Analisa Performa Klastering Data Besar pada Hadoop

Abstract

Big Data is a collection of data with a large and complex size, consisting of various data types and obtained from various sources, overgrowing quickly. Some of the problems that will arise when processing big data, among others, are related to the storage and access of big data, which consists of various types of data with high complexity that are not able to be handled by the relational model. One technology that can solve the problem of storing and accessing big data is Hadoop. Hadoop is a technology that can store and process big data by distributing big data into several data partitions (data blocks). Problems arise when an analysis process requires all data spread out into one data entity, for example, in the data clustering process. One alternative solution is to do a parallel and scattered analysis, then perform a centralized analysis of the results of the scattered analysis. This study examines and analyzes two methods, namely K-Medoids Mapreduce and K-Modes without Mapreduce. The dataset used is a dataset about cars consisting of 3.5 million rows of data with 400MB distributed in a Hadoop Cluster (consisting of more than one engine). Hadoop has a MapReduce feature, consisting of 2 functions, namely map and reduce. The map function performs a selection to retrieve a key, value pairs, and returns a value in the form of a collection of key value pairs, and then the reduce function combines all key value pairs from several map functions. The results of the cluster quality evaluation are tested using the Silhouette Coefficient testing metric. The K-Medoids MapReduce algorithm for the car dataset gives a silhouette value of 0.99 with a total of 2 clusters.

Top-K Query for Large Dataset of Restaurant Review Based-on Hadoop MapReduce Framework

“…The MapReduce operations available in Hadoop include wordcount. Wordcount is a program for counting words in plaintext files [4].…”

“…Hadoop is one of the technologies that enables the collection and distributed processing of large amounts of data on a cluster of interconnected computers with a simple model [3]. Meanwhile, MapReduce is a parallel programming paradigm that exists within Hadoop and is useful for processing large amounts of data between each node [4].…”