Klasifikasi Citra Makanan Khas Kota Pasuruan menggunakan Convolutional Neural Network
Keywords:
Classification, Typical Food, Pasuruan City, Image, Deep Learning, Convolutional Neural NetworkAbstract
Typical food is an important part of Indonesia's cultural and social heritage, with each region having its own unique identity and characteristics. These variations are influenced by geographical location, distribution of spices, and local habits, creating distinctive flavors and various serving variations. Several typical dishes from Pasuruan City, such as BipangANGKAr, Botok Tempe, Cenil, and Klepon, are famous among the people. Even though it is familiar to most people, it is sometimes difficult for people to clearly distinguish types of food typical of other regions, especially if there are similarities in form or presentation. In this context, researchers consider it necessary to conduct research related to the image classification of typical food in Pasuruan City. For this purpose, the Convolution Neural Network (CNN) algorithm is used, a type of neural network specifically designed for processing visual or image data. CNNs are effective for object recognition and detection in images, with the ability to achieve high accuracy and satisfactory results. This research includes 1000 data on typical food from Pasuruan City, focusing on four types of food, namely BipangANGKAr, Botok Tempe, Cenil, and Klepon. Through the use of a single Graphical Processing Unit (GPU) in scenario 2, training time was successfully reduced to 335 seconds. The results show that with a configuration of 32 filters in the first convolution, 64 in the second convolution, and 128 in the third convolution, the model is able to produce a lower error value, namely 0.7438, with an accuracy of 100%.
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