Perfecting a Video Game with Game Metrics
Vol 20, No 1: February 2022

Network and layer experiment using convolutional neural network for content based image retrieval work

Fachruddin Fachruddin (Faculty of Engineering, Universitas Sriwijaya, Palembang)
Saparudin Saparudin (Informatics Department, Telkom University, Bandung)
Errissya Rasywir (Universitas Dinamika Bangsa)
Yovi Pratama (Universitas Dinamika Bangsa)



Article Info

Publish Date
01 Feb 2022

Abstract

In this study, a test will be conducted to find out how the results of experiments on the network and layer used on the convolutional neural network algorithm. The performance and accuracy of the retrieval process method that was tested using the algorithm approach to do an object image retrieval. The expected results of this study are the techniques offered can provide relatively better results compared to previous studies. The results of the classification of object images with different levels of confusion on the Caltech 101 database resulted an average accuracy value. From the experiments conducted in the study, content based image retrieval work (CBIR) work using convolutional neural network (CNN) algorithm in terms of execution time, loss testing and accuracy testing. From several experiments on layers and networks shows that, the more hidden layers used, then the result is better. The graph of validation loss decreases at fewer epochs, slightly fluctuating at more epochs. Likewise, validation accuracy increases insignificantly on epochs with small amounts, but tends to be stable on more epochs.

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Journal Info

Abbrev

TELKOMNIKA

Publisher

Subject

Computer Science & IT

Description

Submitted papers are evaluated by anonymous referees by single blind peer review for contribution, originality, relevance, and presentation. The Editor shall inform you of the results of the review as soon as possible, hopefully in 10 weeks. Please notice that because of the great number of ...