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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JURNAL AGROHITA Emerging Information Science and Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat
Indira Prabasari
Universitas Muhammadiyah Yogyakarta
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Medicine & Pharmacology Social Sciences Other

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Journal : Emerging Information Science and Technology

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Classification of Mangosteen Surface Quality Using Principal Component Analysis Slamet Riyadi; Amelia Mutiara Ayu Ratiwi; Cahya Damarjati; Tony K. Hariadi; Indira Prabasari; Nafi Ananda Utama
Emerging Information Science and Technology Vol 1, No 1: February 2020
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.94 KB) | DOI: 10.18196/eist.115

Abstract

Mangosteen (Garcinia mangostana L) is one of the primary contributor for Indonesia export. For export commodity, the fruit should comply the quality requirement including its surface. Presently, the surface is evaluated by human visual to classify between defect and non- defect surface. This conventional method is less accurate and takes time, especially in high volume harvest. In order to overcome this problem, this research proposed images processing based classification method using principal component analysis (PCA). The method involved pre-processing task, PCA decomposition, and statistical features extraction and classification task using linear discriminant analysis. The method has been tested on 120 images by applying 4-fold cross validation method and achieve classification accuracy of 96.67%, 90.00%, 90.00% and 100.00% for fold-1, fold-2, fold-3 and fold-4, respectively. In conclusion, the proposed method succeeded to classify between defect and non-defect mangosteen surface with 94.16% accuracy.
Co-Authors Amelia Mutiara Ayu Ratiwi Anna NN Chamim Cahya Damarjati Innaka Ageng Rineksane Rineksane Nafi A Utama Nafi Ananda Utama Slamet Riyadi Tony K Hariadi Zulfan Fadholi