Article Per Year (5 Year)
p-Index From 2020 - 2025
0.92
P-Index
This Author published in this journals
All Journal Proceeding International Conference on Information Technology and Business Al-MAIYYAH : Media Transformasi Gender dalam Paradigma Sosial Keagamaan Bulletin of Computer Science and Electrical Engineering (BCSEE) Journal of Comprehensive Science
Nisar, Nisar
Unknown Affiliation
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Bulletin of Computer Science and Electrical Engineering (BCSEE)

 1 
Enhancing Rose Leaf Disease Detection Accuracy Using Optimized CNN Parameters Nabilah, Latifah; Nisar, Nisar; Amnah, Amnah; Arfida, Septilia
Bulletin of Computer Science and Electrical Engineering Vol. 4 No. 2 (2023): December 2023 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/bcsee.v4i2.1184

Abstract

The CNN model developed in this study demonstrated remarkable performance, achieving an outstanding validation accuracy of 99.96%. Through experimentation, it was found that employing the RMSprop optimizer with a learning rate of 0.001 yielded superior results compared to the Adam optimizer utilized in previous iterations. Additionally, increasing the number of epochs from 10 to 20 resulted in a significant enhancement in accuracy, highlighting the importance of iterative training for model refinement. Moreover, the implementation of Early Stopping proved to be a valuable technique, effectively conserving training time by halting the training process once optimal accuracy levels were reached. These findings underscore the efficacy of various optimization strategies in bolstering the performance of CNN models for rose leaf disease detection. The achieved accuracy rates signify a substantial advancement in disease detection technology, holding promise for enhancing agricultural productivity and ensuring plant quality. This research contributes valuable insights into the optimization of CNN parameters, paving the way for further advancements in automated disease detection systems in the field of agriculture.
Comparison of Tomato Leaf Disease Detection Using Transfer Learning Architecture with the VGG19 Method Amelia, Indah; Nisar, Nisar
Bulletin of Computer Science and Electrical Engineering Vol. 4 No. 2 (2023): December 2023 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/bcsee.v4i2.1185

Abstract

Diseases in plants are often detrimental to agriculture, can be seen manually and require a very long time, which can lead to possible errors in disease detection. Detecting diseases in plants early can overcome these problems and reduce the risk of reduced crop production. The aim of this research is to make a comparison of quickly and accurately detecting tomato leaf diseases compared to previous researchers who used Deep Learning applications. Which can be applied effectively for image classification using the VGG19 method. The implementation of this model uses a dataset containing 2,694 images, including 3 different types of diseases. That the conclusion of this research is the fastest and most accurate way to detect tomato leaf diseases. To prove this research, results and necessary data will be presented in this paper. The accuracy obtained on the VGG-19 architecture was 91.85% with the best increase in accuracy compared to the previous journal which only produced 87% accuracy.
Co-Authors Amelia, Indah amnah amnah Emilia Mustary Hendra Setiawan, Muhammad Mahyuddin Mahyuddin Nabilah, Latifah pratama, rendi Rio Kurniawan Rosandi, Triowali septilia arfida Wijaya Samdoria, Samodra