Traspoter Application Development: Website-Based Automatic Garbage Classification Using CNN Method

This research focuses on the development of automatic waste classification by applying the Convolutional Neural Network (CNN) method in a web-based application. This system is designed to help the waste management process through automatic sorting between organic and inorganic waste, so that it can support recycling efforts and reduce environmental impacts. In its application, this application utilizes the CNN algorithm to analyze images and recognize the type of waste with good accuracy. The development uses technologies such as Python and OpenCV to ensure efficient processing of image data, with the CNN model trained using a dataset of 22,564 images. Test results show excellent accuracy, reaching 99.27% for organic waste and 98.72% for inorganic waste.

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Publication Details

Journal
International Journal of Multilingual Education and Applied Linguistics
Published
2024-11-18
DOI
https://doi.org/10.61132/ijmeal.v1i4.148
Primary Topic
Computer Science and Engineering
Type
article
Field-Weighted Citation Impact
0.00
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article

Traspoter Application Development: Website-Based Automatic Garbage Classification Using CNN Method

Endang Purnama Giri, Bima Julian Mahardika, Budy Santoso, Aulia Anggraeni et al.
International Journal of Multilingual Education and Applied Linguistics
Computer Science and Engineering
article

Traspoter Application Development: Website-Based Automatic Garbage Classification Using CNN Method

Endang Purnama Giri, Bima Julian Mahardika, Budy Santoso, Aulia Anggraeni, Muhamad Ali Imron, Anatasya Wenita Putri, Gema Parasti Mindara
article en

Abstract

This research focuses on the development of automatic waste classification by applying the Convolutional Neural Network (CNN) method in a web-based application. This system is designed to help the waste management process through automatic sorting between organic and inorganic waste, so that it can support recycling efforts and reduce environmental impacts. In its application, this application utilizes the CNN algorithm to analyze images and recognize the type of waste with good accuracy. The development uses technologies such as Python and OpenCV to ensure efficient processing of image data, with the CNN model trained using a dataset of 22,564 images. Test results show excellent accuracy, reaching 99.27% for organic waste and 98.72% for inorganic waste.

International Journal of Multilingual Education and Applied LinguisticsVol. 1(4)
Openalex Percentile: Top 81%
Computer Science and Engineering
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