Data Cleansing Strategies on Data Sets Become Data Science - Sardjono Sardjono; R. Yadi Rakhman Alamsyah; Marwondo Marwondo; Elia Setiana (International Journal of Quantitative Research and Modeling, 2020)
The digital era very grows up with the increasing using of smartphone and many organization or companies was implemented of a system to support their business. That is who will increase the volume of usage and dissemination of data, neither through open nor closed internet networks. Because there is the need to process large data and how to get it from different store resource, so requirement strategy to process the data according to the rule of good, effective and efficient in activity data cleansing until the data set can be use as mature and very useful information for their business purpose. By using the R languaged who can process large data and has data complexity for the data loaded from different storage resource can be done as well as. To using R languaged maximally, so we have to a basic skill that needed to process the data set which will be used to be data scient for organizations or companies by good data cleansing techniques. In this research on Data Cleansing Strategies on data set owned by organizations,will describe the correct step by step to obtaining data that very useful to be uses as data science for organization so by the data that generated after the data cleansing process is very meaningful and useful for making decisions, other than that this research give basic overview and guide to the beginner all data scientists by doing data cleansing in the way stages and also provides a way to analyze from the result of execution some functions used.