Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN)

Azkiya Nur Azizah; Irwan Budiman; Fatma Indriani; Mohammad Reza Faisal; Rudy Herteno

doi:10.18495/comengapp.v13i2.469

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Affiliations

Azkiya Nur Azizah
Universitas Lambung Mangkurat

Irwan Budiman
Affiliation not stated

Fatma Indriani
Affiliation not stated

Mohammad Reza Faisal
Affiliation not stated

Rudy Herteno
Affiliation not stated

Content Top

Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN)

Azkiya Nur Azizah ,
Irwan Budiman ,
Fatma Indriani ,
Mohammad Reza Faisal ,
Rudy Herteno

Vol 13 No 2 (2024)

DOI 10.18495/comengapp.v13i2.469

Submitted: Feb 29, 2024

Published: Jun 1, 2024

Abstract

Indonesia is one of the countries famous for its traditional culinary. Traditional cakes in Indonesia are traditional snacks typical of the archipelago's culture which have a variety of textures, shapes, colors that vary and some are similar so that there are still many people who do not know the name of the cake from the many types of traditional Indonesian cakes. The problem can be solved by creating a traditional cake image recognition system that can be programmed and trained to classify various types of traditional Indonesian cakes. The Convolutional Neural Network method with the AlexNet architecture model is used in this research to predict various kinds of traditional Indonesian cakes. The dataset used in this research is 1846 datasets with 8 classes of cake images. This study trained the AlexNet model with several optimizers, namely, Adam optimizer, SGD, and RMSprop. The best parameters from the model testing results are at batchsize 16, epoch 50, learning rate 0.01 for SGD optimizer and learning rate 0.001 for Adam and RMSprop optimizers. Each optimizer tested produces different accuracy, precision, recall, and f1_score values. The highest test results that have been carried out on the image dataset of typical Indonesian traditional cakes are obtained by the Adam optimizer with an accuracy value of 79%.