#TITLE_ALTERNATIVE#

<p align="justify">The world's development into the digital era has produced an emerging trend with the use of huge size text files as ebooks or electronic journals. The Lempel-Ziv Storer Szymanski (LZSS) is a type of compression algorithm that is suitable to compress those kind...

Full description

Saved in:
Bibliographic Details
Main Author: EVEN RAMADHAN (NIM 13203076); Pembimbing: Dr.Ir. Hendrawan, PRIMA
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/13739
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Institut Teknologi Bandung
Language: Indonesia
Description
Summary:<p align="justify">The world's development into the digital era has produced an emerging trend with the use of huge size text files as ebooks or electronic journals. The Lempel-Ziv Storer Szymanski (LZSS) is a type of compression algorithm that is suitable to compress those kinds of files. This algorithm works based on a principle which is called string compression. This principle made LZSS algorithm works very well when used to compress huge size files, especially text files. This happens because in big files, the occurrence of matching between the dictionary buffer and the look-ahead buffer would be very often during the encoding process. Based on these facts, the writer is interested to do a general evaluation of the algorithm which is done by comparing its compression ratio to the compression ratio of the existing compression programs (especially Pack and Compress, which are used in UNIX). The optimum type of text files and images are also evaluated in this Final Project.<p align="justify"><p>The evaluation is carried by implementing the LZSS algorithm into a program using Java programming language. The program made then is used to compress the selected data training set. The overall results show that the algorithm is often ranked in the middle of the table when compared to the existing compression programs. Also, for the optimum file type, the algorithm gets the best compression ratio of 12% for both text and image. <br />