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Taftyani Yusuf Prahudaya
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Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Industrial & Manufacturing Engineering Mechanical Engineering

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METODE KLASIFIKASI MUTU JAMBU BIJI MENGGUNAKAN KNN BERDASARKAN FITUR WARNA DAN TEKSTUR Taftyani Yusuf Prahudaya; Agus Harjoko
Jurnal Teknosains Vol 6, No 2 (2017): June
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/teknosains.26972

Abstract

Guava (Psidium guajava L.) is a fruit that has many health benefits. Guava also has commercial value in Indonesia and has a large market share. This indicates that the commodity of guava has been consumed by society extensively. This time the sorting process is still done manually which still has many shortcomings. This classification gives the classification results are less accurate and inconsistent due to the carelessness of humans. Grading process in the marketing sector is essential. Improper grading potentially detrimental to farmers because all the fruit quality were priced the same. Therefore, we need a consistent classification system.The system uses image processing to extract the color and texture features of guava. As a quality classification KNN method (K-Nearest Neighbor) is used. This system will classify guava into four quality classes, namely the super class, class A, class B, and external quality. KNN designed with input 7 features extraction which is the average value of RGB (Red, Green, and Blue), total defect area, and the GLCM value (entropy, homogeneity, and contrast) with the 4 outputs of quality. From the test results showed that the classification method is able to classify the quality of guava. The highest accuracy is obtained in testing K = 3 with 91.25% accuracy rate.
Co-Authors Agus Harjoko