Perbandingan Kinerja Algoritma Huffman dan Algoritma Shannon-Fano Dalam Mengkomperesi File Citra

Abstract

Stored data is increasing in volume. Various data compression algorithms have been researched and developed. The purpose of this study was to compare the performance of the Huffman Algorithm and the Shannon-Fano Algorithm. A comparison of the performance of the compression algorithm is done by testing each image file. The application used to test was built using C ++. Four image file types will be tested using the application that has been built. The file being tested considers the image file type as a compression object. The current image file tends to increase in terms of file size. The results of testing the four image files obtained contain many symbols and frequencies in determining the value of data entropy. The same file tested using the Huffman Algorithm produces better compression gain. The Fish.bmp file uses the Huffman 2.0% algorithm and uses the Shannon-Fano algorithm 1.7%. The greater the entropy, the smaller the gain obtained. For example, the white.bmp file entropy: 0.19693 using the Huffman Algorithm can gain 85.2%. entropy Fish.bmp file 6.87332 can get a 2.0% gain.