IMPLEMENTASI ALGORITMA CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI GERAKAN BELA DIRI TAEKWONDO BERBASIS ANDROID

Fauzan Akmal Mahdi, . (2022) IMPLEMENTASI ALGORITMA CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI GERAKAN BELA DIRI TAEKWONDO BERBASIS ANDROID. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

In the sport of taekwondo there is a demonstration of a series of movements called poomsae. Poomsae is quite often competed in taekwondo sports competitions in Indonesia and in the world. Currently, judging in the poomsae competition is still done by judges or humans. There is a Deep Learning method that can be used to make the image learning function more efficient and effective with the Convolutional Neural Network algorithm. The Convolutional Neural Network algorithm utilizes the convolution function to process input data in the form of images to be processed by the model so that classification can be done. By using the tensorflow library to create a convolutional network model, it is easy to integrate the model into an android-based mobile application. Therefore, researchers will implement the advantages of the Convolutional Neural Network algorithm to classify movements in taekwondo martial arts through input data in the form of images into an Android-based mobile application.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 1810511063] [Pembimbing 1: Theresiawati] [Pembimbing 2: Desta Sandya Prasvita] [Penguji 1: Ermatita] [Penguji 2: Mayanda Mega Santoni]
Uncontrolled Keywords: Martial arts, Convolutional Neural Network, Image, Poomsae, Taekwondo, Library, Tensorflow, Application, Mobile, Android
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Fauzan Akmal Mahdi
Date Deposited: 30 Aug 2022 07:35
Last Modified: 30 Aug 2022 07:35
URI: http://repository.upnvj.ac.id/id/eprint/20630

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