Sistem Klasifikasi Sampah Perkantoran menggunakan Metode Faster Region Based Convolutional Neural Network berbasis NVIDIA Jetson Nano - Daris Muhammad Yafi; Fitri Utaminingrum (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2022)
According to law number 18 of 2008 concerning waste management, which is about the remnants of human activities or also from natural processes contained in solid or semi-solid form either in the form of organic or inorganic substances, which can be decomposed or not and are considered useless and then discharged into the environment. Therefore, the Ministry of Environment and Forestry (KLHK) has announced that the country's total waste production in 2020 will reach 67.8 million tons. This means that as many as 270 million people produce around 185,753 tons of waste every day. Based on the sources of waste disposed of in 2021, offices are the four largest waste contributor in Indonesia. The solution given in this research is to create a tool to automatically classify paper, plastic bottles, and cans using the Faster Region-based Convolutional Neural Network (Faster R-CNN) algorithm to facilitate waste sorting based on three types of waste. In the testing process, 36 objects were used with the division of 12 objects for the paper class, 12 objects for the plastic bottle class and 12 objects for the can class, and the model used was the result of training the Faster RCNN algorithm with 180,000 number_steps. The test results get an accuracy of 91.6667% for paper class, 91.6667% for plastic bottles, and 83.3333% for cans with a total computation time of 0.7166351s.