Classification Organic and Inorganic Waste with Convolutional Neural Network Using Deep Learning

Authors

  • Abba Suganda Girsang Computer Science Department, BINUS Graduate Program –Master of Computer Science, Bina Nusantara University, Jakarta 11380, Indonesia https://orcid.org/0000-0003-4574-3679
  • Handy Pratama Computer Science Department, BINUS Graduate Program –Master of Computer Science, Bina Nusantara University, Jakarta 11380, Indonesia
  • Leody Pra Santo Agustinus Computer Science Department, BINUS Graduate Program –Master of Computer Science, Bina Nusantara University, Jakarta 11380, Indonesia

Keywords:

Deep Learning, Iimage Classification, Convolutional Neural Networks, Waste Classification

Abstract

The amount of waste worldwide continues to increase every year. Data shows that global waste production has continued to increase from two million tons per year to 381 million tons per year over the last 65 years. The biggest problem in decomposing waste is waste that has not been sorted, so organic and inorganic waste cannot be recycled directly. Organic waste can be recycled into organic fertilizer, which is useful for farmers, while inorganic waste can be processed into items that can be reused, such as flowerpots, handicrafts, and others. This research aims to make the waste sorting process faster and more efficient using deep learning. The first step in conducting this research was to collect datasets with two categories, namely organic and inorganic, which were divided into three parts, namely training, testing, and validation. To find the best learning outcomes, preprocessing and hyperparameter testing are needed. The models used are MobileNet, VGG16, and Xception. In this study, the MobileNet model produced an accuracy of 93.35%, which is the best result of the others.

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References

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Xception Architecture

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Published

22.02.2023

How to Cite

[1]
A. . Suganda Girsang, H. . Pratama, and L. P. . Santo Agustinus, “Classification Organic and Inorganic Waste with Convolutional Neural Network Using Deep Learning”, Int J Intell Syst Appl Eng, vol. 11, no. 2, pp. 343–348, Feb. 2023.

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Research Article