IMPLEMENTASI DEEP LEARNING DENGAN CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI GAMBAR SAMPAH ORGANIK DAN ANORGANIK

Octavia Devi Safitri Sunanto (Program Studi Matematika, Fakultas MIPA, Universitas Sebelas Maret, Surakarta)
Putranto Hadi Utomo (Program Studi Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Sebelas Maret Surakarta)

UNEJ e-Proceeding
2022: E-Prosiding Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi (SeNa-MaGeStiK)

Indonesia is the second largest plastic waste contributor in the world. This is due to the lack of maximum waste recycling process. Recycling of waste is one way to reduce the amount of waste that accumulates in the environment. The recycling process starts from the process of separating waste. In mathematics, the waste separation process can be applied using the Convolutional Neural Network model. Convolutional Neural Network (CNN) is one of the deep learning methods that can be used to detect and recognize an object on a network digi- tal image. This study will determine the CNN (Convolutional Neural Network) model which has high accuracy in sorting waste using the tensorflow method. In this study, an analysis of the CNN that is suitable for the classification of waste sorting will be carried out optimally. So that this algorithm can be used for the implementation of automatic waste separation using the system. This research aims to reduce time and simplify the process of waste separation in the waste recycling stage. Keyword: sampah, anorganik, organik, Convolutional Neural Network, tensorflow.