Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Husna, Nayla
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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

Found 1 Documents
Search

 1 
Comparative Study of Convolutional Neural Network Architectures and Optimizers for Flower Image Classification Yulisara, Ekatri; Husna, Nayla; Martin, David; Ariesta, Candrawati
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 2: PREDATECS January 2026
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i2.2110

Abstract

This study aims to comparatively evaluate the performance of different Convolutional Neural Network (CNN) architectures and optimization algorithms for flower image classification. Three widely used CNN architectures DenseNet201, InceptionV3, and MobileNetV2 are implemented using transfer learning with pre-trained ImageNet weights and tested with two optimizers, Adam and RMSProp. The experiments are conducted on the Flowers Recognition dataset consisting of five flower classes: daisy, dandelion, rose, sunflower, and tulip. Image normalization and data augmentation are applied to improve model generalization, while performance is evaluated using accuracy, precision, recall, and F1-score. The main contribution of this study lies in a systematic comparison of CNN architectures and optimizers within a unified experimental framework, which is rarely addressed in previous studies. The results show that DenseNet201 combined with the Adam optimizer achieves the highest classification accuracy of 90%, followed by MobileNetV2 with RMSProp, while InceptionV3 yields the lowest accuracy of 85%. These results confirm that the research objective is achieved, demonstrating that both CNN architecture and optimizer selection significantly influence flower image classification performance.
Co-Authors Ariesta, Candrawati Martin, David Yulisara, Ekatri