METODE NILAI JARAK GUNA KESAMAAN ATAU KEMIRIPAN CIRI SUATU CITRA (KASUS DETEKSI AWAN CUMULONIMBUS MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS)

Dwi Nugraheny

Abstract

One commonality or similarity matching phase characteristics of an image is by using the method of distance measurement. Distance is an important aspect in the development of methods of grouping and regression. Before the grouping of data or object to the detection process, first determined the size of the proximity distance between data elements. In this study, there will be a comparison of several methods including distance measurement using Euclidean distance, Manhattan/ City Block Distance, Mahalanobis which will be implemented in the case of cumulonimbus image clouds detection using Principal Component Analysis (PCA). The average percentage of accuracy of image similarity value Cumulonimbus clouds using the Euclidean distance method was 93 percent and the distance Manhattan/ City Block Distance is 90 percent, while the Mahalanobis distance method was 50 percent.

Keywords

Similarity, Cumulonimbus, Euclidean, Manhattan, Mahalanobis, PCA

References

Artikelsiana, pengertian awan dan jenis-jenis-awan, http://www.artikelsiana.com, diakses tanggal 10 Juni 2015

Budi Santosa, 2007, Data Mining (Teori dan Aplikasi), Graha Ilmu, Yogyakarta.

Bajwa I.S., Naweed M.S., Asif M.N., Hyder.S.I., 2009, Feature Based Image Classification by using Principal Component Analysis, ICGST-GVIP Journal, ISSN 1687-398X, Vol. 9, Issue II.

Dwi Nugraheny, 2010, Deteksi Awan Comulonimbus Menggunakan Principal Component Analysis (PCA), Prosiding Seminar Nasional Aplikasi Sains & Teknologi, hal A-77 ISSN No. 1979-911X.

Hair Jr., Joseph F., Black, William C., Babin, Barry C., dan Rolph E. Anderson, 2010, Multivariate Data Analysis 7/e, Pearson Prentice Hall, New Jersey.

Jenness Enterprises, 2008, Mahalanobis Distance, http://www.jennessent.com/arcview/mahalanobis_description.htm, diakses tanggal 03 Juli 2015.

Krueger J, et.al., 2004, Thresholds for Eigenface Recognition, http://cnx.org/content/m12533/1.2/, diakses tanggal 15 Juni 2015.

Mudrova M, et.al., 2002, Principal Component Analysis (PCA) in Image Processing, Institute of Chemical Technology, Prague Department of Computing and Control Engineering.

McAndrew, A, 2004, An Introduction to Digital Image Processing with Matlab, School of Computer Science and Mathematics Victoria University of Technology

Rancher, AC, 2004, Methods of Multivariate Analysis Second Edition, John Wiley & Sons, Canada.

Smith I Lindsay, 2002, A tutorial on Principal Component Analysis. Cornell University, USA. February.

http://www.free-pictures-photos.com/clouds, diakses tanggal 10 Juni 2015.

http://vortex.plymouth.edu/clouds.html, diakses tanggal 11 Juni 2015.

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