average mAP-50 of 0.817%. The final results of this study demonstrate that YOLOv5 is effective in automatically and accurately detecting banana diseases. The web-based implementation provides added value in terms of accessibility and ease of use. The study recommends further development with a larger dataset and the utilization of mobile applications to support field implementation in real time.
TAM, Emotional Engagement, and Trust on AR Virtual Try-On Behavioral Intention in Shopee Cahya Ade Ningrum; Muawan Bisri; Fathoni Dwiatmoko
Jurnal JEETech Vol. 7 No. 1 (2026): May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v7i1.7114

Abstract

The rapid development of digital shopping encourages e-commerce platforms  to continue to innovate through the application of immersive technology, one of which is  the Augmented Reality (AR) Virtual Try-On (VTO) feature that facilitates consumers in simulating products virtually before purchasing decisions are made. This study was designed to investigate the contribution of Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Emotional Engagement (EE), and Trust (TR) to the Behavioral Intention (BI) of AR VTO feature users in Shopee, Indonesia. The study adopted a quantitative approach through surveying 500 active Shopee users who had used the feature at least once, with data analysis using the Structural Equation Modeling-Partial Least Squares (SEM-PLS) method assisted by SmartPLS 4. All instruments were declared valid and reliable based on the results of the outer model evaluation. The four variables were simultaneously proven to have a positive and significant influence on BI. The novelty of this research lies in the simultaneous integration of the cognitive dimensions of TAM, affective, and trust in a single analytical framework applied specifically to the context of Shopee's AR VTO in the Indonesian market, an approach that has not been explored much in the literature before. The research findings have practical implications for Shopee developers in designing a more intuitive, emotionally responsive, and data-secure AR interface, to encourage wider adoption of technology among Indonesian digital consumers.
Integrasi WhatsApp Gateway dan Cloud Computing pada Sistem Absensi Digital Terpadu Siswa (SAGITA) Gustiawan Maula Rizki; Fathoni Dwiatmoko; Agus Komarudin
Jurnal JEETech Vol. 7 No. 1 (2026): May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v7i1.7111

Abstract

The integration of Information and Communication Technology (ICT) in vocational high schools demands agile and accurate administrative governance. Manual attendance monitoring often collides with data accuracy issues and recapitulation inefficiency. This study aimed to design, develop, and evaluate the Integrated Student Digital Attendance System (SAGITA) utilizing a cloud computing architecture at SMK NU Kaplongan, accommodating 1,186 students across 38 study groups. The system integrated the Google Workspace ecosystem (Google Forms and Sheets) with Google Apps Script (GAS) and a local WhatsApp Gateway API for real-time information broadcasting. Employing an applied research methodology with a prototyping approach, the evaluation was conducted through functional testing (Black-box) and administrative impact testing (Pre-test/Post-test). The results showed that the system successfully sent notifications with an average latency of 3-5 seconds. The administrative evaluation indicated a measurable efficiency in recapitulation time and a decrease in reporting delays. The system's continuity relies heavily on physical supporting infrastructure, including the availability of a Virtual Private Server (VPS), a Public IP, and an Uninterruptible Power Supply (UPS). This low-code model offers a cost-effective alternative administrative solution for large-scale educational institutions
K-Means Clustering for Primary Education Inequality in Serang City Ari Sopiandi; Muawan Bisri; Eko Aziz Apriadi; Agus Komarudin
Jurnal JEETech Vol. 7 No. 1 (2026): May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v7i1.7115

Abstract

Educational inequality remains a persistent issue in various regions, including Serang City. Differences in access to and quality of education across areas are influenced by several factors such as the number of schools, students, and teaching staff. However, available educational data are generally presented only in the form of descriptive statistics, which are not sufficient to provide in-depth insights into the patterns of inequality. Therefore, a computational-based approach is needed to analyze educational data more effectively. This study aims to analyze trends in educational development and map educational inequality in Serang City using a data mining approach with the K-Means algorithm. The data used in this study consist of educational data obtained from the Serang City Education Office, Dapodik, and BPS, including the number of schools, the number of students, and the number of teaching staff. The analysis process is carried out through data preprocessing, normalization, and data clustering using the K-Means algorithm with the help of RapidMiner software. The results show that educational data can be grouped into several clusters representing the level of educational conditions across regions. Each cluster has different characteristics, which can be used to identify areas with high and low levels of educational inequality. Thus, the data mining approach is able to provide a more systematic overview of educational conditions and support data-driven decision-making
Prototipe AI-IoT Edge Berbasis Raspberry Pi dan TinyML untuk Pemantauan Jaringan Kampus secara Real-Time Bima Aulia Firmandani; F Yudi Limpraptono; Michael Ardhita; Machrus Ali
Jurnal JEETech Vol. 7 No. 1 (2026): May
Publisher : Universitas Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32492/jeetech.v7i1.7112

Abstract

Complex campus networks featuring server-based services and the growing Internet of Things (IoT) require near-real-time monitoring systems without incurring significant overhead. This study proposes a lightweight Artificial Intelligence-Internet of Things (AI-IoT)-based network monitoring prototype on an edge computing platform, utilizing an unsupervised autoencoder for anomaly detection. This prototype is implemented out-of-band on a Raspberry Pi 4 Model B device that serves as both a collection and inference node. The deep learning model on the TensorFlow Lite framework is compressed using TinyML for compatibility with small devices. The results use a dataset of 600,000 labeled flows that illustrate the trade-off in operational flexibility. At the P70 threshold, an F1-Score of 0.60 (precision 0.96, recall 0.43) is obtained, and in the P95 scenario, false positives can be completely eliminated. The edge infrastructure demonstrated excellent performance with an average batch processing latency of 74 ms and a throughput of over 300 flows/second with a constant Random Access Memory (RAM) usage of 2.8%.

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