Implementation of Transfer Learning in CNN for Classification of Nut Type

Authors

  • Insidini Fawwaz Universitas Prima Indonesia
  • Jimmy Deardo Sagala Universitas Prima Indonesia
  • Reivaldo Kevin Febriawan Sijabat Universitas Prima Indonesia
  • Novita Marissa Maringga Universitas Prima Indonesia

DOI:

10.33395/sinkron.v8i4.12784

Keywords:

Transfer Learning, Convolutional Neural Network, Nut Classification, Inception V3, Xception

Abstract

Nut has a high nutritional value and is widely used as an ingredient in cooking and snacks. Nut is included in the group of grains and has many types. Each type of nut has different nutritional content. Some types of nuts can also cause allergies or negative reactions in certain people, so it is important to identify the type of nut to be consumed. There are many types of nut that are different from each other, but some of them are similar. This makes it difficult to distinguish between the types of nuts, so there is a need for technology that can accurately identify nut types. Transfer Learning method is used to utilize trained models and applied to nut type classification. The two CNN models used are Inception V3 and Xception. The dataset consists of 11 types of nuts consisting of 1,320 data. The data is divided into 60% for training data and 40% for validation data. Preprocessing is done to ensure the image size is consistent and clarify the focus on the data image to be tested. The training results show that the Xception model is superior to Inception V3, with an accuracy of 86.36% on the validation data, while Inception V3 only reached 74.05%. Xception is able to predict nut types more precisely.

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How to Cite

Fawwaz, I., Sagala, J. D., Sijabat, R. K. F., & Maringga , N. M. (2023). Implementation of Transfer Learning in CNN for Classification of Nut Type. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(4), 2308-2315. https://doi.org/10.33395/sinkron.v8i4.12784

Issue

Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023

Section

Articles

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Copyright (c) 2023 Jimmy Sagala, Insidini Fawwaz, Reivaldo Sijabat, Novita Maringga

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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