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All Journal Journal of Energy, Material, and Instrumentation Technology
Nevalen Aginda Prasetyo
Department of Physics, University of Lampung
Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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Sistem Identifikasi Tingkat Kematangan Buah Nanas Secara Non-Destruktif Berbasis Computer Vision Nevalen Aginda Prasetyo; Arif Surtono; Junaidi Junaidi; Gurum Ahmad Pauzi
Journal of Energy, Material, and Instrumentation Technology Vol 2 No 1 (2021): Journal of Energy, Material, and Instrumentation Technology
Publisher : Departement of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (567.154 KB) | DOI: 10.23960/jemit.v2i1.26

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%.
Co-Authors Gurum Ahmad Pauzi Junaidi Junaidi Surtono, Arif