The Integration of Soluble Solid Content (SSC) Data : Classification and Prediction of Oranges Quality Ida Ratna Nila (a*), M.Ari Fahril (a), Sabrian Tri Anda (a), Rachmad Almi Putra (a), Fajriani (a), Afrahun Naziah (a), Fira Lita Ananda (b) Evalina Siahan (b), Rahmatul Fajri (c)
(a) Physics Study Program, Faculty of Science and Technology, Universitas Samudra
Jl, Prof. Dr. Syarief Thayeb, Meurandeh, Langsa Lama, Langsa City, Aceh 24416
(b) Physics Study Program, Faculty of Science and Technology, Universitas Samudra
Jl, Prof. Dr. Syarief Thayeb, Meurandeh, Langsa Lama, Langsa City, Aceh 24416
(c) Department of Chemistry, Faculty of Science and Technology, Universitas Samudra
Jl, Prof. Dr. Syarief Thayeb, Meurandeh, Langsa Lama, Langsa City, Aceh 24416
*idaratnanila[at]unsam.ac.id
Abstract
Oranges fruit ripeness is an important factor in determining the quality and selling value of the fruit. Currently, orange fruits are classified through visual analysis of fruit skin color, which proves both ineffective and inefficient. This research aims to classify orange fruits by sweetness level using digital image processing techniques, validated through Soluble Solid Content (SSC) measurements. The ripeness level of orange fruits is measured based on color and texture features extracted from digital images, and subsequently validated using a refractometer for sugar content measurement. This method aims to provide a non-destructive alternative in determining the ripeness of orange fruits. The study employs Principal Component Analysis (PCA) for classifying orange fruits quality based on physical characteristics (color texture, size, and weight). The sample used consisted of 98 orange fruits with three maturity categories: unripe, ripe, and overripe. The results show that this method can identify the ripeness of orange fruits with an accuracy of up to 93%, providing potential for practical applications in the agricultural and fruit processing industries.
