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Leboh: An Android Mobile Application for Waste Classification Using TensorFlow Lite

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Intelligent Systems and Applications (IntelliSys 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 544))

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Abstract

Waste is an important part of human life and possibly be a serious issue for health and the environment if there is no proper deal. In this paper, the authors develop an Android mobile application for waste classification using EfficientNet-Lite model from TensorFlow Lite. The model is trained and validated using a dataset containing 15,190 images from 11 classes: cardboard, paper, glass, metal, electronics, battery, plastic, textile, shoes, organic, and trash. The model is evaluated using 655 images from the testing dataset and it is produced an accuracy of 95.39%. The model training and validation are done in Google Colab (Python). The model is then used as a classifier for an Android application. The application is named Leboh and developed for Indonesian speakers. The user testing of the Android application obtains an accuracy of 82.5%. Based on user testing, the quantity of plastic waste is higher than other types. EfficientNet-Lite successfully works well to classify municipal solid waste and runs fast on mobile devices.

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Acknowledgment

This paper was supported by research funding from Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Tarumanagara, No 1689-Int-KLPPM/UNTAR/XI/2021.

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Authors and Affiliations

  1. Universitas Tarumanagara, Jl. S. Parman No. 1 Gedung R Lantai 11, Jakarta, 11440, Indonesia

    Teny Handhayani & Janson Hendryli

Authors
  1. Teny Handhayani
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  2. Janson Hendryli
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Corresponding author

Correspondence to Teny Handhayani .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

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Handhayani, T., Hendryli, J. (2023). Leboh: An Android Mobile Application for Waste Classification Using TensorFlow Lite. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_4

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