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All Journal Planta Tropika BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks AGRITEKNO, Jurnal Teknologi Pertanian Emerging Information Science and Technology AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment)
Indira Prabasari, Indira
Program Studi Agroteknologi, Fakultas Pertanian, Universitas Muhammadiyah Yogyakarta
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Education Energy Environmental Science

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Journal : Emerging Information Science and Technology

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Analysis of Cross Validation on Classification of Mangosteen Maturity Stages using Support Vector Machine Prabasari, Indira; Zuhri, Afrizal; Riyadi, Slamet; Hariadi, Tony K; Utama, Nafi Ananda
Emerging Information Science and Technology Vol 5, No 1 (2024): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v5i1.22359

Abstract

This study explores the efficacy of the Support Vector Machine (SVM) method in classifying mangosteen fruit images based on six ripeness levels. Employing SVM enables nonlinear data classification and simultaneous utilization of multiple feature extractions, resulting in enhanced accuracy. Analysis reveals that models integrating three feature extractions outperform those with only two. With ample training data and optimized parameters, SVM achieves detection accuracy exceeding 90%. However, algorithmic enhancements are necessary to compute RGB color index values for all pixels on mangosteen skin surfaces, possibly through circular-shaped windows approximating the fruit's contour. Moreover, comparative assessments of RGB color system calculations against alternative systems such as HSI are crucial for selecting the most suitable color model in alignment with human perception.
Classification of Mangosteen Surface Quality Using Principal Component Analysis Riyadi, Slamet; Ayu Ratiwi, Amelia Mutiara; Damarjati, Cahya; Hariadi, Tony K.; Prabasari, Indira; Utama, Nafi Ananda
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 | 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 Ayu Ratiwi, Amelia Mutiara Cahya Damarjati Chandra Setiawan, Chandra Innaka Ageng Rineksane Lestari, Mutiara I Nusivera, C.D. Slamet Riyadi Titiek F Djaafar Tony K Hariadi Utama, Nafi Utama, Nafi A Utama, Nafi Ananda Zuhri, Afrizal