KLASIFIKASI CITRA KUE TRADISIONAL MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

PUTRA, INDRA JULIANSYAH and Rini, Dian Palupi and Rizqie, M. Qurhanul (2025) KLASIFIKASI CITRA KUE TRADISIONAL MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Traditional cake image classification aims to recognize various types of cakes based on visual features, thereby assisting in the effort to introduce traditional cakes. Indonesian traditional cakes hold significant cultural value that must be preserved while also presenting substantial potential in supporting tourism through culinary tourism. This study develops a classification model based on Convolutional Neural Networks (CNN) to identify eight types of Indonesian traditional cakes namely Dadar Gulung, Kastengel, Klepon, Lapis, Lumpur, Risoles, Serabi, and Putri Salju. The dataset was sourced from Kaggle and further enriched by utilizing the Google Custom Search API. The research stages included image preprocessing such as cropping and resizing, splitting the dataset into training (70%), validation (15%), and testing (15%) sets, and augmenting the data to increase variability. The model was trained using two CNN architectures, Xception and VGG-19, with 24 fine-tuning scenarios involving various combinations of batch size, learning rate, and layer settings. The results showed that Xception outperformed VGG-19 in all metrics. Xception achieved the best performance with a configuration of unfreezing layers, a learning rate of 0,0001, and a batch size of 64, resulting in an accuracy of 97,38%, precision of 97,4%, recall of 97,38%, and F1-score of 97,38%. Meanwhile, VGG- 19 achieved its best performance with unfrozen layers, a learning rate of 0.00001, and a batch size of 32, yielding 96.46% accuracy, 96.48% precision, 96.46% recall, and 96.44% F1-score.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kue tradisional Indonesia, Convolutional Neural Network, Xception, VGG-19, Klasifikasi Citra
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Indra Juliansyah Putra
Date Deposited:	24 Mar 2025 04:12
Last Modified:	24 Mar 2025 04:12
URI:	http://repository.unsri.ac.id/id/eprint/169996

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