Classification of Melinjo Fruit Levels Using Skin Color Detection With RGB and HSV
Keywords:Digital Image, Classification Of Ripeness Of Melinjo Fruit, RGB, HSV
This study aims to detect the ripeness of melinjo fruit using digital image method. Structured identification or division using image processing and computer vision requires the socialization of patterns based on training datasets. Melinjo (Gnetum gnemon L.) is a plant that can grow anywhere, such as yards, gardens, or on the sidelines of residential areas, as a result, produces melinjo into a plant that has relatively large potential to be developed. The process of image processing and pattern socialization is a highly developed research study. Starting based on the process of socializing an object, or a structured division of the object and about detecting the level of fruit maturity. The structured division process regarding ripeness into 3 classes, namely: raw, half-cooked and ripe where the process is carried out using Google Collaboratory which processes the RGB color space to HSV. In this study, the testing method for the system that will be used is a functional test where the test is carried out only by observing the execution results through test data and checking the functionality of the system being developed. The level of accuracy obtained from this study is 98.0% correct.
Ayllon, MA, Mendoza, JJ, & Tomas, MC (nd). Detection of ripeness of whole local fruit using Convolutional Neural Networks Through Image Processing Machine Translated by Google . 145–148.
Basak, J. K., Madhavi, B. G. K., Paudel, B., Kim, N. E., & Kim, H. T. (2022). Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models. Foods, 11(14), 2086.
Cruz, J. C. D., Garcia, R. G., Dimaunahan, E. D., Labaclado, J. J., Reyes, G. A. B., Riomero, H. M. C. P., & Salamatin, P. M. (2019). Eczema, Hives and Psoriasis Detection with the Application of Local Binary Pattern, Color Histogram, SVM and RGB-HSV Color Space. In Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology (pp. 160-165).
Din, A. F., & Abdul Nasir, A. S. (2021). Automated cells counting for leukaemia and malaria detection based on RGB and HSV colour spaces analysis. In Proceedings of the 11th National Technical Seminar on Unmanned System Technology 2019 (pp. 981-996). Springer, Singapore.
Liantoni, F., & Annisa, FN (2018). Fuzzy K-Nearest Neighbor on Chili Maturity Classification Based on Hsv Image Features. JIPI (Scientific Journal of Informatics Research and Learning) , 3 (2), 101–108. https://doi.org/10.29100/jipi.v3i2.851
Herng, O. W., Nasir, A. S. A., Chin, O. B., & Tan, E. S. M. M. (2021, November). Harumanis Mango Leaves Image Segmentation on RGB and HSV Colour Spaces using Fast k-Means Clustering. In Journal of Physics: Conference Series (Vol. 2107, No. 1, p. 012068). IOP Publishing.
Kulakova, A. D., Galkin, V., & Makarenko, A. V. (2022). Methoarial; analysis of methods of colour calibration of images using rgb ang hsv colour spaces in tasks of intelligent computer vision (for example, images ob-tained in industrial greenhouses. Upravlenie Bol'shimi Sistemami, 97, 87-107.
Mohsin, R. H., Daway, H. G., & Rashid, H. G. (2021). Automatic Detection of Smoke in Videos Relying on Features Analysis Using RGB and HSV Colour Spaces. In Data Science and Security (pp. 182-190). Springer, Singapore.
Mujahidin, S. (2015). Skin Color Classification based on the RGB Color Space. National Seminar on Information Technology Applications (SNATI) , 1(1), 17–19. http://journal.uii.ac.id/index.php/Snati/article/view/3530
Pambudi, SW, & Sustiono. (2015). Design and Build of Dragon Fruit Ripening Quality Selection Tool Using Image Processing Technique with HSV Image Segmentation Method. Journal of Science And Informatics , 1(2), 28–37.
Phuangsaijai, N., Jakmunee, J., & Kittiwachana, S. (2021). Investigation into the predictive performance of colorimetric sensor strips using RGB, CMYK, HSV, and CIELAB coupled with various data preprocessing methods: A case study on an analysis of water quality parameters. Journal of Analytical Science and Technology, 12(1), 1-16.
Sanusi, H., S., SH, & Susetianingtias, DT (2019). Making Leaf Image Classification Applications Using Rgb And Hsv Color Spaces. Scientific Journal of Computer Informatics , 24 (3), 180–190. https://doi.org/10.35760/ik.2019.v24i3.2323
Siregar, YDI (2014). Utilization of Red Melinjo Skin Extract (Gnetum gnemon) as a Natural Colorant for Lipstick Making. Journal of VALENCY Chemistry , 1(4), 98–108. https://doi.org/10.15408/jkv.v0i0.3607
Wardani, VR, Fatimah, S., Nadia, & Cahyani, IM (2019). Melinjo peel ethanol extract activity ( Indonesian Pharmaceutical Media , 14 (1), 1466–1470.
Whidhiasih, RN, Wahanani, NA, & Supriyanto. (2013). Classification of Starfruit Based on RED-GREEN-BLUE Imagery. Journal of Computer Science Research, Systems Embedded & Logic , 1(1), 29–35.
Wibowo, JS (2011). Detection and Classification of Image Based on Skin Color Using HSV. Journal of Information Technology DYNAMIC. 16 (2), 118–123.
Widiantie, R., Setiawati, I., & Handayani. (2021). Utilization of melinjo and melinjo skin is an innovative product in improving the economy of the Sumbakeling village community . 58–62.
Yogi, M. (2016). Application of watermelon ripeness detection based on RGB value using thresholding method . JURIKOM: Journal of Computer Research , 3(6), 84–89.