Implementation of OLAP and K-Medoids Clustering for Accreditation Data Analysis of Study Programs

At present to maintain the quality of the data in the study program is very much needed, given the current accreditation based on PDDIKTI(Pangkalan Data Pendidikan Tinggi) data which must be reported every semester. Higher education data are generally still separate, there is no valid data warehouse and system for data analysis so that it complicates data quality control. The purpose of this study is to apply data warehouse, OLAP (Online Analytical Processing) and k-medoids clustering for data prediction and control according to the study program’s accreditation self-evaluation report matrix. This study uses data the last 3 years for prospective new students, students, students, study periods, achievements and lecturers to be analyzed using OLAP and the k-medoids cluster. The results showed OLAP can see information in an informative, real-time data accreditation matrix and the k-medoids cluster produces accurate cluster estimates with an evaluation value of Davies Bouldin Index of 0.2927 and said to be a good cluster


Introduction
Accreditation is a process of evaluation of tertiary education, this evaluation contains quantitative data which will be gradually taken from the Higher Education Database (PD-Dikti) which contains the performance indicators of tertiary institutions. This indicator was prepared by BAN-PT in particular by considering the uniqueness of the tertiary institution, Universities data and Information Centers play an important role in guaranteeing the availability, coverage, accuracy, validity, and reliability of PD-DIKTI data that will be used by BAN-PT in the period in 2020 40% is used and the period 2021 uses 60% of the data [1].
Data warehouse and OLAP (Online Analytical Processing) are toll business intelligence that allows online analysis of large and multidimensional data. The data is stored in accordance with the multidimensional data model [2] The goal of Business Intelligence (BI) is to transform the operation of the collected data into information and knowledge that will be used for decision making. The process of data is periodically extracted, cleaned, transformed, and entered into a repository called a data warehouse (DW), where data is stored in the form of multidimensional cubes for analysis to make decisions using OLAP (On-Line Analytical Processing). Although BI techniques accelerate and improve the decision-making process for almost two decades, currently facing a very dynamic environment requires external factors such as market data or socioeconomic data in the decision-making process. Thus, traditional OLAP is based on stationary data that is, reliable data owned by the company itself, IOP Conf. Series: Materials Science and Engineering 879 (2020) 012067 IOP Publishing doi:10.1088/1757-899X/879/1/012067 2 the last few years witnessed the urge to enrich the data warehouse with the latest external situational data [3].
Data in universities each semester becomes very large data, data warehouse (data warehouse) is one of the containers for collecting a number of data, data warehouse can also be developed in policy-making decisions, which serves to store, analyze and visualize data effectively, processes extract transform load (ETL) in the data warehouse is needed as data collection from various data sources, so ETL must be distributed in the data warehouse to approach the archive data reporting and visualization [4]. In the study of Comparative Analysis of K-means and K-medoid Algorithms on IRIS Data, the application of the Partitioning method of clustering techniques to decide which categories are used, the K-means or Kmedoids method obtains better accuracy in partitioning data several times proving the superiority of Kmedoid compared to K-mean in implementation time, class grouping quality and also number of records. Data obtained using K-medoid compared to K-mean using real samples obtained from reliable repositories [5].
The purpose of this research is to predict data related to the study program's accreditation by taking data from existing data sources in the university information system, then the data is entered in the college data warehouse, data already in the data warehouse is processed and analyzed with OLAP SQL business enterprise intelligence server, then the data related to the study program accreditation matrix namely student GPA data and the average study period of students used k-medoids clustering research data samples, for better evaluation of the data, this research was applied to Pancasakti University, Tegal University's law study program.

Method
The method used in this study is proposed by OLAP (Online Analytical Processing) which produces data analysis, achievements in graphs and also uses a combination of the k-medoids clustering method, the data is taken from the information system data in the tertiary institutions, data-The data is then extracted and entered into a data warehouse for analysis [6] for the steps of the research process as follows:

Observation of research objects
These observations include determining the data to be used in research

Data Collection
This data collection is done for input data in research

System Analysis and Design
Analyze and design the system that will be developed 2.4 Implementation Implement the results of the system analysis and design process

Testing and Evaluation
Test and evaluate system process data that is not appropriate

Results and Discussion
Data related to study program accreditation is analyzed data every year PDDIKTI reported, these data include student data, prospective student data, study period, average GPA, lecturer teaching load and student achievement, these data control is carried out every semester to maintain the quality of data in the study program. The dynamic distance-based k-medoid method [7] or the partitional Clustering method used in this study, k-medoids minimize the distance between the labeled points in the cluster and the point designated as the center of the cluster [8] to check the similarity between data objects, the calculation process is done between one object with another object, between these objects are used to determine the level of similarity or object equation by means of distance calculation [9].
Tests in data clusters are needed to test how good or bad the data clusters [10] clustering evaluation is also used to evaluate the quality of data grouping, the authors consider using the Davies Bouldin Index (DBI) the smaller the value of the Bouldin index davies obtained (non-negative> = 0), the better the clusters obtained from clustering using the clustering algorithm [11]. Purity is also used to evaluate a cluster that is represented as a cluster member in a class. Clusters obtained are said to be good if the value of purity approaches 1. To calculate the purity of each cluster can use the formula as follows [12] [13] (Ω, ) = 1/ ∑ | ∩ | where Ω = w 1 , w 2 ..... w j is the class set and C = C 1 , C 2 , ...., C k is the cluster set. Bad clusters have a purity value approaching 0, perfect clusters have purity 1. This measure only applies to samples labeled where the class or labeling of each data point is available [14]. More clearly about the business process of this research can be seen in Figure 1.

Input Proses Output
Data ETL (Extraction,  In Figure 1 explains the input data is processed using OLAP SQL server enterprise business intelligence system and then also clustered with k-medoid clustering, the output of the data entered is in the form of a graph within 3 years regarding student data, study period, average GPA, lecturer teaching load, and student achievement can be shown in the following figure 2: In the picture above shows the results of data processing by OLAP (Online Analytical Processing), based on data analysis results using OLAP student achievement has increased but 2 years before did not get any achievement, the data of study duration showed that in the last 3 years the average length of study remained and experienced an increase in 2019, the average GPA data shows there has been an increase and decrease in the average GPA of students in the last 3 years and the teaching process of lecturers shows the teaching load of lecturers is not balanced there are lecturers having a high teaching load and some are low, graphs The leader is used to control and make decisions.
College data taken from the existing information system at the university that is at the Pancasakti Tegal university data extraction data is then entered into the data warehouse, after in the data warehouse data that is in accordance with the study program accreditation matrix data as sample data that is the average calculation matrix the average of GPA students and the length of their study period as awareness systems, students who have GPA and long study periods can be immediately evaluated to maintain the data of the accreditation matrix so that it has an optimal weighting matrix value. The following table 1 is a dataset for calculating the k-medoids algorithm The k-medoids step is to select the k user event until the first iteration is smaller than the next iteration, based on the distance calculation the clustering evaluation calculation is obtained in table 4 Then the clustering evaluation is calculated by purity cluster evaluation based on the calculation of distances that have been done previously shown in table 5