PADANG FOOD IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN)

Nabilah Putri Permana; Syafri Arlis

doi:10.33480/pilar.v21i2.7388

Penulis

Nabilah Putri Permana Universitas Putra Indonesia “YPTK” Padang
Syafri Arlis Universitas Putra Indonesia “YPTK” Padang

DOI:

https://doi.org/10.33480/pilar.v21i2.7388

Kata Kunci:

convolutional neural network, deep learning, image classification, image recognition, Padang food

Abstrak

The recognition of Padang traditional foods presents a challenge because of their high visual similarity, which makes manual classification difficult. This study aims to develop an automatic image classification model for Padang foods using the Convolutional Neural Network (CNN) algorithm. The dataset consisted of 1350 images across nine classes of Padang dishes including omelet, chili egg, cow tendon curry, stuffed intestine curry, fish curry, dendeng batokok, rendang, ayam pop, and fried chicken. The CNN architecture was trained for twenty epochs and evaluated using accuracy, loss, confusion matrix, and testing with new images. The results show that the model reached a final training accuracy of 70.2 percent and a validation accuracy of 65 percent, while testing with unseen images produced correct predictions with moderate confidence levels. These findings suggest that CNN is effective for classifying Padang traditional foods and can be applied in culinary promotion, digital food catalogs, and technology based ordering platforms.

Unduhan

Data unduhan belum tersedia.

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