IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK KLASIFIKASI JENIS SAMPAH ORGANIC DAN ANORGANIC

Chandra Kurniawan; Dede Supiyan

doi:10.59003/nhj.v6i2.2215

Authors

Chandra Kurniawan Universitas Pamulang
Dede Supiyan Universitas Pamulang

DOI:

https://doi.org/10.59003/nhj.v6i2.2215

Keywords:

Convolutional Neural Network, InceptionV3, image classification, paper waste, website.

Abstract

The problem of paper waste sorting remains a challenge because many people are still unable to distinguish between organic and inorganic paper waste. Errors in the sorting process reduce recycling effectiveness and increase environmental pollution due to the mixing of materials that cannot be processed together. This study aims to develop a web-based classification system for organic and inorganic paper waste using the Convolutional Neural Network (CNN) method with the InceptionV3 architecture. The dataset used was obtained through independent image acquisition using smartphone cameras and public datasets, with a total of approximately 900 images consisting of 650 images of organic paper waste and 250 images of inorganic paper waste. The research process included data collection, preprocessing, data labeling, model training, and model integration into a web application using TensorFlow, FastAPI, and Laravel. The developed system provides real-time classification through a camera, classification history storage, and visualization of system usage statistics. The results indicate that the system can assist users in identifying paper waste more quickly and efficiently and can serve as an educational medium to increase public awareness of the importance of proper paper waste sorting before the recycling process is carried out.

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