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All Journal Jurnal INKOM Seminar Nasional Informatika (SEMNASIF) JOIV : International Journal on Informatics Visualization Jurnal Mantik Jurnal Atma Inovasia Infact: Jurnal Sains dan Komputer
Antonius Bima Murti Wijaya
Universitas Kristen Immanuel
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

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Modified LeNet-5 Architecture to Classify High Variety of Tourism Object: A Case Study of Tourism Object for Education in Tinalah Village Wijaya, Antonius Bima Murti; Murti, Desideria Cempaka Wijaya; Handoko, Victoria Sundari
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2095

Abstract

This research aims to modify a CNN (Convolutional Neural Network) based on LeNet-5 to reduce overfitting in a Tinalah Tourism Village dataset object detection. Tinalah Tourism Village has many objects that can be identified for tourism education and enhanced tourist experience. While these objects, spread across the different sites of Tinalah do vary, some share similarities in their histogram patterns. Visually, if the size of a picture is reduced in the LeNet-5 ‘preferred size’ feature, it will inevitably lose some of its information, making pictures too similar reducing accuracy. In order to learn and classify objects, this research performs a modification on LeNet-5 architecture to provide a better performance geared toward larger input imaging. The previous state-of-the-art architecture showed an overfitting performance where the training accuracy performed too much better than the testing accuracy in our dataset. We brought in a dropout layer to reduce overfitting, increase the dense layer's size, and add a convolution layer. We then compared the modified LeNet-5 with other state-of-the art architecture, such as LeNet-5 and AlexNet. Results showed that a modified LeNet-5 outperformed other architectures, especially in performing accuracy for testing the Tinalah dataset, reaching 0.913 or (91,3 %). This research discusses the dataset, the modified LeNet-5 architecture, and performance comparison between state-of-the-art CNN architecture. Our CNN architecture can be developed by involving a transfer learning mechanism to provide greater accuracy for further research.
Co-Authors Barita Suci Vernando Zebua Benyamin L Sinaga Brigida Arie Minartiningtyas Desideria Cempaka Wijaya Murti Emerald, Gabriel Haeni Budiati Handoko, Victoria Sundari Irya Wisnubhadra Jatmika Jatmika Kusumastuti, Zikrina Ratri Lisa Niat Kartika Waruwu Marlina Sara Murti, Desideria Cempaka Wijaya Rachel Christia Tumbel Timothy Ageng Pracaya Yo’el Pieter Sumihar