Performance Comparison between VGG16 and Inception V3 for Organic Waste and Recyclable Waste Classification

Authors

  • Abba Suganda Girsang Computer Science Department, BINUS Graduate Program –Master of Computer Science, Bina Nusantara University, Jakarta 11380, Indonesia https://orcid.org/0000-0002-0529-2095
  • Andrew Dharma Saputra Computer Science Department, BINUS Graduate Program –Master of Computer Science, Bina Nusantara University, Jakarta 11380, Indonesia,
  • Vincentius Yanrie Computer Science Department, BINUS Graduate Program –Master of Computer Science, Bina Nusantara University, Jakarta 11380, Indonesia

Keywords:

Computer Vision, VGG16, Inception V3, waste classification

Abstract

Computer vision is used for learning image recognition, where a CNN algorithm is used to carry out the learning of the image itself. In this paper, a comparison is made between the two algorithms to determine which is better by comparing VGG16 and Inception V3 using a dataset that distinguishes the types of organic waste and recyclable waste. This study proposes algorithms using VGG-16 and Inception V3 that use a semi-supervised learning system to train algorithms from different images. By training the two algorithms, it can be seen that VGG16 and Inception V3 have quite good accuracy, but for the dataset used, it is better to use the VGG16 algorithm because Inception V3 has a fairly complicated algorithm model that makes the performance of the algorithm with the dataset not optimal. Therefore, further research is needed to optimize the two models to train the dataset. This case is taken because waste management is still a problem for our environment.

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Published

17.02.2023

How to Cite

Suganda Girsang, A. ., Dharma Saputra, A. ., & Yanrie, V. . (2023). Performance Comparison between VGG16 and Inception V3 for Organic Waste and Recyclable Waste Classification. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 557–563. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2711

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

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