Implementation of Text Compression using Adaptive Shannon-Fano Algorithm
Satria Gunawan Zain1, Nirwana2, Andi Baso Kaswar3, Suhartono4, Abd. Rahman Patta5
1Satria Gunawan Zain*, Computer Engineering, Univercity Negeri Makassar, Indonesia.
2Nirwana, Computer and Informatics Engineering Education, Universitas Negeri Makassar, Makassar, Indonesia.
3Andi Baso Kaswar, Computer Engineering, Universitas Negeri Makassar, Makassar, Indonesia.
4Suhartono, Computer Engineering, Universitas Negeri Makassar, Makassar. Indonesia.
5Abd. Rahman Patta, Computer Engineering, Universitas Negeri Makassar, Indonesia.
Manuscript received on January 22, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 29, 2020. | PP: 3984-3990 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6383029320/2020©BEIESP | DOI: 10.35940/ijeat.C6383.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study aims to implement the Shannon-fano Adaptive data compression algorithm on characters as input data. This study also investigates the data compression ratio, which is the ratio between the number of data bits before and after compression. The resulting program is tested by using black-box testing, measuring the number of character variants and the number of types of characters to the compression ratio, and testing the objective truth with the Mean Square Error (MSE) method. The description of the characteristics of the application made is done by processing data in the form of a collection of characters that have different types of characters, variants, and the number of characters. This research presents algorithm that support the steps of making adaptive Shannon-fano compression applications. The length of the character determines the variant value, compression ratio, and the number of input character types. Based on the results of test results, no error occurs according to the comparison of the original text input and the decompression results. A higher appearance frequency of a character causes a greater compression ratio of the resulting file; the analysis shows that a higher number of types of input characters causes a lower compression ratio, which proves that the proposed method in real-time data compression improves the effectiveness and efficiency of the compression process.
Keywords: Data Compression, Shannon-Fano, Text Compression.