Open AccessImplementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle
Author(s) -
Muhammad Rafly Alwanda,
Raden Putra Kurniawan Ramadhan,
Derry Alamsyah
Publication year - 2020
Publication title -
algoritme jurnal mahasiswa teknik informatika
Language(s) - English
Resource type - Journals
ISSN - 2775-8796
DOI - 10.35957/algoritme.v1i1.434
Subject(s) - convolutional neural network , computer science , artificial intelligence , object (grammar) , value (mathematics) , recall , pattern recognition (psychology) , machine learning , psychology , cognitive psychology
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.