AUTHORS: Yayuk Anggraini, Teddy Mantoro, Media A. Ayu
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ABSTRACT: Compression in Information Technology is the way to minimize the file size. Performance of compression algorithm is measured by the speed of process and the compression ratio. The compression time will effect on memory allocation and CPU performance, while the low compression ratio will weakens the ability of the algorithm to compress the data. Huffman and ShannonFano are two compression algorithms have same ways to work, but both produced a different performance. The test results concluded that the Shannon-Fano performance has a percentage of 1,56% lower than Huffman. This problem can be solved by adding a reversible transformation algorithm to the data source. Burrows-Wheeler Transform (BWT) produces output that is more easily to be processed at a later stage, and Move-to-front (MTF) is an algorithm to transform the data unifying and reduce redundancies. This study discusses a combination of BWT + MTF + Shannon-Fano algorithm and compare it with other algorithms (Shannon-Fano, Huffman and LZ77) which were applied on text files. The test results have shown that the combination of BWT + MTF + ShannonFano has the most efficient compression ratio, which is 60.89% higher at around 0.37% compared to LZ77. On compression time aspect, LZ77 is the slowest, approximately 39391,11 ms, while a combination of BWT + MTF + Shannon-Fano performs at approximately 1237,95 ms. This study concluded that the combination of BWT + MTF + Shannon-Fano algorithm performs as the most optimal algorithm in compression time (speed) and compression ratio (size).
KEYWORDS: BWT, MTF, Shannon-Fano, Huffman, LZ77, text files, compression algorithm optimization.REFERENCES:
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