Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Embedded Systems, Security and Intelligent Systems Jurnal Pendidikan dan Profesi Keguruan
Mustari Lamada
Universitas Negeri Makassar, Indonesia
Computer Science & IT Education Mathematics Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Project-Based Learning Implementation in Improving X Grade Student Colaborative Skills of Computer Network Enginering Murniati Murniati; Mustari Lamada; Firdaus Firdaus
Jurnal Pendidikan dan Profesi Keguruan Vol. 5 No. 1 (2025): Jurnal Pendidikan dan Profesi Keguruan
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/progresif.v5i1.9663

Abstract

This study aims to determine the results and effects of the implementation of project-based learning on the collaborative abilities of class X students majoring in Computer Network Engineering at SMK 3 Enrekang. This study is a classroom action research conducted in two cycles. Each cycle consists of four stages including the planning stage, implementation stage, observation stage, and reflection stage. The subjects in this study were 19 class X TKJ students. The data collection techniques used were observation, questionnaires, and documentation. Based on the results, it showed an increase in the implementation of learning from 74% in cycle I to 91.46% in cycle II, with a difference in increase of 17.46%. Collaborative abilities also increased, with an average observation from 54.04% to 72.81% with a difference in increase of 18.77%, and a questionnaire from 55.20% to 91.46% with a difference in increase of 36.26%. The results of this study indicate that the project-based learning model is effective in improving student collaboration
Transfer Learning-Based CNN for Guava Fruit Disease Detection and Classification Azir Zuldani Pratama; Mustari Lamada; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10153

Abstract

Guava (Psidium guajava L.) is a tropical plant from the Myrtaceae family and the Psidium genus that is susceptible to diseases such as anthracnose and scab, especially in humid environmental conditions. To accurately detect and classify these diseases, digital image-based technology is needed. However, previous studies still have limitations in dataset size, method variation, and model optimization. Therefore, a study was conducted with the title Guava Fruit Disease Detection and Classification System Using a Convolutional Neural Network (CNN) Based Transfer Learning Model. This study tested four Transfer Learning models, namely MobileNetV2, DenseNet169, VGG16, and EfficientNetV2B5. Based on the test results, the MobileNetV2 model with a combination of activation functions and optimizers (Swish, Swish, Adam) showed the best performance, having the fastest computation time, namely 10 minutes 17 seconds. This proves that the model built is not only superior in accuracy, but also efficient in execution time and can be applied to guava fruit disease detection and classification systems. These findings provide valuable insights into the MobileNetV2 method, combined with Swish, Swish, and Adam, as the best choice for classifying or detecting guava fruit disease levels compared to other methods. This approach can also lead to the development of a widely applicable web-based system for plant disease identification. This offers several benefits for farmers, including faster and more accurate disease detection, efficiency, and cost savings.
Co-Authors Azir Zuldani Pratama Dewi Fatmarani Surianto Firdaus Firdaus Murniati Murniati