KOMPARASI METODE INTERPOLASI UNTUK SISTEM PENGENALAN SEL DARAH PUTIH

Lina Lina(1*), Kelly Anthony(2),

(1) Universitas Tarumanegara
(2) Universitas Tarumanegara
(*) Corresponding Author

Abstract


The over time role of technology becomes very important. That is because the function of technology is to facilitate human work. Because human needs are increasingly complex, technological developments are created in such a way as to meet human needs. The experts in the medical field are currently very dependent on technology to do their jobs, in order to obtain effective and efficient results. Application system designed aims to help experts in the medical field to diagnose diseases through introduction to white blood cell types. The recognition system was developed using the Nearest Feature Line (NFL) method. In this NFL method, characteristic lines are formed using the method of linear interpolation, linear spline, quadratic spline, and cubic spline. Aside from introducing an introduction system, this paper also discusses comparisons between interpolation methods to form characteristic lines. The test was carried out using FTI Untar Pattern Recognition laboratory blood cell data. The test results show that the formation of characteristic lines using the linear interpolation method provides better recognition results compared to the spline interpolation method.

Keywords


Nearest Feature Line, Linear Interpolation, Spline Interpolation.

Full Text:

PDF

References


Chapra, S. C. (2005). Applied Numerical Methods with MATLAB for Engineers and Scientists. The McGraw-Hill Companies, Inc.

Hoffbrand , A.V Pettit, J.E and Moss, P. A. H. (2008). HEMATOLOGI (Lyana Setiawan (ed.); Edisi 4). Kapita Selekta.

Kiernan, J. . (1999). Histological & Histochemical Methods (Oxford).

Kusumoputro, B. (2003). Garis Ciri Pada Metode Perhitungan Jarak Terpendek Dalam Ruang Eigen. Universitas Stuttgart, 7(1), 1–10.

Li, S. Z. (1999). S. Z. Li and Juwei Lu, “Face recognition using the nearest feature line method,” in IEEE Transactions on Neural Networks, vol. 10, no. 2, pp. 439-443, March 1999.

Lina, B. K. (2004). Sistem Pengenalan Wajah 3-D Menggunakan Garis Ciri pada Metode Perhitungan Jarak Terpendek dalam Ruang Eigen, Jurnal Makara Seri Sains. Universitas Indonesia, 7(1), 1–6.

Lina, K. A. (2019). KOMPARASI METODE INTERPOLASI UNTUK SISTEM PENGENALAN SEL DARAH PUTIH.

Sani, A. (2018). Penerapan Metode K-Means Clustering Pada Perusahaan. Jurnal Ilmiah Teknologi Informasi, 353, 1–7.

Weisstein, E. W. (2017). Distance. https:// mathworld.wolfram.com/Distance.html




DOI: https://doi.org/10.37365/jti.v5i1.56

Refbacks

  • There are currently no refbacks.


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

klik4d