PERBANDINGAN METODE MAPREDUCE BERBASIS SINGLE NODE HADOOP PADA APLIKASI WORD COUNT

OKTAVIANI, ELVIRA and Utami, Alvi Syahrini and Marieska, Mastura Diana (2023) PERBANDINGAN METODE MAPREDUCE BERBASIS SINGLE NODE HADOOP PADA APLIKASI WORD COUNT. Undergraduate thesis, Sriwijaya University.

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Abstract

Word Count is a type of task used to count the occurrences of unique words in a text file. Processing time is an important factor to consider in meeting the standards of Big Data processing. In the context of Big Data processing, Hadoop MapReduce serves as a framework used to develop software and process large-scale data in parallel. The conducted research involved the processing of text files using the MapReduce method on the Hadoop Distributed File System (HDFS) using a single node, comparing the results of word count processing with and without the use of MapReduce. The research findings indicate that the implementation of Word Count without using MapReduce offers better speed and scalability in processing Indonesian language text data on a Hadoop single node. Additionally, the comparison of processing time between the Word Count program with Hadoopbased MapReduce and the Word Count program without MapReduce shows that the latter has faster processing time. A significant reduction in processing time, up to 95% for a 5 MB file, can be achieved by using the Word Count method without MapReduce, although the level of reduction decreases with increasing file size. Keywords : Big Data, Word Count, MapReduce, HDFS, Hadoop Single Node

Item Type:	Thesis (Undergraduate)
Subjects:	T Technology > T Technology (General) > T10.5-11.9 Communication of technical information T Technology > T Technology (General) > T58.6-58.62 Management information systems > T58.6 General works Industrial engineering Information technology. Information systems (General) Management information systems -- Continued
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Elvira Oktaviani
Date Deposited:	24 Jul 2023 07:33
Last Modified:	24 Jul 2023 07:33
URI:	http://repository.unsri.ac.id/id/eprint/120932

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