Article Sidebar

Submitted
17 June 2023
Accepted
21 June 2023
Published
28 June 2023
Download
PDF (Bahasa Indonesia)
Statistic
Read Counter : 557 Download : 183

Downloads

Download data is not yet available.

Main Article Content

Abstract

Corona Virus Disease 2019 (covid-19) merupakan pandemi dunia yang menimbulkan berbagai kerugian di dunia. Diagnosis covid-19 yang valid memerlukan waktu yang cukup lama dan hasil ini tidak sepenuhnya akurat. Salah satu cara untuk meningkatkan hasil akurasi adalah dengan menggunakan image classification. k-Nearest Neighbor (kNN) adalah salah satu Teknik klasifikasi yang paling banyak digunakan untuk melakukan pekerjaan tersebut, hanya saja kNN masih memiliki kelemahan. Untuk mengatasi kelemahan pada kNN, maka dilakukan modifikasi dengan menambahkan local mean dan distance weight, di mana varian kNN ini dikenal dengan nama Local Mean Distance Weight k-Nearest Neighbor (LMDWkNN). Oleh sebab itu, penelitian kali mencoba membandingkan kinerja kedua algoritma ini untuk melakukan image classification pada citra covid-19. Kinerja diukur berdasarkan nilai akurasi, precision, dan recall, di mana dari hasil pengujian terbukti bahwa kinerja LMDWkNN lebih baik dari pada kinerja kNN. Algoritma LMDWkNN mengalami peningkatan rata-rata sebesar 3.5% untuk nilai akurasi, 2.89% untuk precision, dan 3.54% untuk recall. Meskipun begitu kNN tetap mampu menghasilkan kinerja yang sama baiknya yang mana kinerja kNN akan sangat bergantung dari nilai k yang digunakan

Keywords

Corona Virus Disease 2019 (covid-19) Image Classification k-Nearest Neighbor (kNN) Local Mean Distance Weight k-Nearest Neighbor (LMDWkNN) Performance

Article Details

License

Copyright (c) 2023 Jurnal Komputer Terapan

Creative Commons License

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

Author Biography

Sapriadi Sapriadi, institut kesehatan helvetia

Farmasi, Fakultas Farmasi dan Kesehatan, Institut Kesehatan Helvetia, Medan
How to Cite
Sapriadi, S. (2023). Perbandingan Kinerja k-Nearest Neighbor dan Local Mean Distance k-Nearest Neighbor Pada Data Citra Covid-19. Jurnal Komputer Terapan, 9(1), 74–81. https://doi.org/10.35143/jkt.v9i1.6019
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Kementerian Kesehatan Republik Indonesia, “Peran Ditjen Kesmas Daman Pandemi COVID 19 2020-2021,” Jakarta, 2021.
  2. A. Syauqi, “Jalan panjang covid19 (Sebuah Refleksi Dikala Wabah Merajalela Berdampak Pada Perekonomian),” JKUBS J. Chem. Inf. Model., vol. 1, no. 1, pp. 1–19, 2020.
  3. N. Mamoon and G. Rasskin, “COVID-19,” 2019. [Online]. Available: https://www.covidvisualizer.com/
  4. M. F. Asriansyah, “Pandemi Covid 19 dan Upaya Pencegahan,” 2022. https://www.djkn.kemenkeu.go.id/artikel/baca/15799/Pandemi-Covid-19-dan-Upaya-Pencegahan.html
  5. E. Martínez Chamorro, A. Díez Tascón, L. Ibáñez Sanz, S. Ossaba Vélez, and S. Borruel Nacenta, “Radiologic diagnosis of patients with COVID-19,” Radiologia, vol. 63, no. 1, pp. 56–73, 2021, doi: 10.1016/j.rx.2020.11.001.
  6. S. Ebrahimzadeh et al., “Thoracic imaging tests for the diagnosis of COVID-19,” 2022. doi: 10.1002/14651858.CD013639.pub5.
  7. S. Kamthan, H. Singh, and T. Meitzler, “Hierarchical fuzzy deep learning for image classification,” Memories - Mater. Devices, Circuits Syst., vol. 2, no. June, p. 100016, 2022, doi: 10.1016/j.memori.2022.100016.
  8. X. Zhang, H. Xiao, R. Gao, H. Zhang, and Y. Wang, “K-nearest neighbors rule combining prototype selection and local feature weighting for classification,” Knowledge-Based Syst., vol. 243, 2022, doi: 10.1016/j.knosys.2022.108451.
  9. Y. Guo, S. Han, Y. Li, C. Zhang, and Y. Bai, “K-Nearest Neighbor combined with guided filter for hyperspectral image classification,” Procedia Comput. Sci., vol. 129, pp. 159–165, 2018, doi: 10.1016/j.procs.2018.03.066.
  10. J. Wang, P. Neskovic, and L. N. Cooper, “Improving nearest neighbor rule with a simple adaptive distance measure,” Pattern Recognit. Lett., vol. 28, no. 2, pp. 207–213, 2007, doi: 10.1016/j.patrec.2006.07.002.
  11. J. A. Romero-del-Castillo, M. Mendoza-Hurtado, D. Ortiz-Boyer, and N. García-Pedrajas, “Local-based k values for multi-label k-nearest neighbors rule,” Eng. Appl. Artif. Intell., vol. 116, no. June, p. 105487, 2022, doi: 10.1016/j.engappai.2022.105487.
  12. S. Ougiaroglou and G. Evangelidis, “Fast and accurate k-nearest neighbor classification using prototype selection by clustering,” Proc. 2012 16th Panhellenic Conf. Informatics, PCI 2012, no. i, pp. 168–173, 2012, doi: 10.1109/PCi.2012.69.
  13. L. Cai, Y. Song, T. Liu, and K. Zhang, “A Hybrid BERT Model That Incorporates Label Semantics via Adjustive Attention for Multi-Label Text Classification,” IEEE Access, vol. 8, pp. 152183–152192, 2020, doi: 10.1109/ACCESS.2020.3017382.
  14. K. Li, H. Wang, W. Wang, F. Wang, and Z. Cui, “Improving artificial bee colony algorithm using modified nearest neighbor sequence,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, pp. 8807–8824, 2022, doi: 10.1016/j.jksuci.2021.10.012.
  15. X. Wu et al., Top 10 algorithms in data mining, vol. 14, no. 1. 2008. doi: 10.1007/s10115-007-0114-2.
  16. G. I. Okolo, S. Katsigiannis, and N. Ramzan, “IEViT: An enhanced vision transformer architecture for chest X-ray image classification,” Comput. Methods Programs Biomed., vol. 226, p. 107141, 2022, doi: 10.1016/j.cmpb.2022.107141.
  17. S. Suyanto, P. E. Yunanto, T. Wahyuningrum, and S. Khomsah, “A multi-voter multi-commission nearest neighbor classifier,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 6292–6302, 2022, doi: 10.1016/j.jksuci.2022.01.018.
  18. A. Islam, S. B. Belhaouari, A. U. Rehman, and H. Bensmail, “K Nearest Neighbor OveRsampling approach: An open source python package for data augmentation,” Softw. Impacts, vol. 12, no. February, p. 100272, 2022, doi: 10.1016/j.simpa.2022.100272.
  19. M. Kumar, N. K. Rath, A. Swain, and S. K. Rath, “Feature Selection and Classification of Microarray Data using MapReduce based ANOVA and K-Nearest Neighbor,” Procedia Comput. Sci., vol. 54, pp. 301–310, 2015, doi: 10.1016/j.procs.2015.06.035.
  20. P. Nair et al., “A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method,” Int. J. Sci. Technol. Res., vol. 28, no. 3, pp. 221–226, 2020, doi: 10.1016/j.imu.2021.100825.
  21. K. U. Syaliman, Yuliska, and N. F. Najwa, “Seleksi Fitur Menggunakan Pendekatan k-Nearest Neighbor,” J. Sist. Inf. dan Teknol. Jar., vol. 3, no. 1, pp. 8–13, 2022.
  22. K. U. Syaliman, A. Labellapansa, and A. Yulianti, “Improving the Accuracy of Features Weighted k-Nearest Neighbor using Distance Weight,” no. ICoSET 2019, pp. 326–330, 2020, doi: 10.5220/0009390903260330.
  23. P. A. Charde and S. D. Lokhande, “Classification Using K Nearest Neighbor for Brain Image Retrieval,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 8, pp. 760–765, 2013, doi: 10.25126/jtiik.2020722608.
  24. L. Farokhah, “Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 6, p. 1129, 2020, doi: 10.25126/jtiik.2020722608.
  25. Z. Pan, Y. Wang, and W. Ku, “A new k-harmonic nearest neighbor classifier based on the multi-local means,” Expert Syst. Appl., vol. 67, pp. 115–125, 2017, doi: 10.1016/j.eswa.2016.09.031.
  26. J. Gou, H. Ma, W. Ou, S. Zeng, Y. Rao, and H. Yang, “A generalized mean distance-based k-nearest neighbor classifier,” Expert Syst. Appl., vol. 115, pp. 356–372, 2019, doi: 10.1016/j.eswa.2018.08.021.
  27. K. U. Syaliman, E. B. Nababan, and O. S. Sitompul, “Improving the accuracy of k-nearest neighbor using local mean based and distance weight,” J. Phys. Conf. Ser., vol. 978, no. 1, pp. 1–6, 2018, doi: 10.1088/1742-6596/978/1/012047.
  28. Y. Mitani and Y. Hamamoto, “A local mean-based nonparametric classifier,” Pattern Recognit. Lett., vol. 27, no. 10, pp. 1151–1159, 2006, doi: 10.1016/j.patrec.2005.12.016.
  29. S. A. Dudani, “The Distance-Weighted k-Nearest-Neighbor Rule,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. SMC-8, no. 4, pp. 311–313, 1978, doi: 10.1109/tsmc.1978.4309958.
  30. N. García-Pedrajas and D. Ortiz-Boyer, “Boosting k-nearest neighbor classifier by means of input space projection,” Expert Syst. Appl., vol. 36, no. 7, pp. 10570–10582, 2009, doi: 10.1016/j.eswa.2009.02.065.
  31. A. Kataria and M. D. Singh, “A Review of Data Classification Using K-Nearest Neighbour Algorithm,” Int. J. Emerg. Technol. Adv. Eng., vol. 3, no. 6, pp. 354–360, 2013.
  32. Z. Lei, S. Wang, and D. Xu, “Protein sub-cellular localization based on noise-intensity-weighted linear discriminant analysis and an improved k-nearest-neighbor classifier,” Proc. - 2016 9th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2016, no. 3, pp. 1871–1876, 2017, doi: 10.1109/CISP-BMEI.2016.7853022.

Similar Articles

1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.