Comparison of Classification for Grading Red Dragon Fruit (<i>Hylocereus Costaricensis</i>)
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Keywords
Abstract
Pitaya is another name for dragon fruit which is currently a popular fruit, especially in Indonesia. One of the problems related to determining the quality of dragon fruit is the postharvest sorting and grading process. In general, farmers determine the grading system by measuring the weight or just looking at the size of the fruit, of course, this raises differences in grading perceptions so that it is not by SNI. This research is a development of previous research, but we changed the type of dragon fruit from white dragon fruit (Hylocereus undatus) to red dragon fruit (Hylocereus costaricensis). We also adapted the image processing and classification methods in previous studies and then compared them with other classification methods. The number of images in the training data is 216, and the number of images in the testing data is 75. The comparison of the accuracy of the three classification methods is 84% for the KNN method, 85.33% for the Naive Bayes method, and 86.67% for the Backpropagation method. So that the backpropagation method is the best classification method in classifying the quality grading of red dragon fruit. The network architecture used is 4, 8, 3 with a learning rate of 0.3 so that the training accuracy is 98.61% and the testing accuracy is 86.67%.
References
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Direktorat Budidaya Tanaman Buah, “Pedoman Baku Budidaya Standard Operating Procedure (SOP) Buah Naga (Hylocereus undatus) Kabupaten Sleman,” Sleman, 2009.
D. Fitrada, “APLIKASI IMAGE PROCESSING UNTUK MENENTUKAN TINGKAT MUTU BUAH NAGA (Hylocereus undatus) SECARA NON-DESTRUCTIVE,” 2010.
R. Wulanningrum and N. V. W. Lelitavistara, “Discrete Cosine Transform Untuk Identifikasi Citra Hylocereus Costaricensis,” Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 6, no. 2, p. 353, 2015, doi: 10.24176/simet.v6i2.472.
R. D. M. Bimantaka, “IDENTIFIKASI KEMATANGAN BUAH NAGA MERAH MENGGUNAKAN METODE BACKPROPAGATION BERBASIS ANDROID,” Yogyakarta, 2017.
F. Nisa’, “Sistem Identifikasi Citra Hylocereus costaricensis dengan Menggunakan Metode K-Nearest Neighbor dan Mean Statistik,” Kediri, 2018.
Z. E. Fitri, A. Baskara, M. Silvia, A. Madjid, and A. M. N. Imron, “Application of Backpropagation Method for Quality Sorting Classification System on White Dragon Fruit (Hylocereus undatus),” in IOP Conference Series: Earth and Environmental Science, Apr. 2021, vol. 672, no. 1. doi: 10.1088/1755-1315/672/1/011001.
Z. E. Fitri, W. B. Nugroho, A. Madjid, and A. M. N. Imron, “Comparison of Neural Network Methods for Classification of Banana Varieties (Musa paradiasaca),” Jurnal Rekayasa Elektrika, vol. 17, no. 2, 2021, doi: 10.17529/jre.v17i2.20806.
Z. E. Fitri and A. M. N. Imron, “Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Using Backpropagation,” in Biomedical Engineering, and Health Informatics, 2021, vol. 2020, pp. 499–508. [Online]. Available: http://www.springer.com/series/7818
Z. E. Fitri, R. Rizkiyah, A. Madjid, and A. M. N. Imron, “Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat,” Jurnal Rekayasa Elektrika, vol. 16, no. 1, pp. 44–49, 2020, doi: 10.17529/jre.v16i1.15535.
Z. E. Fitri, L. N. Sahenda, P. S. D. Puspitasari, P. Destarianto, D. L. Rukmi, and A. M. N. Imron, “The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor,” Lontar Komputer, vol. 12, no. 2, pp. 91–101, Aug. 2021.
J. N. Anita, “Pengukuran Dimensi Retakan pada Citra Jalan menggunakan Filter Gabor,” Surabaya, 2018.
Z. E. Fitri, I. K. E. Purnama, E. Pramunanto, and M. H. Purnomo, “A comparison of platelets classification from digitalization microscopic peripheral blood smear,” 2017 International Seminar on Intelligent Technology and Its Application: Strengthening the Link Between University Research and Industry to Support ASEAN Energy Sector, ISITIA 2017 - Proceeding, vol. 2017-Janua, pp. 356–361, 2017, doi: 10.1109/ISITIA.2017.8124109.
Z. E. Fitri, L. N. Y. Syahputri, and A. M. N. Imron, “Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Based on K-Nearest Neighborr,” Scientific Journal of Informatics, vol. 7, no. 1, pp. 136–142, 2020, doi: 10.15294/sji.v7i1.24372.
A. M. N. Imron and Z. E. Fitri, “A Classification of Platelets in Peripheral Blood Smear Image as an Early Detection of Myeloproliferative Syndrome Using Gray Level Co-Occurence Matrix,” in Journal of Physics: Conference Series, May 2019, vol. 1201, no. 1. doi: 10.1088/1742-6596/1201/1/011001.
Z. E. Fitri, U. Nuhanatika, A. Madjid, and A. M. N. Imron, “Penentuan Tingkat Kematangan Cabe Rawit (Capsicum frutescens L.) Berdasarkan Gray Level Co-Occurrence Matrix,” Jurnal Teknologi Informasi dan Terapan, vol. 7, no. 1, pp. 1–5, 2020, doi: 10.25047/jtit.v7i1.121.
S. Shidada et al., “Identifikasi Acute Lymphoblastic Leukemia pada Citra Mikroskopis Menggunakan Algoritma Naïve Bayes Evaluas i,” vol. 14, no. 1, pp. 78–83, 2021.
H. F. Putro, R. T. Vulandari, and W. L. Y. Saptomo, “Penerapan Metode Naive Bayes Untuk Klasifikasi Pelanggan,” Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN), vol. 8, no. 2, pp. 19–24, Oct. 2020, doi: 10.30646/tikomsin.v8i2.500.
A. A. Mahran, R. K. Hapsari, and H. Nugroho, “PENERAPAN NAIVE BAYES GAUSSIAN PADA KLASIFIKASI JENIS JAMUR BERDASARKAN CIRI STATISTIK ORDE PERTAMA,” Jurnal Ilmiah NERO, vol. 5, no. 2, pp. 91–99, 2020.
Abd. Ghofur, “Implementasi Metode Klasifikasi Naive Bayes Untuk Memprediksi Kualitas Cabai,” Jurnal Ilmiah Informatika, vol. 1, no. 1, pp. 32–38, 2016, doi: 10.35316/jimi.v1i1.441.
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Mar 29, 2022
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Fitri, Z. E., Baskara, A., Madjid, A., & Imron, A. M. N. (2022). Comparison of Classification for Grading Red Dragon Fruit (<i>Hylocereus Costaricensis</i>). Jurnal Nasional Teknik Elektro, 11(1), 43–49. https://doi.org/10.25077/jnte.v11n1.899.2022
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