Segmentasi Tingkat Kematangan Buah Pisang Cavendish Sangat Matang Berdasarkan Warna Menggunakan Watershed

 (*)Ageng Muktianto Mail (Universitas Nusa Mandiri, Jakarta, Indonesia)
 Vidya Indriyani (Universitas Nusa Mandiri, Jakarta, Indonesia)

(*) Corresponding Author

Abstract

The Cavendish banana is considered to be one of the most sought-after export-oriented fruit in Indonesia. The large market size of export fruits, especially Cavendish bananas, opens opportunities for Indonesia to raise both its product quantity and quality in order to increase its competitiveness. Various methods have been conducted to increase the quantity and quality of Indonesia's cavendish bananas, one of which is the adoption of the image processing technology. This method aims to simplify and resolve cultivation and processing issues regarding Cavendish bananas, among other things by minimizing human error in determining fruit ripeness (which is traditionally conducted by manual labor). This research uses HSV segmentation and a Watershed algorithm in segmenting images of 50 ripe Cavendish bananas with 40 training data and 10 testing data. Based on our research, we founda out that ripe Cavendish bananas have 42% red, 37% green, and 21% blue in average, with an accuracy rate of 65%

Keywords


Cavendish Banana; Level of Ripeness; HSV; Watershed

Full Text:

PDF


Article Metrics

Abstract view : 894 times
PDF - 590 times

References

S. V. Anstalt, “Food and agriculture organization of the United Nations,” 2013.

H. E. Setyowati, “Panen Perdana Tanaman Pisang Cavendish Pengembangan Hortikultura Berorientasi Ekspor,” www.ekon.go.id, 2020. https://ekon.go.id/publikasi/detail/606/panen-perdana-tanaman-pisang-cavendish-pengembangan-hortikultura-berorientasi-ekspor (accessed Jul. 08, 2021).

M. F. Ajizi, D. Syauqy, and M. H. H. Ichsan, “Klasifikasi Kematangan Buah Pisang Berbasis Sensor Warna dan Sensor Load Cell Menggunakan Metode Naive Bayes,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. e-ISSN, vol. 2548, p. 964X, 2019.

M. R. Bakhtar and E. R. Swedia, “APLIKASI PENGOLAHAN CITRA UNTUK MENENTUKAN TINGKAT KEMATANGAN BUAH PISANG DENGAN MENGGUNAKAN RUANG WARNA HUE,” J. Ilm. Teknol. dan Rekayasa, vol. 22, no. 1, 2017.

A. Chan, P. Liem, N. P. Wong, and T. Gunawan, “Segmentasi buah menggunakan metode k-means clustering dan identifikasi kematangannya menggunakan metode perbandingan kadar warna,” J. SIFO Mikroskil, vol. 15, no. 2, pp. 91–100, 2014.

A. Kumar, P. Kumar, and P. Hod, “A new framework for color image segmentation using watershed algorithm,” Comput. Eng. Intell. Syst., vol. 2, no. 3, pp. 41–46, 2011.

F. Mazen and A. A. Nashat, “Ripeness classification of bananas using an artificial neural network,” Arab. J. Sci. Eng., vol. 44, no. 8, pp. 6901–6910, 2019.

W. Castro, J. Oblitas, M. De-La-Torre, C. Cotrina, K. Bazán, and H. Avila-George, “Classification of cape gooseberry fruit according to its level of ripeness using machine learning techniques and different color spaces,” IEEE access, vol. 7, pp. 27389–27400, 2019.

A. S. Sinaga and E. Marpaung, “Segmentasi Warna HSV Telapak Tangan Untuk Deteksi Bakteri Pada Pendemi Covid 19,” Fountain Informatics J., vol. 5, no. 3, pp. 1–5, 2020.

S. Widayati and I. P. Wardhani, “Analisa Segmentasi Warna Hsv Pada Citra Video Dengan Metode Threshold,” Pros. SeNTIK, vol. 4, no. 1, pp. 339–345, 2020.

A. P. A. Sty and N. Astrianda, “Klasifikasi Kematangan Buah Tomat Dengan Variasi Model Warna Menggunakan Support Vector Machine,” VOCATECH Vocat. Educ. Technol. J., vol. 1, no. 2, pp. 44–51, 2020.

A. Sinha, “a New Approach of Watershed Algorithm Using Distance Transform Applied To Image Segmentation,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 1, no. 2, 2013.

M. H. Malik, T. Zhang, H. Li, M. Zhang, S. Shabbir, and A. Saeed, “Mature tomato fruit detection algorithm based on improved HSV and watershed algorithm,” IFAC-PapersOnLine, vol. 51, no. 17, pp. 431–436, 2018.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Segmentasi Tingkat Kematangan Buah Pisang Cavendish Sangat Matang Berdasarkan Warna Menggunakan Watershed

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 JURIKOM (Jurnal Riset Komputer)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JURIKOM (Jurnal Riset Komputer)
Publish by Universitas Budi Darma (before STMIK BUDI DARMA (P3M))
Email: jurikom.stmikbd@gmail.com

Creative Commons License
 This work is licensed under a Creative Commons Attribution 4.0 International.