KLASIFIKASI TINGKAT KEMATANGAN CABAI MERAH KERITING MENGGUNAKAN SVM MULTICLASS BERDASARKAN EKSTRAKSI FITUR WARNA
DOI:
https://doi.org/10.23960/jitet.v12i3.4430Abstract Views: 1167 File Views: 1036
Abstract
The utilization of digital image processing holds significant potential for classifying the ripeness of curly red peppers (Capsicum annuum L.). This study aims to develop an automatic classification method using multiclass Support Vector Machine (SVM) with a linear kernel. Images of peppers, captured using a smartphone camera, were categorized into three classes: ripe, unripe, and semi-ripe. Features such as mean, variance, and range from the RGB color space were extracted for training and testing data. Testing was conducted by dividing the data into training and test sets and employing 10-fold cross-validation. Results demonstrated a classification accuracy of 98.33%. The combination of mean, variance, and range features significantly improved accuracy compared to single features. This research demonstrates the effectiveness of the developed method and its applicability in automated classification systems to support the agricultural sector.Downloads
References
Eni Istiyanti, “Efisiensi Pemasaran Cabai Merah Keriting Di Kecamatan Ngemplak Kabupaten Sleman (The Marketing Efficiency Of Red Chili In Ngemplak Regency Sleman Distric),” J. Pertan. MAPETA, vol. XII, no. 2, pp. 116–124, 2015.
N. T. Anggraeni and A. Fadlil, “SISTEM IDENTIFIKASI CITRA JENIS CABAI (Capsicum Annum L.) MENGGUNAKAN METODE KLASIFIKASI CITY BLOCK DISTANCE,” JSTIE (Jurnal Sarj. Tek. Inform., vol. 1, no. 2, pp. 409–418, 2013.
M. Muchtar, Y. P. Pasrun, R. Rasyid, N. Miftachurohmah, and M. Mardiawati, “PENERAPAN METODE NAÏVE BAYES DALAM KLASIFIKASI KESEGARAN IKAN BERDASARKAN WARNA PADA CITRA AREA MATA,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 1, Jan. 2024, doi: 10.23960/jitet.v12i1.3879.
M. Muchtar and R. A. Muchtar, “Integrasi fitur warna, tekstur dan renyi fraktal untuk klasifikasi penyakit daun kentang menggunakan linear discriminant analysis,” J. Mnemon., vol. 7, no. 1, pp. 77–84, 2024.
F. Liantoni and F. N. Annisa, “Fuzzy K-Nearest Neighbor Pada Klasifikasi Kematangan Cabai Berdasarkan Fitur Hsv Citra,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 3, no. 2, pp. 101–108, 2018, doi: 10.29100/jipi.v3i2.851.
X. Chao, G. Sun, H. Zhao, M. Li, and D. He, “Identification of apple tree leaf diseases based on deep learning models,” Symmetry (Basel)., vol. 12, no. 7, Jul. 2020, doi: 10.3390/sym12071065.
M. Muchtar and L. Cahyani, “Klasifikasi Citra Daun dengan Metode Gabor Co-Occurence,” Ultim. Comput., vol. VII, no. 2, pp. 39–47, 2015.
M. Muchtar and R. A. Muchtar, “Perbandingan Metode KNN dan SVM Dalam Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dan Fitur Statistik,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 2, pp. 876–884, 2024.
N. El-Bendary, E. El Hariri, A. E. Hassanien, and A. Badr, “Using machine learning techniques for evaluating tomato ripeness,” Expert Syst. Appl., vol. 42, no. 4, pp. 1892–1905, 2015, doi: 10.1016/j.eswa.2014.09.057.
H. Khotimah, N. Nafi’iyah, and Masruroh, “Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dengan KNN,” ELTI J. Elektron. List. dan Teknol. Inf. Terap., vol. 2, no. 1, pp. 1–7, Dec. 2019, [Online]. Available: https://ojs.politeknikjambi.ac.id/elti
M. H. Hanafi, N. Fadillah, and A. Insan, “Optimasi Algoritma K-Nearest Neighbor untuk Klasifikasi Tingkat Kematangan Buah Alpukat Berdasarkan Warna,” It J. Res. Dev., vol. 4, no. 1, pp. 10–18, 2019, doi: 10.25299/itjrd.2019.vol4(1).2477.
T. Purwaningsih, I. A. Anjani, and P. B. Utami, “Convolutional Neural Networks Implementation for Chili Classification,” Proceeding - 2018 Int. Symp. Adv. Intell. Informatics Revolutionize Intell. Informatics Spectr. Humanit. SAIN 2018, pp. 190–194, 2018, doi: 10.1109/SAIN.2018.8673373.
J. Pardede, M. G. Husada, A. N. Hermana, and S. A. Rumapea, “Fruit ripeness based on RGB, HSV, HSL, L ab color feature using SVM,” in 2019 International Conference of Computer Science and Information Technology (ICoSNIKOM), Nov. 2019, pp. 1–5.
R. Ali, R. Hardie, and A. Essa, “A Leaf Recognition Approach to Plant Classification Using Machine Learning,” Proc. IEEE Natl. Aerosp. Electron. Conf. NAECON, vol. 2018-July, pp. 431–434, 2018, doi: 10.1109/NAECON.2018.8556785.
V. Soelaiman and A. Ernawati, “Pertumbuhan dan Perkembangan Cabai Keriting (Capsicum annuum L.) secara In Vitro pada beberapa Konsentrasi BAP dan IAA,” Bul. Agrohorti, vol. 1, no. 1, p. 62, 2013, doi: 10.29244/agrob.1.1.62-66.
K. Science, “Capsicum annuum L.,” Plants of the World Online, 2023. https://powo.science.kew.org/taxon/urn:lsid:ipni.org:names:316944-2 (accessed May 22, 2024).
M. Muchtar and R. Riska, “DETEKSI AREA KERUSAKAN PADA CITRA TERUMBU KARANG AKIBAT CORAL BLEACHING BERBASIS PENGOLAHAN CITRA DIGITAL,” J. Innov. Futur. Technol. P-ISSN, vol. 5, pp. 2656–1719, 2023.
M. Muchtar, “Penggabungan Fitur Dimensi Fraktal dan Lacunarity untuk Klasifikasi Daun,” Institut Teknologi Sepuluh Nopember, Surabaya, 2015.
M. Muchtar, N. Suciati, and C. Fatichah, “Fractal Dimension and Lacunarity Combination for Plant Leaf Classification,” J. Ilmu Komput. dan Inf., vol. 9, no. 2, p. 96, Jun. 2016, doi: 10.21609/jiki.v9i2.385.
