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All Journal Journal of Education Technology and Information System
Fauzan Nusyura
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Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Library & Information Science

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Journal : Journal of Education Technology and Information System

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Develop Sinawang Plugin with PjBL in Moodle to Improve Network Administration Competence Romadhon, Muslich Wahyu; Prismana, I Gusti Lanang Putra Eka; Fauzan Nusyura
Journal of Education Technology and Information System Vol. 1 No. 02 (2025): Journal of Education Technology and Information System (JETIS)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jetis.v1i02.35858

Abstract

This research aims to design and implement the Sinawang plugin in Moodle as a learning media that supports the application of Project Based Learning (PjBL) in designing the student learning process. In addition, this research also aims to evaluate the impact of the use of learning media in improving the competence of Network System Administration in class XI TKJ students at SMKN 1 Kediri. The Sinawang plugin design process utilizes PHP, Javascript, HTML, and CSS programming languages with the Research and Development (R&D) development model. The test design used in this research is Developmental Testing. The results of media testing through the black box testing method show that all components function properly (valid). Data from the validation of media, lesson plans, materials, and questions show the level of validity successively 88% (very valid), 83% (very valid), 80% (valid), and 83% (very valid). The analysis results showed that the average score of the cognitive test on the posttest (86.6) was higher than the pretest (52.8). Similarly, the mean value of the psychomotor test on the posttest (85.9) was also higher than the pretest (74.5). Based on the paired sample t-test results, the Significance (2-tailed) value obtained for both tests is 0.000, which is smaller than 0.05. Therefore, in accordance with the basis for decision making in the paired sample t-test, the null hypothesis (H0) is rejected and the alternative hypothesis (H1) is accepted. This indicates that there is an increase in student competence in Network System Administration subjects through the use of the "Sinawang" plugin with the application of Moodle-based Project-Based Learning (PjBL).
Design of an Android Application for Leaf Disease Detection in Plants Muhammad Nizam Setiawan; Ardhini Warih Utami; Fauzan Nusyura
Journal of Education Technology and Information System Vol. 2 No. 01 (2026): Journal of Education Technology and Information System (JETIS)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jetis.v2i01.42208

Abstract

Agriculture plays a strategic role in Indonesia's economy, with approximately 29,342,202 individual agricultural enterprises recorded in 2023, according to Statistics Indonesia (BPS). Golokan Village, located in Sidayu District, Gresik Regency, is one of the agrarian areas where 23.22% of the population works as farmers, and it has a total agricultural land area of 385 hectares. However, between 2019 and 2023, there was a significant decline in the production of three main commodities: corn decreased from 302.5tons to 275.6tons, tomatoes from 810tons to 585 tons, and cassava from 1,000tons to 832tons. One of the contributing factors is the difficulty in early detection of plant diseases. To address this challenge, this study designed and developed an Android application called AgroAI utilizing deep learning technology, specifically a Convolutional Neural Network (CNN) model based on the MobileNet architecture optimized with TensorFlow Lite for mobile devices. The development was carried out using the Scrum methodology in two sprints. The first sprint included needs analysis, dataset collection, interface design, and model training. The second sprint implemented the core features such as leaf disease detection via camera or gallery, classification results with recommended solutions, analysis history management, educational articles, and user authentication via Firebase. Black box testing confirmed that all features functioned as intended, while model validation achieved an accuracy of 94.74%. This application is expected to enhance farmers' efficiency in crop management and support the sustainability of both local and national agricultural sectors.
Co-Authors Ardhini Warih Utami I Gusti Lanang Putra Eka Prismana, I Gusti Lanang Putra Eka Muhammad Nizam Setiawan Romadhon, Muslich Wahyu