Abstract
The need for data analysis in tertiary education every semester is needed, this is due to the increasingly large and uncontrolled data, on the other hand generally higher education does not yet have a data warehouse and big data analysis to maintain data quality at tertiary institutions is not easy, especially to estimate the results of university accreditation high, because the data continues to grow and is not controlled, the purpose of this study is to apply k-medoids clustering by applying the calculation of the weighting matrix of higher education accreditation with the data of the last 3 years namely length of study, average GPA, student and lecturer ratio and the number of lecturers according to the study program, so that it can predict accurate cluster results, the results of this study indicate that k-medoid clustering produces good cluster data results with an evaluation value of the Bouldin index davies cluster index of 0.407029478 and is said to be a good cluster result.
Highlights
Accreditation is the determination of quality standards and assessments of an educational institution by parties outside an independent institution in this case BAN-PT
The self-evaluation refers to the selfevaluation guidelines that have been issued by BAN-PT, if deemed necessary, the manager of the tertiary institution can add additional elements to be evaluated by the interests of the tertiary institution concerned
Nowadays data warehouse technology cannot handle the process of loading and analytic data into meaningful information for management
Summary
Accreditation is the determination of quality standards and assessments of an educational institution (higher education) by parties outside an independent institution in this case BAN-PT. This study aims to understand the assessment of basic education in the perspective of the State Reviewer as a mechanism that generates information regarding the positivity and weaknesses of a school or an educational system to provide improvements For this reason, a Data Warehouse was created and later some analysis of the indicators were performed through clustering.The university has a large amount of data so that the academic data of the university has grown significantly and become big data. Based on the explanation above the Kmedoids clustering method in tertiary data for supporting accreditation is very important, while the system for processing such data does not yet exist, currently, data processing is still manual or only based on experience or not based on criteria set by tertiary institutions or competent body This is certainly a mistake in making decisions and results in data quality at tertiary institutions which cannot be predicted early on, especially based on management information system data [10].
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