Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal JISKa (Jurnal Informatika Sunan Kalijaga)
Mufidatuzzainiya, Agustina
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Penggunaan Teknik Transfer Learning pada Metode CNN untuk Pengenalan Tanaman Bunga Mufidatuzzainiya, Agustina; Faisal, Muhammad
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.195-206

Abstract

This study explores the impact of using the transfer learning method to improve flower recognition performance using Convolutional Neural Network (CNN) models. The dataset used consists of 4242 flower images divided into five classes: daisy, tulip, rose, sunflower, and dandelion. This research implements three models: basic CNN, VGG16, and EfficientNetB3, to test the effectiveness of transfer learning in flower classification. The basic CNN model achieved a training accuracy of 73.38% and validation accuracy of 71.76%, but it is limited in generalizing to new data. The VGG16 model achieved perfect training accuracy but experienced overfitting, with validation accuracy stabilizing around 85-90%. Meanwhile, the EfficientNetB3 model with transfer learning reached a training accuracy of 98.50% and a validation accuracy of 94.00%, demonstrating strong generalization without significant overfitting. The experiment was conducted using data augmentation techniques, and performance evaluation was carried out using accuracy, precision, and recall metrics. The results show that transfer learning with the EfficientNetB3 model provides the best performance in flower classification compared to the basic CNN and VGG16 models. For future research, further development can be done by expanding the types of flower datasets and applying additional optimization techniques to improve accuracy in more complex models.
Co-Authors Muhammad Faisal