Padang Cuisine Classification using Deep Convolutional Neural Networks (CNN) and Transfer Learning

Abstract

Padang cuisine, as an integral part of Indonesia's culinary wealth, challenges researchers to explore its potential through technological approaches. This article explores the application of Deep Convolutional Neural Networks (DCNN) and Transfer Learning for the classification of Padang cuisine. DCNN is used to understand and recognize unique visual patterns in food images, while Transfer Learning leverages existing knowledge to improve the model's performance in the specific task of classifying Padang cuisine. This process involves collecting a representative dataset, data preprocessing, and model training to create a system capable of accurately identifying various Padang dishes. Experimental results show that this approach achieves an accuracy rate of 92%, demonstrating its effectiveness in classifying different types of Padang cuisine. By engaging advanced technology, this article contributes to a deeper understanding of the integration between traditional culinary richness and innovation in the modern world of artificial intelligence. These findings indicate that AI implementation can be used to document and celebrate diverse culinary heritage, as well as support the culinary industry in managing and identifying dishes in real-time.