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

Dwi Nugraheny

doi:10.28989/angkasa.v7i2.145

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.

Highlights

Salah satu tahap pencocokan kesamaan ataupun kemiripan ciri-ciri suatu citra adalah dengan menggunakan metode pengukuran jarak
One commonality or similarity matching phase characteristics o f an image is by using the method o f distance measurement
Distance is an important aspect in the development o f methods o f grouping and regression

Summary

Introduction

Salah satu tahap pencocokan kesamaan ataupun kemiripan ciri-ciri suatu citra adalah dengan menggunakan metode pengukuran jarak. Akan dilakukan perbandingan dari beberapa metode pengukuran jarak diantaranya menggunakan jarak Euclidean, Manhattan/City Block Distance, Mahalanobis yang akan di implementasikan pada deteksi citra awan Cumulonimbus menggunakan Principal Component Analysis (PCA). Berdasarkan uaraian tersebut di atas, pada penelitian ini akan dilakukan perbandingan beberapa metode jarak antar vektor dan di implementasikan menggunakan Principal Component Analysis (PCA) untuk membangun model deteksi citra awan Cumulonimbus.

Results

Conclusion