Penentuan Kematangan Buah Salak Pondoh Di Pohon Berbasis Pengolahan Citra Digital
Pawit Rianto(1*), Agus Harjoko(2)
(1) 
(2) Departemen Ilmu Komputer dan Elektronika, Universitas Gadjah Mada, Yogyakarta
(*) Corresponding Author
Abstract
Because there is no a system based on Digital Image Processing to determine the degree of ripeness of Salak Pondoh (Salacca zalacca Gaertner Voss.) on tree, then this study has attempted to implement such a system. System was built with consists of several sub-processes. First, the segmentation process, the system will perform a search of pixels alleged pixels salak pondoh, by utilizing the features of color components r, g, b, and gray of each pixel salak pondoh then calculated large the dissimilarity ( Euclidean Distance ) against values of data features , , , and comparison. If the value of dissimilarity less than the threshold value and is also supported by the neighboring pixels from different directions has a value of dissimilarity is less than a threshold value, the pixel is set as an object pixel, for the other condition set as background pixels. For the next, improvements through an elimination noise stage and filling in the pixels to get a perfect binary image segmentation. Second, classification, by knowning the mean value of R and V of the entire pixel object, then the level of ripeness salak pondoh can be determined by using the method of classification backpropagation or k -Nearest Neighbor. From the test results indicate that the success of the system by 92% when using a backpropagatioan classification algorithm and 93% with k-Nearest Neighbor algorithm.
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[1]
Nuske,S., Achar,S., Bates,T., Narasimhan,S., dan Sigh, S., 2011,Yield Estimation in Vineyards by Visual Grape Detection,IEEE,RSJ International Conference on Intelligent Robots and Systems,2352-2358.
[2]
Meunkaewjinda, A., Kumsawat, P., Attakitmongcol, L., dan Srikaew, A., 2008, Grape Leaf Disease Detection from Color Imagery Using Hybrid Intelligent System, IEEE, 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 1, 513-516.
[3]
Syaban, K., dan Harjoko, A., 2016, Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network. IJCCS , Vol.10, No.2, Hal.161-172.
[4]
Edan, Y., 1995, Design of an Autonomous Agricultural Robot, Springer, Applied Intelligence, 5,41-50.
[5]
Jhuria, M., Kumar, A., dan Borse, R., 2013, Image Processing for Smart Farming : Detection of Disease and Fruit Grading, IEEE, Second International Conference on Image Information Processing ( ICIIP), 521-526.
[6]
Sari,O.K.,2008,Studi Budidaya dan Penanganan Pasca Panen Salak Pondoh ( Salacca zalacca Gaertner Voss. ) di Wilayah Kabupaten Sleman, Skripsi Fakultas Pertanian Institut Pertanian Bogor, Bogor.
[7]
Gunawan,M., 2011,Analisis Investasi Usaha Tani Salak Pondoh di Desa Dawuhan Kecamatan Madukara Kabupaten Banjarnegara, Skripis Jurusan Pertanian Universitas Pembangunan Nasional Veteran, Yogyakarta.
[8]
Ahmad,U.,2005,Pengolahan Citra Digital dan Teknik Pemrogramannya (Edisi Pertama), Graha Ilmu, Yogyakarta.
[9]
Gonzalez. R., dan Woods. R.E., 2008, Digital Image Processing, Third Edition, Eurson Education, Prentice-Hall, Inc.
[10]
Feng,L.,Xioyu,L., dan Yi,C., 2014, An Efficient Detection Method for Rare Colored Capsule Based on RGB and HSV color Space, Proceedings IEEE International Conference on Granular Computing, hal.175-178.
[11]
Sumariyani,L., 2015, Identifikasi Varietas Beras Berdasarkan Citra Digital Menggunakan Image Processing dan Neural Network, Tesis Jurusan Ilmu Komputer dan Elektronika Universitas Gadjah Mada, Yogyakarta.
DOI: https://doi.org/10.22146/ijccs.17416
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