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All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer)
Ewit Purba
Universitas Budi Darma, Medan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analisis Perbandingan Kinerja Boldi-Vigna Codes Dengan Algoritma Fixed Lenght Binary Encoding (FLBE) Dalam Kompresi File Text Ewit Purba; Efori Bu’ulolo; Bister Purba
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 6, No 1 (2022): Challenge and Opportunity For Z Generation in Metaverse Era
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v6i1.5700

Abstract

The increasing use of larger data causes problems in data storage, the greater the data stored, the greater the storage space that will be needed. This can cause the data transformation process to be slow and take a long time. Currently, there are many algorithms developed for data compression, but none are so good for compressing various file types because of their different characteristics. One solution or alternative in solving the problem that will be done is to compress the file to reduce the size of the data and speed up the data transmission process so as to save storage space. The algorithms used in this research are Boldi-Vigna (ζ1) Code and Fixed Length Binary Encoding (FLBE) algorithms. To find out the comparison of compression performance, the parameters to be compared are Ratio of Compression (RC), Compression Ratio (CR), Space Saving (SS), Redundancy (Rd), Compression & Decompression Time. Based on the test results show that the Fixed Length Binary Encoding (FLBE) algorithm is better than the Boldi-Vigna (ζ1) Code algorithm where the average result of the comparison of the Boldi-Vigna (ζ1) Code Ratio is 1.69 bits while the Ratio of Compression algorithm Fixed Length Binary Encoding (FLBE) 1.86 bits. The average compression ratio of the Boldi-Vigna algorithm (ζ1) Code is 58.92%, while the Compression Ratio of the Fixed Length Binary Encoding (FLBE) algorithm is 53.57%.
Co-Authors Bister Purba Efori Bu’ulolo