Classification of Mangosteen Surface Quality Based on Image Processing Using Support Vector Machine

Authors

  • Slamet Riyadi Universitas Muhammadiyah Yogyakarta
  • Cahya Damarjati Universitas Muhammadiyah Yogyakarta
  • Nuri Primas Universitas Muhammadiyah Yogyakarta
  • Tony K. Hariadi Universitas Muhammadiyah Yogyakarta
  • Indira Prabasari Universitas Muhammadiyah Yogyakarta
  • Nafi Ananda Utama Universitas Muhammadiyah Yogyakarta
  • Ku Ruhana Ku-Mahamod Universiti Utara Malaysia

Keywords:

Mangosteen surface defect, Features Extraction, Discrete Curvelet Transform, Support vector machine

Abstract

Mangosteen is one of the highest export commodities among other fruits in Indonesia. Mangosteen should be free from defects and damages to be accepted as export quality. In most of the mangosteen plantation in Indonesia, sorting of mangosteen is performed manually with human eye. This method is less effective and inaccurate because it depends on workers’ conditions and perceptions. The use of image processing technology for quality inspection has been done for various fruits. However, mangosteen quality inspection in Indonesia has not used image processing technology. The objective of this research is to develop a method using support vector machine to classify defect and non-defect mangosteen surface. The method involved mangosteen surface image capture, pre-processing, defect features extraction using Curvelet transformation and classification using support vector machine. The proposed method was implemented on 120 images which produced 96.67% of accuracy. It can be concluded that the proposed method has successful classify mangosteen surface

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Published

23-11-2021

How to Cite

Riyadi, S., Damarjati, C. ., Primas, N. ., K. Hariadi, T. ., Prabasari, I. ., Utama, N. A. ., & Ku-Mahamod, K. R. . (2021). Classification of Mangosteen Surface Quality Based on Image Processing Using Support Vector Machine. International Journal of Integrated Engineering, 13(5), 288–294. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/6912