Prasetyo, A Nevalen and Surtono, Arif and Junaidi, Junaidi and Pauzi, Gurum Ahmad (2021) Sistem Identifikasi Tingkat Kematangan Buah Nanas Secara Non-Destruktif Berbasis Computer Vision. Journal of Energy, Material, and Instrumentation Technology, 2 (1). ISSN 2747-2043

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Abstract

A computer vision-based non-destructive pineapple maturity level identification system has been realized. This research was conducted to create a system capable of identifying six indexes of pineapple maturity level. An artificial neural network is used as a classifier for the level of maturity pineapples. Artificial neural network input is a statistical parameter consisting of mean, standard deviation, variance, kurtosis, and skewness of RGB and HSV color models pineapple images. Statistical parameters of the color model with a Pearson correlation value greater than 0.5 were used to characterize pineapple images. A total of 360 pineapple images were used in the training process with a percentage of 75% of training data and 25% of validation data. An image segmentation process is applied to separate the pineapple image from the image background. The result of this research is a pineapple maturity level identification system consisting of software and hardware which is able to identify six indexes of pineapple maturity level with average accuracy value of 98,4%.

Item Type:	Article
Subjects:	Q Science > QC Physics
Divisions:	Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Fisika
Depositing User:	M.Si.M.Eng ARIF SURTONO
Date Deposited:	14 Nov 2021 12:16
Last Modified:	14 Nov 2021 12:16
URI:	http://repository.lppm.unila.ac.id/id/eprint/36772

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