Comparative Analysis of Sequitur Algorithm with Adaptive Huffman Coding Algorithm on Text File Compression with Exponential Method

Authors

  • Muhammad Fadlan  Research Scholar. STMIK Budi Darma, Medan, Indonesia
  • Surya Darma Nasution  Department of Computer Engineering, STMIK Budi Darma, Medan, Indones
  • Fadlina  Department of Computer Engineering, STMIK Budi Darma, Medan, Indones
  • Saidi Ramadan Siregar  Department of Computer Engineering, STMIK Budi Darma, Medan, Indones
  • Pristiwanto  Department of Computer Engineering, STMIK Budi Darma, Medan, Indones

Keywords:

Compression, Text file, Adaptive Huffman Coding, Sequitur, Exponential.

Abstract

This research was conducted to analyze and compare sequitur algorithm with the adaptive Huffman coding algorithm in compressing text file, to find which algorithm more effective and efficient in compressing compressed text file will be compared with an exponential method to be seen the performance of each algorithm based on CR, SS, RC. Text file compression will read the current string in the text file. In the Huffman adapting algorithm the text file coding will be changed into bit form, and the sequitur algorithm will change the file based on the grammar. The test result states that Huffman adapting coding algorithm is no better than sequitur algorithm.

References

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International Journal of Scientific Research in Science and Technology

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Published

2017-12-31

Issue

Volume 3, Issue 8, November-December-2017

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Muhammad Fadlan, Surya Darma Nasution, Fadlina, Saidi Ramadan Siregar, Pristiwanto "Comparative Analysis of Sequitur Algorithm with Adaptive Huffman Coding Algorithm on Text File Compression with Exponential Method" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 3, Issue 8, pp.349-353, November-December-2017.