Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle
Muhammad Rafly Alwanda, Raden Putra Kurniawan Ramadhan, Derry Alamsyah
Abstract
Recognition of objects to date has been widely applied in various fields, for example in handwritten recognition. This research utilizes the ability of CNN to use LeNet-5 architecture for the introduction of doodle types with 5 object images, namely clothes, pants, chairs, butterflies and bicycles. Each doodle object consists of 30 images with a total dataset of 150 images. The test results show that the first, second and fourth scenarios of bicycle objects are more recognized with an accuracy value of 93% - 98%, recall 86% - 93% and precision 81% - 93%, clothes objects are more recognized in the third scenario with an accuracy value of 94%, 86% recall, and 83% precision.