Muhammad Rafly Alwanda
STMIK Global Informatika MDP

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Implementation of Convolutional Neural Network Method Using LeNet-5 Architecture for Doodle Recognition Muhammad Rafly Alwanda; Raden Putra Kurniawan Ramadhan; Derry Alamsyah
Jurnal Algoritme Vol 1 No 1 (2020): Jurnal Algoritme
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1071.736 KB) | DOI: 10.35957/algoritme.v1i1.434


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.