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All Journal Planta Tropika BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks Emerging Information Science and Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat
Utama, Nafi Ananda
Program Studi Agroteknologi, Fakultas Pertanian, Universitas Muhammadiyah Yogyakarta, Jl. Lingkar Selatan, Kasihan, Bantul, Yogyakarta 55183, Indonesia, Telp. 0274 387656.
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Medicine & Pharmacology Social Sciences

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Journal : Emerging Information Science and Technology

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Discrete Curvelet Transform Feature Extraction for Mangosteen Fruit Surface Damage Detection Utama, Nafi Ananda; Triyani, Wahyu Indah; Riyadi, Slamet; Damarjati, Cahya
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.v5i1.22602

Abstract

Mangosteen (Garcinia mangostana L) is one of the commodities of Indonesian fruit and is used as an export primadona that became the basis of Indonesia to increase the currency of the country. The quality of the fruit can be seen from the surface, whether there is damage or not. The sorting that the farmers have been doing all this time is still using the conventional way, that is, with the sense of sight. This conventional method seems to be less effective because it takes a lot of energy, takes a long time, and there are different perceptions between farmers. To solve this problem, a method of surface quality extraction of mango fruit will be developed based on image processing. The initial stage of image processing is with the image size equation then the image is converted to grayscale mode, then a discrete curvelet transformation is performed. The next stage is the extraction of mean, energy, entropy, standard deviation, variance, sum, correlation, contrast, and homogeneity. The result of the subsequent feature extraction is used to enter a value at the classification stage. From some of these extractions it will be known which extraction has the highest accuracy value. The method of classification used is Linear Discriminant Analysis (LDA) with the method of K-Fold Cross Validation which in this study is divided into 4-fold cross validation. After testing on 120 images, the highest value of accuracy is with extraction of standard characteristics deviation of 91.7% and variance of 88.4%.
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 A.A. Ketut Agung Cahyawan W Agus Jamal Ayu Ratiwi, Amelia Mutiara Bambang Heri Isnawan Cahya Damarjati Deewo, Chonthicha Dewi, Sukuriyati Susilo Dian Pratiwi Gunawan Budiyanto Indira Prabasari, Indira Innaka Ageng Rineksane Mulyono Mulyono Rudy Suryanto, Rudy Rustiningsih Rustiningsih Rustiningsih, Rustiningsih Slamet Riyadi Subandono, Bagus Sukamta Sukamta Suryanto, Arwan Taufiq Hidayat Tony K Hariadi Triyani, Wahyu Indah Widyastuti, Titiek Zuhri, Afrizal