Detecting Papaya Fruit Ripeness Level Based on Color Features Using the HSI Color Space Transformation Method

Authors

  • Nia Zanah Universitas Islam Negeri Sumatera Utara
  • Fadhilah Ramadhani Nst Universitas Islam Negeri Sumatera Utara
  • Muhammad Yudha Pratama Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.55537/bigint.v1i2.781

Keywords:

color features, HSI color space transformation, maturity level

Abstract

Papaya fruit is one of the kings of fruit in Indonesia. It is said to be the king of fruit because it is rich in benefits and has good nutritional and vitamin content. Grouping the maturity of papaya fruit is very important. The difficulty in clarifying the ripeness of papaya fruit is based on its color features, and the grouping of papaya fruit ripeness is divided into several categories, namely ripe, semi-ripe and unripe. This research aims to predict the level of ripeness of papaya fruit using the K-Nearest Neighbor method using features obtained from extraction results with RGb and HSI images. The system that has been created can classify papaya fruit images into raw, semi-ripe and ripe classes. Programming includes identifying needs, analyzing problems, selecting algorithms, and determining the data structure to be used. The aim of program design is to create a good and effective design before starting to implement the program. The first stage is to design the program using the GUI tool from the Matlab application. In the second stage, the source code must be entered so that the program can be run. Papaya fruit samples that were tested for ripeness were obtained from taking images using several smartphone cameras and at different times. Some were taken during the day, some at night. Table 4. Shows that all of the images of papaya fruit samples are in accordance with the application of papaya ripeness levels. With test results showing that test accuracy reaches a perfect level of 100%. This indicates that the testing system used is able to accurately detect the ripeness of papaya fruit. The use of the K-Nearst Neighbor (K-NN) method has been successfully carried out in this research.

Downloads

Download data is not yet available.

References

K. B. D. R. Nur Widyasari, U. D. Rosiani, and A. N. Pramudhita, "Implementation of a Papaya Fruit Ripeness Level Detection System Using the RGB Method," Smatika J., vol. 11, no. 01, pp. 32–36, 2021, doi: 10.32664/smatica.v11i01.536.

Alfian Firlansyah, Andi Baso Kaswar, and Andi Akram Nur Risal, "Classification of Papaya Fruit Ripeness Levels Based on Color Features Using ANN," Techno Xplore J. Computer Science. and Technol. Inf., vol. 6, no. 2, pp. 55– 60, 2021, doi: 10.36805/technoxplore.v6i2.1438.

K. Suketi, R. Poerwanto, and S. Sujiprihati, "Physical and Chemical Characteristics of Papaya Fruit at Different Maturity Stages Physical and Chemical Characteristics of Papaya at Different Maturity Stages," Agronomy, vol. 38, no. 1, pp. 60–66, 2020.

Ellif, S. H. Sitorus, and R. Hidayati, “Naïve Bayes 1.,” Coding J. Komput. and Appl., vol. 09, no. 01, pp. 66–75, 2021.

Y. F. Br Tarigan, K. Andriani, R. Rosnelly, and W. Wanayumini, "Implementation of the HSI Method in Color Space Transformation in Detecting the Ripeness of Shrimp Mango Fruit," J. Media Inform. Budidharma, vol. 6, no. 4, p. 2257, 2022, doi: 10.30865/mib.v6i4.4547.

Doni, "Implementation of Image Quality Improvement Using the Lucy-Richardson Method," Maj. Ilm. CORE, vol. 14, no. September, pp. 275–278, 2019.

E. Syaefulloh and H. Purwadaria, "IDENTIFICATION OF MATURITY AND MATURITY LEVELS OF IPB 1 PAPAYA (Carica papaya L.) USING DIGITAL IMAGE PROCESSING AND ARTIFICIAL NEURAL NETWORKS," Agritech J. Fak. Technol. Pertan. UGM, vol. 27, no. 2, pp. 75–81, 2007.

M. L. Taris, W. D. Widodo, and K. Suketi, "Maturity Criteria for IPB Callina Papaya Fruit (Carica papaya L.) from Several Harvest Ages," J. Hortik. Indonesia., vol. 6, no. 3, p. 172, 2015, doi: 10.29244/jhi.6.3.172-176.

S. P. Adenugraha, V. Arinal, and D. I. Mulyana, "Classification of Ambon Banana Ripeness Using KNN and PCA Methods Based on RGB and HSV Images," J. Media Inform. Budidharma, vol. 6, no. 1, p. 9, 2022, doi: 10.30865/mib.v6i1.3287.

T. Banana Fruit Ripeness Based on Banana Skin Image Color Features Sakir et al., "Application of the HSI Color Space Transformation Method for Detection," vol. XIV, no. 3, pp. 243–254, 2020.

O. N. Shpakov and G. V. Bogomolov, “Technogenic activity of man and local sources of environmental pollution,” Stud. Environ. Sci., vol. 17, no. C, pp. 329–332, 1981, doi: 10.1016/S0166-1116(08)71924-1.

S. Computer, D. Selatan, K. Denpasar, and D. Kesegaran, "Detection of Freshness of Apples, Bananas, and Oranges Using the HSI Color Space Transformation Method and K-Nearest Neighbor," vol. 8, no. 1, pp. 1–10, 2024.

W. P. Atmaja and V. Lusiana, "CLASSIFICATION OF PEKALONGAN BATIK TYPES USING HSI IMAGE WITH THE K-NEAREST NEIGHBOR METHOD," vol. 8, no. April, pp. 1–8, 2023.

D. Wandi, F. Fauziah, and N. Hayati, "Detection of Wiltiness in Roses Using HSI and HSV Color Space Transformation Methods," STRING (Ris. and Inov. Teknol Writing Unit., vol. 5, no. 3, p. 333, 2021, doi: 10.30998/string.v5i3.8464.

R. Azhar, A. Z. Arifin, and W. N. Khotimah, "Integration of Density-Based Clustering and HMRF-EM in HSI Color Space for Tuna Fish Image Segmentation," Inspir. J. Technol. Inf. and Commun., vol. 6, no. 1, pp. 28–37, 2016, [Online]. Available: https://jurnal.akba.ac.id/index.php/inspiration/article/view/89

D. I. Muhammad, E. Ermatita, and N. Falih, "Using K-Nearest Neighbor (KNN) to Classify Starfruit Images Based on Color Features," Inform. J. Computer Science., vol. 17, no. 1, p. 9, 2021, doi: 10.52958/iftk.v17i1.2132.

S. R. Raysyah, Veri Arinal, and Dadang Iskandar Mulyana, "Classification of Coffee Fruit Maturity Levels Based on Color Detection Using Knn and Pca Methods," JSiI (Journal of Information Systems), vol. 8, no. 2, pp. 88–95, 2021, doi: 10.30656/jsii.v8i2.3638.

F. Liantoni, "Leaf Classification with Improved Image Features Using the K-Nearest Neighbor Method," J. Ultim., vol. 7, no. 2, pp. 98–104, 2016, doi: 10.31937/ti.v7i2.356.

M. M. Baharuddin, H. Azis, and T. Hasanuddin, "Performance Analysis of the K-Nearest Neighbor Method for Identifying Glass Types," Ilk. J. Ilm., vol. 11, no. 3, pp. 269–274, 2019, doi: 10.33096/ilkom.v11i3.489.269-274.

B. Yanto, J. Jufri, A. Lubis, B. H. Hayadi, and E. Armita, NST, "Clarification of Pineapple Ripeness Using Hue Saturation Intensity (Hsi) Color Space," INOVTEK Polbeng - Seri Inform., vol. 6, no. 1, p. 135, 2021, doi: 10.35314/isi.v6i1.1882.

A. I. Thoriq, M. H. Zuhri, P. Purwanto, P. Pujiono, and H. A. Santoso, "Classification of Banana Maturity Levels Based on Skin Image with HSI Color Space Transformation Features Using the K-NN Method," J. Dev. Res., vol. 6, no. 1, pp. 11–15, 2022, doi: 10.28926/jdr.v6i1.200.

O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto, and D. R. I. M. Setiadi, "Tomatoes classification using K-NN based on GLCM and HSV color space," Proc. - 2017 Int. Conf. Innov. Creat. Inf. Technol. Comput. Intel. IoT, ICITech 2017, vol. 2018-January, pp. 1–6, 2018, doi: 10.1109/INNOCIT.2017.8319133.

I. F. Astuti, F. D. Nuryanto, P. P. Widagdo, and D. Cahyadi, "Oil palm fruit ripeness detection using K-Nearest neighbor," J. Phys. Conf. Ser., vol. 1277, no. 1, 2019, doi: 10.1088/1742-6596/1277/1/012028.

H. Edha, S. H. Sitorus, and U. Ristian, "APPLICATION OF THE HUE SATURATION INTENSITY (HSI) COLOR SPACE TRANSFORMATION METHOD FOR DETECTING THE RIPENESS OF HENDRYANTO'S SWEET HARUM MANGO FRUIT," J. Komput. and Appl., vol. 8, no. 1, pp. 1–10, 2020.

I. B. Suban, A. Paramartha, M. Fortwonatus, and A. J. Santoso, "Identification of the Maturity Level of Carica Papaya Using the K-Nearest Neighbor," J. Phys. Conf. Ser., vol. 1577, no. 1, 2020, doi: 10.1088/1742-6596/1577/1/012028.

Downloads

Published

2023-07-31

How to Cite

Zanah, N., Nst, F. R. ., & Pratama, M. Y. . (2023). Detecting Papaya Fruit Ripeness Level Based on Color Features Using the HSI Color Space Transformation Method. Bigint Computing Journal, 1(2), 77–84. https://doi.org/10.55537/bigint.v1i2.781

Issue

Section

Article