Pengenalan dan Klasifikasi Ragam Kue Indonesia menggunakan Arsitektur ResNet50V2 pada Convolutional Neural Network (CNN)

Iskandar, Jonathan Steven and Kristianto, Ryan Putranda (2023) Pengenalan dan Klasifikasi Ragam Kue Indonesia menggunakan Arsitektur ResNet50V2 pada Convolutional Neural Network (CNN). Seminar Nasional Amikom Surakarta 2023, 1 (1). pp. 81-92. ISSN 3031-5581

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Abstract

In addressing the issue of recognizing and classifying similar types of Indonesian cakes, with a focus on cakes that exhibit high visual resemblance, this research conducted testing of the Convolutional Neural Network (CNN) algorithm using the ResNet50V2 architecture to identify and classify various types of Indonesian cake images. The dataset used was obtained from Kaggle under the name "Indonesian Cakes." This study yielded two significant findings. First, prediction results from uploaded images were presented in the form of probabilities, and kastengel cakes were considered valid if their prediction probabilities exceeded the threshold of 0.6. Second, the accuracy level of the tested algorithm reached 73.19%, which can be considered a commendable accuracy rate. These results indicate that the CNN algorithm with the ResNet50V2 architecture has the potential to be used in the recognition and classification of similar types of Indonesian cakes, contributing to the development of an automated cake type recognition system and aiding in maintaining quality and consistency in the food and culinary industry. This research provides a crucial foundation for the development of image recognition technology that can be applied within the context of Indonesian culinary culture.

Item Type:	Article
Uncontrolled Keywords:	CNN, ResNet50V2, Clarification, Indonesian Cake
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Fakultas Teknik > Prodi Ilmu Informatika
Depositing User:	Ryan Putranda Kristianto
Date Deposited:	30 Sep 2025 08:28
Last Modified:	01 Oct 2025 05:11
URI:	http://repositori.ukdc.ac.id/id/eprint/1792

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