Klasifikasi Citra Sampah Daur Ulang Menggunakan CNN dengan Fungsi Aktivasi ReLU untuk Mendukung Sistem RVM
DOI:
https://doi.org/10.29313/bcss.v6i2.24217Keywords:
Convolutional Neural Network, Rectified Linear Unit, Reverse Vending MachineAbstract
Abstract. The total amount of waste generated nationwide in Indonesia reaches 144,839 metric tons per day, while only about 25% is managed optimally. This situation underscores the need for technologies capable of supporting automated waste management and sorting processes. One such technology is the Reverse Vending Machine (RVM), a machine that can accept and sort recyclable waste while providing incentives to users. To perform these functions optimally, RVM must be able to automatically and accurately recognize various types of trash. Therefore, this study aims to classify images of recyclable waste using a Convolutional Neural Network (CNN) with a Rectified Linear Unit (ReLU) activation function. The dataset used consists of five waste classes: paper/cardboard packaging, beverage cans, dented beverage cans, plastic bottles, and glass bottles. The CNN architecture used consists of three convolutional layers with a 3×3 kernel size and 32, 64, and 128 filters, respectively, and is supplemented by max pooling, flatten, and fully connected layers. The model was trained using the Stochastic Gradient Descent (SGD) algorithm with a mini-batch approach. The research results show that the model is capable of classifying images of recyclable waste with good performance, achieving an accuracy of 84.52%, a precision of 86.18%, a recall of 84.52%, and an F1-score of 84.46%.
Abstrak. Jumlah timbulan sampah nasional di Indonesia mencapai 144.839 ton per hari, sementara hanya sekitar 25% yang terkelola secara optimal. Kondisi tersebut mendorong perlunya penerapan teknologi yang mampu mendukung proses pengelolaan dan pemilahan sampah secara otomatis. Salah satu teknologi yang dapat diterapkan adalah Reverse Vending Machine (RVM), yaitu mesin yang mampu menerima dan memilah sampah daur ulang sekaligus memberikan insentif kepada penggunanya. Untuk menjalankan fungsi tersebut secara optimal, RVM memerlukan kemampuan untuk mengenali berbagai jenis sampah secara otomatis dan akurat. Oleh karena itu, penelitian ini bertujuan untuk mengklasifikasikan citra sampah daur ulang menggunakan metode Convolutional Neural Network (CNN) dengan fungsi aktivasi Rectified Linear Unit (ReLU). Dataset yang digunakan terdiri atas lima kelas sampah, yaitu kertas/karton kemasan, kaleng minuman, kaleng minuman penyok, botol plastik, dan botol kaca. Arsitektur CNN yang digunakan terdiri dari tiga lapisan konvolusi dengan ukuran kernel 3×3 dan jumlah filter bertingkat 32, 64, dan 128, serta dilengkapi lapisan max pooling, flatten, dan fully connected. Proses pelatihan model dilakukan menggunakan algoritma Stochastic Gradient Descent (SGD) dengan pendekatan mini-batch. Hasil penelitian menunjukkan bahwa model mampu mengklasifikasikan citra sampah daur ulang dengan performa yang baik, mencapai accuracy sebesar 84,52%, precision sebesar 86,18%, recall sebesar 84,52%, dan F1-score sebesar 84,46%.
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