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All Journal Jurnal Informatika Teknologi dan Sains (Jinteks)
Fahmi Chairulloh
STIKOM Cipta Karya Informatika
Computer Science & IT Control & Systems Engineering

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KLASIFIKASI TINGKAT KEMATANGAN BUAH NANAS BERDASARKAN TEKSTUR GRAY LEVEL CO-OCCURRENCE MATRIX DENGAN METODE SUPPORT VECTOR MACHINE Sutisna; Fahmi Chairulloh
Jurnal Informatika Teknologi dan Sains Vol 4 No 4 (2022): EDISI 14
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (328.08 KB) | DOI: 10.51401/jinteks.v4i4.2047

Abstract

Pineapple (Ananas comosus (L) Merr) is one type of fruit that is commonly known and consumed by the people of Indonesia. The use of pineapples in general in the community is only limited to the flesh of the fruit. Pineapple fruit with high quality for consumption. Pineapple fruit that is ripe and unripe can be seen from the color, texture and shape. The manual method that is usually used to identify the level of ripeness of pineapple is by examining the appearance and aroma of the fruit and checking by touch. This method is considered less effective if it is used to sort the ripeness level of Pineapple in very large quantities. There are several features that can be used in image pattern recognition systems such as the Support Vector Machine (SVM). In this study, researchers will classify the level of maturity of pineapple based on texture where the preprocessing and feature extraction stages are based on GLCM texture contrast, correlation, energy, and homogeneity. then from the extraction of the image features can be identified by using the classification algorithm Support Vector Machine. Based on the results obtained, the highest accuracy on the C25 reached 86%.
Co-Authors Sutisna