I. INTRODUCTION II. LITERATURE REVIEW. A. EPSBED 1) EPSBED Definition EPSBED is a reporting media which organized by the study program of each college

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2 Data Warehouse for Study Program Evaluation Reporting Based on Self Evaluation (EPSBED) using EPSBED Data Warehouse Model: Case Study Budi Luhur University Indra, Yudho Giri Sucahyo, Windarto Faculty of Information Technology of Universitas Budi Luhur, Faculty of Computer Science of Universitas Indonesia, Faculty of Information Technology of Universitas Budi Luhur Jl.Ciledug Raya, Post Code 12260, Jakarta, Indonesia Abstract- Each of study program at a university in Indonesia are required to report the results of academic activities for one semester to the Directorate General of Higher Education, The Ministry of National Education of Republic of Indonesia (DIKTI) through the Coordinator of Private Higher Education (KOPERTIS). The reporting will be used to measure the performance of the study program for each university in Indonesia. The reporting process is known as the Study Program Evaluation Based on Self Evaluation (EPSBED). Until now data from the processing of EPSBED not yet maximized by the executive party of Budi Luhur University to become one of the reference in the field of academic decisions. For this reason the analysis of the data warehouse of EPSBED may be one of an important component to be considered in any decision-making by the executive party of Budi Luhur University. Moreover, by using the EPSBED data warehouse the process of generating report become faster in a count of minutes because the process is automated and scheduled. The methodology undertaken in this research contains several stages. The first stage is analyzing the needs of information which required by the executive of Budi Luhur University. The second stage is to collect data to fill up information needs. The third stage is analyzing the data warehouse which designed using star schema techniques and using the Pentaho for community or open source version as a tool. The last stage is to implement Online Analytical Processing (OLAP) from the application of the data warehouse. Keywords-components: EPSBED, data warehouse, star schema, study program, and college I. INTRODUCTION A. Background Since 2002, every college in Indonesia has carry out its obligations in reporting of study program performances using a particular database structure. The database structure has been formalized and packaged in a reporting system called Study Program Evaluation Based on Self Evaluation known as EPSBED by all universities in Indonesia. In accordance with that was stipulated in Director General of Higher Education Decree No.34/DIKTI/Kep/2001. In terms of the EPSBED reporting process, Budi Luhur University must do so many query processes to retrieve the required data from the database of it. This is due to of UBL database has different structure from EPSBED database structure. Moreover, it should be done the data cleansing process because there are many incomplete data. The results of the process which generated by EPSBED report has been building for many years in the UBL database. To date, there has not been data warehouse to show historical data results of these EPSBED. The EPSBED data has not been maximized as a material consideration by the executive in taking a decision. B. Problem Formulation From those core issues, this is a fundamental question for How to design a data warehouse to facilitate and accelerate the reporting process and how to implement the EPSBED data warehouse to be taken into consideration material in any decision-making by the executive of Budi Luhur University? C. Research Objectives The final purposes of this research is to: 1) Describes the design and developing of data warehouse to facilitate and accelerate the process of EPSBED reporting. 2) Make a cube (fact table) and OLAP (Online Analytical Processing) to view detailed of EPSBED report using roll up and drill down features. II. LITERATURE REVIEW A. EPSBED 1) EPSBED Definition EPSBED is a reporting media which organized by the study program of each college 1

Since the academic year of 2002-2003, the reporting of study program activities has been using electronic data and the reporting aspects includes institutional, curriculum, lecturers, students, and

3 to the Directorate General of Higher Education, Ministry of National Education of Republic of Indonesia (DIKTI). Under the provisions and the legislation, each of study programs must report their ongoing activities related to the academic activities each semester. Since the academic year of , the reporting of study program activities has been using electronic data and the reporting aspects includes institutional, curriculum, lecturers, students, and associated to infrastructures which accessed by the study program (Ilah, 2) The Legal Basis of EPSBED Based on the Decree of Director General of Higher Education Number: 08/DIKTI/Kep/2002 on Technical Guidelines for the National Education Decree Number: 184/U/2001 About Monitoring Control Guidelines and the Development of Diploma Program, Bachelor and Master degree in Higher Education (including the provision of certificates and transcripts). Those decrees are some of the legal bases in the implementation of EPSBED in each of Universities in Indonesia. and Delen (2011) explained that the data warehouse is also used as a central repository of past data and current data which potential for manager's deliberation of an organization. 2) Data Warehouse Modeling Techniques (Figure 2.2) In this research will be used multi dimensional model, where there are two dimensions for each data warehouse, namely: fact tables and dimension tables. Fact tables generally have a foreign key and measurement. Measurement is a field that has a numeric value, used for the measurement (measure), while the foreign key is the primary key of the corresponding dimension in the design of the fact table. The data warehouse modeling technique is using star join approach. Star join approach resembles the form of star, which is fact table in the center and dimension tables surrounded it. This approach can be seen in Figure ) ESPBED Workflow (Figure 2.1) EPSBED workflow is sequence of data migration process from each college's internal database to DIKTI EPSBED database. In accordance with the reference of the Higher Education Development Data Base (PDPT), the workflow of EPSBED can be described below. Figure 2.2 Star Join Approach Multidimensional Data Model [3] C. Data Warehouse Architecture Data warehouse architecture can be described below (Figure 2.3): Figure 2.1 EPSBED Workflow [1] B. Data Warehouse 1) Data Warehouse Definition Data warehouse is a collection of data used for management decision making, which subject oriented (topic), integrated, time variant and not easily to changed (Inmon, 2005). Turban, Sharda Figure 2.3 Data Warehouse Architecture [2] 2

4 Figure 2.3 show that data warehouse is divided into four parts: 1) Source Data System Sources of data obtained from various transactions and production result of operational application of the company that runs every day. Transactional data is still a regular data or raw data. 2) Data Staging Area Before entering into this phase, first stage data extracted and entered into the staging area. At this stage in the data is cleansed, reconciled, matched, and standardized so that the data are clean from defects, this process is commonly known as transform. 3) Data & Metadata Storage Once data are cleansed then the data inserted (loaded) into the data warehouse. Data in the data warehouse can be used as a material in determining the policy (decision support) by the executive in a variety of issues. 4) End User Presentation Tools At this final stage is the development of an existing data warehouse. One of these is the using of data warehouse to use as business intelligence. performed loading process to inserting data into the data warehouse. In process of designing the data warehouse used a star schema model. From the result of designing a model is obtained a star schema fact table that is expected to support the reporting of EPSBED. 4) Results of Data Warehouse Processing Analysis At this stage, the results of data warehouse process will be developed to be used by the executive as materials analysis in making decision. The results of this processing are presented in the form of OLAP (Online Analytical Processing) with more detailed and dynamic in roll up and drill down feature. From four stages of design above, below is described the flow diagram (Figure 3.1): D. Data Warehouse Tools To design a data warehouse is used Pentaho Schema Workbench. As for the implementation of the Online Analytical Processing (OLAP) is using JPivot which is already integrated with BI Pentaho Server. Both of these tools can be downloaded through a site sourceforge.net. III. RESEARCH METHODOLOGY 1) Information Requirement Analysis At this stage, the research carried out by conducting in-depth analysis of the information required by the executive. This information needs to be the basis for data collection at a later stage. 2) Data Collection Techniques At this stage the process of collecting data by observation techniques, the study of literature and interviews with relevant parties. Interviews were conducted with the Head of Information and Technology bureau, the Chairman of Information Techniques study program, Information Systems and Information Management Diploma 3. The result of this stage is data transaction (OLTP) that will be retrieved to be used in designing of data warehouse. 3) Designing The Data Warehouse At this stage the data extracted from transactional database, and then performed a cleansing process to eliminate the empty or redundant data. After cleansing, the data in will be transformed with a view to defining the tables in the relational data source. After the transformation process the data is Figure 3.1 Research Methodology Stages IV. DATA WAREHOUSE ARCHITECTURE 1. Logical Data Warehouse architecture (Figure 4.1) At Figure 4.1 contains an explanation of the logical architecture of the data warehouse for EPSBED UBL reporting needs. 3

5 Figure 4.1 Logical Architecture of EPSBED UBL Data Warehouse The data source is a source of data from the entire academic transaction processes in UBL. The data source is using Oracle 9i licensed software. At the first stage, tables selection process would be carried out which are needed in designing data warehouse in accordance with the existing dimension tables and fact tables, this process known as selection process. Then the specified tables extracted thereafter performed the data mapping from each of tables which needs to be inserted into the data warehouse, this process is known as extraction. 2. Physical Architecture of EPSBED UBL Data Warehouse Figure 5.1 Design of Student s Academic Activities Fact Table (FACT_TRAKM) Figure 4.2 Physical Architecture of EPSBED UBL Data Warehouse UBL operational database is using Oracle 9i with SID: SYSTEM. While the database which used for the data warehouse is using Oracle 9i with SID: SIDIKTI. While in ETL process is using Pentaho Data Integration (Kettle) as a tool, for the cube is using Pentaho Schema Workbench and Pentaho Analysis Services OLAP. V. DESIGNING THE DATA WAREHOUSE In implementing the EPSBED data warehouse it contains multiple fact tables, including the fact table of student's academic activities. 1) Design of Student s Academic Activities Fact Table (FACT_TRAKM) FACT_TRAKM fact table is a fact table that is used to generate reports of GPA distribution, distribution of IPS and the number of student's credits along their study in each subject and used to generate student's status reports. FACT_TRAKM fact table contains of a measurement and the foreign key. Measurement is a numeric type field which used as a measurement in the fact table. The foreign key is a primary key in the corresponding dimension in the design of the fact table and description of a measurement and the foreign key in FACT_TRAKM fact table. Dimensions related to the design of academic activities (FACT_TRAKM) fact table are dimension of student (DIM_MSMHS), dimension of college (DIM_MSPTI), dimension of GPA condition (DIM_KONDISI_IPK), dimension of IPS condition (DIM_KONDISI_IPS), dimension academic year (DIM_TAHUNAJARAN), dimension of the study level (DIM_JENJANG), dimension of study program (DIM_MSPST) and dimension of status of student (DIM_STATUS_MHS). 4

6 CITEE 2012 Yogyakarta, 12 July 2012 ISSN: VI. RESEARCH DISCUSSION 2) Transformation Process A. Staging Process of Extract, Transform, and Loading act tables and dimension tables, After designing fact the next stage are to do the extraction, transformation transform and loading (ETL) to obtain a valid data which stored in the data warehouse. ETL processes can be described as follows: 1) Extraction Process Extraction process is the process of taking data from the data source as a field or a table tabl from a transactional database, which is required in the EPSBED data warehouse. This process is done in two methods. Those methods are a manual method and the method of Kettle. The manual method is done because the data were taken less than 20 records. The Th manual method is done only by using the query manually to recall the data. Kettle is a method of extracting the data source to select a field or a table using Kettle's tool. The results of extraction process can be seen in Figure ure 7.1. The transformation process is a process to adjust the field's name from the data source with attributes or fields dimension and fact tables in accordance with the requirements of EPSBED data warehouse. Th The adjustment is done due to differences of database structure in the data source with the data warehouse structure. The results of transformation process can be seen in Figure 6.2 Figure Transform Process Scheme 3) Loading Process Loading process is the final al process of the data warehouse development stages, after passing through an extraction phase, transformation phase and cleansing phase to be inserted into the data warehouse. This loading process uses the Pentaho Data Integration (Kettle) tool. The he complete scheme of ETL process described below (see Figure 6.3). Figure ETL Scheme using Kettle After the ETL process then the data that was inserted into the data warehouse is a subject subject-oriented data, has dimension of time and integrated. The results of this data warehouse process can be used as consideration materials by the executive to make a decision. Figure 6.1 Extract Scheme using Kettle 5

B. TRAKM Cube Schema After the ETL stages is completed, the dimension tables and the fact tables already contain valid required data for designing the OLAP in data warehouse.

In Pentaho Workbench Schema will contain a single fact table (cube) which has some relevant dimension tables.

4 Cube Scheme in Workbench From the figure shown above, a cube (fact table) named c_trakm contains these attributes nimhstrakm, kdptitrakm, kdjentrakm, kdpsttrakm and the measurement that is the

7 B. TRAKM Cube Schema After the ETL stages is completed, the dimension tables and the fact tables already contain valid required data for designing the OLAP in data warehouse. Each dimension will be linked to a fact table to become a star schema that will be used in the data warehouse implementation. A tool named Pentaho Workbench Schema is used to create a star schema. In Pentaho Workbench Schema will contain a single fact table (cube) which has some relevant dimension tables. The fact table contains some attributes and measurements that will be shown in a figure below: Figure 6.5 Cumulative Grade Point Average dan Semester Grade Point Average Figure 6.4 Cube Scheme in Workbench From the figure shown above, a cube (fact table) named c_trakm contains these attributes nimhstrakm, kdptitrakm, kdjentrakm, kdpsttrakm and the measurement that is the average of GPA and IPS. The dimension tables which associated with c_trakm cube are dim_mahasiswa, dim Perguruan Tinggi Indonesia (dim_pti), dim of level study and dim of course (prodi). After making of cube is completed, the next step is to publish the results of this scheme into Pentaho cube bi-server to generate the necessary OLAP for EPSBED data warehouse analyses. C. Application Results of The EPSBED Data Warehouse Model This part is explaining the implementation results of EPSBED data warehouse model. This information is used to generate visualization of EPSBED reporting process and it will be displayed in an Online Analytical Process (OLAP) form. The result contains of these information of Cumulative Grade Point Average, Semester Grade Point Average, distribution of Cumulative Grade Point Average, and distribution of Semester Grade Point Average. Figure below shows the result of OLAP visualization of Cumulative Grade Point Average and Semester Grade Point Average. As shown at Figure 6.5, by using drill down from a Cumulative Grade Point Average and Semester Grade Point Average in the odd semester of year academic of 2010/2011 for Information Management study program at Diploma 3 degree in UBL. On the drill down can be seen that the Cumulative Grade Point Average value is 2,935 and that the Semester Grade Point Average value is 2,979. The drill down is functioned to look at the Cumulative Grade Point Average and the Semester Grade Point Average each of study program or any other study program more dynamic and detailed. Besides being able to drill down, Mondrian can also roll up. Here is the roll up of the Cumulative Grade Point Average and the Semester Grade Point Average (see Figure 6.6). Figure 6.6 Roll Up of the Cumulative Grade Point Average and the Semester Grade Point Average At the figure 6.6 above is shown the result of the use of roll-up feature from the Cumulative Grade Point Average and the Semester Grade Point of Information Management study program in the academic year The value of Cumulative Grade Point Average in the academic year is 2.91 and the average value of the Semester Grade Point Average is Those values mentioned above are an accumulation of all 6

8 students point at the Information Management study program in the academic year of 2010/2011. D. Data Warehouse of The EPSBED Facilitate and Accelerate The Reporting Process of EPSBED However, after the EPSBED data warehouse being formed it is already contains required historical data for reporting the EPSBED. The reporting process of EPSBED each semester (to report the academic data each of program study) just take from an existing EPSBED data warehouse. Formerly, EPSBED reporting process used to using manual query to retrieve data from scattered tables on academic transactional database was usually takes a long time that was about five days. By the existence of this EPSBED data warehouse, process of reporting EPBED become faster and will be completed in a count of minutes, because the required data have been prepared in dimension tables and fact tables. Likewise, the previous semester's data had been documented at this EPSBED data warehouse as well. VII. CONCLUSION Based on the research that has been done, can be summed up some of the following: 1) Data warehouse implementation in UBL can help to solve problems in completing the EPSBED reporting quickly. Before the implementation of data warehouse, process of collecting data includes: extracting, transform and load were done by queries for EPSBED reporting needs. Usually it takes a month to complete all the reports by using queries. By the existence of data warehouse, then overall data of EPSBED which would be reported to DIKTI has been passed stages of extract, transform and load using Kettle. Thus, the data in EPSBED application is more quickly presented and have a valid data. It requires shorter time within only two hours in completing the EPSBED report as well. 2) The process of reporting EPSBED is done automatically and can be scheduled using the job components of Kettle, so it simplify and speed up the performance of EPSBED reporting team. 3) The results of EPSBED data warehouse processing can be used as a material consideration by the executive in determining policies. The information is presented in a form of a distribution of Cumulative Grade Point Average reports, a distribution of Semester Grade Point Average reports, reports of student's status and graduation rates of students, the number of active tenured lecturer reports and the number of tenured lecturers reports based on recent education in each of study program. VIII. REFERENCES [1] DIKTI. (2010). Pengembangan Pangkalan Data Pendidikan Tinggi. May, Direktorat Jenderal Pendidikan Tinggi, Kementerian Pendidikan Nasional Republik Indonesia. Dikti_Hery.ppt [2] Efraim Turban et al. (2007). Decision Support and Business Intelligent System. Pearson. [3] Inmon, W.H.(2005). Building The Data warehouse. New York: John Wiley and Sons, Inc.w [4] Ilah. (2010). Evaluasi Program Studi Berdasarkan Evaluasi Diri (EPSBED). May, Direktorat Jenderal Pendidikan Tinggi, Kementerian Pendidikan Nasional Republik Indonesia. 7

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