Oka Sudana, Department of Information Technology, Udayana University, Indonesia
I Putu Agung Bayupati, Department of Information Technology, Udayana University, Indonesia
Dewa Gede Yudiana, Department of Information Technology, Udayana University, Indonesia
Mangosteen (Garcinia Mangostana L.) is a plant that grows in tropical area and originally from Indonesia which becomes the prime commodity of the national trade and has a very big export potential. Mangosteen is a climacteric food so it can be ripen in its storage period. Exported mangosteen needs to pass through the output process, the output process still uses the manual mode . the identification process that uses a person’s eyes visually is still having some limits. The process needs more capacity to sort the fruit. With the developed technology nowadays the process itself can be done digitally. This research applied the Deep Learning with Convolution Neural Network (CNN) method which give the 97.1% accuration result.
Convolution Neural Network (CNN), Deep learning, Mangosteen, Maturity Level
[1] A. Kurniawati, R. Poerwanto, D. Effendi, and H. Cahyana, “Evaluation of Fruit Characters , Xanthones Content , and Antioxidant Properties of Various Qualities of Mangosteens ( Garcinia mangostana L .),” Indonesian Journal of Agronomy, vol. 38, no. 3, pp. 232–237, 2010.
[2] K. P. Badan Penelitian dan Pengembangan Pertanian, “Balai Besar Penelitian dan Pengembangan Pascapanen Pertanian,” 2017.
[3] Dian Nursantika. Fajri Rakhmat Umbara, “Pengenalan Citra Buah Manggis Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation,” Infomedia Vol.1 No. 1, vol. 1, no. infomedia, pp. 44–47, 2016.
[4] R. Anitha, S. Jyothi, V. N. Mandhala, D. Bhattacharyya, and T. Kim, “Deep Learning Image Processing Technique for Early Detection of Alzheimer ’ s Disease,” International Journal of Advanced Science and Technology, vol. 107, pp. 85–104, 2017.
[5] J. Yun and J. H. Kim, “A Study on Training Data Selection Method for EEG Emotion Analysis using Machine Learning Algorithm,” International Journal of Advanced Science and Technology, vol. 119, pp. 79–88, 2018.
[6] J. Seetha and S. S. Raja, “Brain Tumor Classification Using Convolutional Neural Networks,” Biomedical & Pharmacology Journal, vol. 11, no. September, pp. 1457–1461, 2018.
[7] K. Vora and S. Yagnik, “A Survey on Backpropagation Algorithms for Feedforward Neural Networks,” International Journal Of Engineering Development And Research, pp. 193–197.
[8] I. A. Habriana Budi Kurniasaria, Susilo, “Penerapan Pengolahan Citra Digital dengan Matlab 7.1 pada Citra Radiografi,” Unnes Phisics Journal, vol. 1, no. 2252, pp. 1–4, 2012.
[9] I. W. S. E. Putra, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) Pada Caltech 101,” Jurnal Teknik ITS, vol. 5, no. 1, p. 76, 2016.
[10] L. Marifatul Azizah, S. Fadillah Umayah, and F. Fajar, “Deteksi Kecacatan Permukaan Buah Manggis Menggunakan Metode Deep Learning dengan Konvolusi Multilayer,” Semesta Teknika, vol. 21, no. 2, pp. 230–236, 2018.
[11] H. Prabowo et al., “Deteksi Kondisi Kematangan Buah Jeruk Berdasarkan,” Jurnal Elektronik Sistem Informasi dan Komputer, vol. 3, no. 2, 2017.
[12] F. Wibowo, A. Harjoko, H. Mustafidah, . S., and . A., “The Classification of Papaya using Digital Image Processing and Artificial Neural Networks,” International Journal of Advanced Science and Technology, vol. 118, pp. 35–46, 2018.
[13] Indarto and Murinto, “Banana Fruit Detection Based on Banana Skin Image Features Using HSI Color Space Transformation Method,” Jurnal Ilmiah Informatika, vol. V, no. November, pp. 15–21, 2017.
[14] D. Rusjayanthi, “Identifikasi Biometrika Telapak Tangan Menggunakan Metode Pola Busur Terlokalisasi, Block Standar Deviasi, dan K-Means Clustering,” Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, vol. 4, no. 2, pp. 265–276, 2013.
[15] F. Astutik, “Sistem Pengenalan Kualitas Ikan Gurame dengan Wavelet, PCA, Histogram HSV dan KNN,” Lontar Komputer, vol. Vol. 4, no. 2, p. 11, 2013.
[16] N. K. A. Wirdiani and A. A. K. Oka Sudana, “Medicinal plant recognition of leaf shape using Localized Arc Pattern Method,” International Journal of Engineering and Technology, vol. 8, no. 4, pp. 1847–1854, 2016.
[17] W. Hu, Y. Huang, L. Wei, F. Zhang, and H. Li, “Deep Convolutional Neural Networks for Hyperspectral Image Classification,” Journal of Sensors, vol. 2015, 2015.
[18] S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Computational Intelligence and Neuroscience, vol. 2016, 2016.
[19] S. Naskar, “A Fruit Recognition Technique using Multiple Features and Artificial Neural Network,” International Journal of Computer Applications, vol. 116, no. 20, pp. 23–28, 2015.
[20] O. K. a Sudana, D. Putra, and A. Arismandika, “Face Recognition System on Android Using Eigenface Method,” Journal of Theoretical and Applied Information Technology, vol. 61, no. 1, pp. 128–134, 2014.
[21] A. Hidayat, U. Darusalam, I. Technology, and S. Jakarta, “Detection of Disease on Corn Plants Using Convolutional Neural Network Methods,” Journal of a Science and Information, vol. 1, pp. 51–56, 2019.
[22] C. K. Dewa and A. L. Fadhilah, “Convolutional Neural Networks for Handwritten Javanese Character Recognition,” Indonesian Journal of Computing and Cybernetics Systems, vol. 12, no. 1, pp. 83–94, 2018.
[23] M. Mahmudul, A. Mia, S. K. Biswas, M. C. Urmi, and A. Siddique, “An Algorithm For Training Multilayer Perceptron ( MLP ) For Image Reconstruction Using Neural Network Without Overfitting .,” International Journal Of Scientific & Technology Research, vol. 4, no. 02, pp. 2–6, 2015.
APA:
Sudana, O., Bayupati, I. P. A., & Yudiana, D. G. (2020). Classification of Maturity Level of the Mangosteen using the Convolutional Neural Network (CNN) Method. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 135, 37-48. doi: 10.33832/ijast.2020.135.04.
MLA:
Sudana, Oka, et al. “Classification of Maturity Level of the Mangosteen using the Convolutional Neural Network (CNN) Method.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 135, 2020, pp. 37-48. IJAST, http://article.nadiapub.com/IJAST/Vol135/4.html.
IEEE:
[1] O. Sudana, I. Putu Agung Bayupati, and D. Gede Yudiana, "Classification of Maturity Level of the Mangosteen using the Convolutional Neural Network (CNN) Method." International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 135, pp. 37-48, Feb 2020.
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