DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.106-112

Klasterisasi udang berdasarkan ukuran berbasis pemrosesan citra digital menggunakan metode CCA dan DBSCAN

Shrimps clusterization by size using digital image processing with CCA and DBSCAN

Universitas Jenderal Achmad Yani Yogyakarta, Indonesia

Received: 11 Aug 2019; Revised: 11 Feb 2020; Accepted: 14 Feb 2020; Available online: 15 Feb 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
The quality of farmed shrimps has several criteria, one of which is shrimp size. The shrimp selection was carried out by the contractor at the harvest time by grouping the shrimp based on their size. This study aims to apply digital image processing for shrimp clustering based on size using the connected component analysis (CCA) and density-based spatial clustering of applications with noise (DBSCAN) methods. Shrimp group images were taken with a digital camera at a light intensity of 1200-3200 lux. The clustering results were compared with clustering from direct observation by two experts, each of which obtained an accuracy of 79.81 % and 72.99 % so that the average accuracy of the method was 76.4 %.
Fulltext View|Download Email colleagues
Keywords: size clustering; vaname shrimp; image processing; connected component analysis; DBSCAN
Funding: Kementerian Riset dan Pendidikan Tinggi Republik Indonesia

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Most viewed articles
Perancangan dan Pengembangan Sistem Informasi Perpustakaan Berbasis Web (Studi Kasus Jurusan Sistem Komputer) Decision Support System for Thesis Graduation Recommendation Using AHP-TOPSIS Method Decision Support System in Recommending Best Unit in PDAM Tirta Lihou Using Promethee Method Expert System for Diagnosis of Plant Pest and Disease Horticulture with Forward and Backward Chaining Inference Teka-teki Unsur Kimia sebagai Media Pembelajaran Kimia Interaktif bagi siswa SMA Kelas X Berbasis Android More articles
Most cited articles
Flood Prediction with Ensemble Machine Learning using BP-NN and SVM Web Application of Lecturer Performance Assesment on Teaching Process: A Case Study at Internal Quality Assurance Agency of Institut Teknologi Padang Mobile Application of Expert System for Meningitis Early Diagnosis Backward Chaining Analysis Model in Detecting Game Addiction Levels in Children Pembuatan Aplikasi “Warung Keluarga” Sebagai Aplikasi E-Commerce Berbasis Web dan Mobile More cited articles
  1. L. Sahubawa, N. Khakim, and M. Lasindrang, “Kajian sebaran potensi ekonomi sumber daya,” Jurnal Teknosains, vol. 4, no. 2, pp. 101–198, 2015. doi: 10.22146/teknosains.7953
  2. N. T. K. Duy, N. D. Tu, T. H. Son, and L. H. D. Khanh, “Automated monitoring and control system for shrimp farms based on embedded system and wireless sensor network,” in 2015 International Conference on Electrical, Computer and Communication Technologies, Coimbatore, India, Mar. 2015, pp. 1–5. doi: 10.1109/ICECCT.2015.7226111
  3. P. S. Sneha and V. S. Rakesh, “Automatic monitoring and control of shrimp aquaculture and paddy field based on embedded system and IoT,” in 2017 International Conference on Inventive Computing and Informatics, Coimbatore, India, Nov. 2017, pp. 1085–1089. doi: 10.1109/ICICI.2017.8365307
  4. A. Rerkratn and A. Kaewpoonsuk, “ZigBee based wireless temperature monitoring system for shrimp farm,” in 2015 International Conference on Control, Automation and Systems, Busan, South Korea, Oct. 2015, pp. 428–431. doi: 10.1109/ICCAS.2015.7364953
  5. N. T. K. Duy, T. T. Hieu, and L. H. D. Khanh, “A versatile, low poweron monitoring and control system for shrimp farms based on NI myRIOand ZigBee network,” in 2015 International Conference on Computation of Power, Energy, Information and Communication, Chennai, India, Apr. 2015, pp. 282–287. doi: 10.1109/ICCPEIC.2015.7259476
  6. A. S. Herlambang, O. D. Nurhayati, and K. T. Martono, “Sistem pendeteksi kualitas daging dengan ekualisasi histogram dan thresholding berbasis Android,” Jurnal Teknologi dan Sistem Komputer, vol. 4, no. 2, pp. 404-413, 2016. doi: 10.14710/jtsiskom.4.2.2016.404-413
  7. A. Muzami, O. D. Nurhayati, and K. T. Martono, “Aplikasi identifikasi citra telur ayam omega-3 dengan metode segmentasi region of interest berbasis Android,” Jurnal Teknologi dan Sistem Komputer, vol. 4, no. 2, pp. 380-388, 2016. doi: 10.14710/jtsiskom.4.2.2016.380-388
  8. A. Priadana and A. W. Murdiyanto, “Metode SURF dan FLANN untuk identifikasi nominal uang kertas rupiah tahun emisi 2016 pada variasi rotasi,” Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 1, pp. 19-24, 2019. doi: 10.14710/jtsiskom.7.1.2019.19-24
  9. X. Zhang, L. Duan, L. Ma, and J. Wu, “Text extraction for historical Tibetan document images based on connected component analysis and corner point detection,” Communications in Computer and Information Science, vol. 772, pp. 545–555, 2017. doi: 10.1007/978-981-10-7302-1_45
  10. R. S. Narasimhan, A. Vengadarajan, and K. R. Ramakrishnan, “Design of connected component analysis based clustering of CFAR image in pulse Doppler radars,” in 2017 Aerospace Conference, 2017, Big Sky, USA, Mar. 2017, pp. 1–6. doi: 10.1109/AERO.2017.7943725
  11. I. Ruslianto, “Klasifikasi telur ayam dan telur burung puyuh menggunakan metode connected component analysis,” SISFOTENIKA, vol. 3, no. 1, pp. 41–50, 2013
  12. F. Spagnolo, F. Frustaci, S. Perri, and P. Corsonello, “An efficient connected component labeling architecture for embedded systems,” Journal of Low Power Electronics and Applications, vol. 8, no. 1, pp. 1-7, 2018. doi: 10.3390/jlpea8010007
  13. M. J. Klaiber, D. G. Bailey, Y. O. Baroud, and S. Simon, “A resource-efficient hardware architecture for connected component analysis,” IEEE Transactions on Circuits System for Video Technology, vol. 26, no. 7, pp. 1334–1349, 2016. doi: 10.1109/TCSVT.2015.2450371
  14. Q. Chen, K. K. F. Yuen, and C. Guan, “Towards a hybrid approach of self-organizing map and density-based spatial clustering of applications with noise for image segmentation,” in 2017 International Conference on Developments in eSystems Engineering, Paris, France, Jun. 2017, pp. 238–241. doi: 10.1109/DeSE.2017.24
  15. V. Zilvan, “Ekstraksi objek pada citra radar FM-CW dengan metode DBSCAN,” INKOM: Jurnal Informatika, Sistem Kendali, dan Komputer, vol. 9, no. 1, pp. 29–38, 2015
  16. B. Khalil and C. Ali, “Density-based spatial clustering of application with noise algorithm for the classification of solar radiation time series,” in 2016 International Conference on Modelling, Identification and Control, Algiers, Algeria, Nov. 2016, pp. 279–283. doi: 10.1109/ICMIC.2016.7804123
  17. S. A. D. Budiman, D. Safitri, and D. Ispriyanti, “Perbandingan metode k-means dan metode DBSCAN pada pengelompokan rumah kost mahasiswa di kelurahan Tembalang Semarang,” Jurnal Gaussian, vol. 5, no. 4, pp. 757–762, 2016
  18. M. Tanzil Furqon and L. Muflikhah, “Clustering the potential risk of tsunami using density-based spatial clustering of application with noise (DBSCAN),” Journal of Enviromental Engineering and Sustainable Technology, vol. 3, no. 1, pp. 1–8, 2016. doi: 10.21776/ub.jeest.2016.003.01.1
  19. S. Kamseno and B. Satya, “Analisis data world development indicators menggunakan cluster data mining,” Semnasteknomedia Online, vol. 5, no. 1, pp. 2-1–121, 2017
  20. K. Dawson-Howe, A practical introduction to computer vision with OpenCV, enhanced edition, 1st ed. West Suusex: Wiley, 2014
  21. S. Suyanto, Data mining untuk klasifikasi dan klasterisasi data. Bandung: Informatika, 2017

Last update:

  1. Metode k-means clustering dan morfologi berbasis computer vision dan analisis regresi untuk aplikasi sistem grading udang Vaname

    Sumardi Sumardi, Syahfrizal Tahcfulloh. Jurnal Teknologi dan Sistem Komputer, 11 (1), 2024. doi: 10.14710/jtsiskom.2023.14529

Last update: 2024-11-21 10:08:06

No citation recorded.