CRISP-DM: Metodologi Proyek Data Science
Keywords:
Literature review, data science, CRISP-DMAbstract
This research is motivated by the development of data science which is increasingly popular. To support this development, especially in the field of research, methodologies are needed so the projects created can be well structured and organized. This research aims to discuss one of the most widely used data science methodologies, that’s cross industry standard process for data mining (CRISP-DM). This research used a type of literature review research with data collection technique used literature study. Based on the research that has been conducted, it can be concluded that CRISP-DM methodology can be used in the field of data science projects precisely in the areas of data mining, artificial intelligence, machine learning, deep learning, big data, data analysis, and data analytics. This methodology has six stages that each of these stages has different phases from one to another. For those phases, researchers can follow phases that have been commonly used in other studies or can adjust according to their respective projects. Then as a consideration in using CRISP-DM methodology, it can be considered based on the benefits and challenges of it.
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