Predicting Success Study Using Students GPA Category

. Maintaining student graduation rates are the main tasks of a University. High rates of student graduation and the quality of graduates is a success indicator of a university, which will have an impact on public confidence as stakeholders of higher education and the National Accreditation Board as a regulator (government). Making predictions of student graduation and determine the factors that hinders will be a valuable input for University. Data mining system facilitates the University to create the segmentation of students' performance and prediction of their graduation. Segmentation of student by their performance can be classified in a quadrant chart is divided into 4 segments based on grade point average and the growth rate of students performance index per semester. Standard methodology in data mining i.e CRISP-DM (Cross Industry Standard Procedure for Data Mining) will be implemented in this research. Making predictions, graduation can be done through the modeling process by utilizing the college database. Some algorithms such as C5, C & R Tree, CHAID, and Logistic Regression tested in order to find the best model. This research utilizes student performance data for several classes. Parameters used in addition to GPA also included the master's students data are expected to build the student profile data. The outcome of the study is the student category based on their study performance and prediction of graduation. Based on this prediction, the university may recommend actions to be taken to improve the student achievement index and graduation rates.

ters that hich will mely: to -axis and For that is GPA made is vely per Q1 GPA < 2.5 and rate of progress <= 0 Q2 GPA < 2.5 and rate of progress > 0 Q3 GPA >= 2.5 and rate of progress <= 0 Q4 GPA >= 2.5 and rate of progress > 0 GPA segmentation will be used in a predictive model of graduation. Parameters used in addition to GPA also included the master's students are expected to form the student profile data.
Making predictions, graduation can be done through the modeling process by utilizing the college database. The more parameters should be used to form a comprehensive model anyway. Some algorithms such as C5, C & R Tree, CHAID, and Logistic Regression tested to look for the best model.
The outcome is student category based on their study performance and prediction of graduation. Based on this prediction, the college may recommend actions to be taken to improve the student achievement index and graduation rates.

Contribution and Research Output
The results of this research will generate quadrant student performance that will allow University to act help to the success of student learning, especially for quadrant 1, 2 and 3. For sharper handling required additional data that will result in the group with specific characteristics that require different handling tips.

Purpose of Research
The purpose of this study was to analyze the performance of the progress of student learning outcomes and prevent the failure of student learning to improve graduation index University. As the research data is limited to certain departments of several forces.

Research Methodology
Standard methodology in data mining i.e. CRISP-DM (Cross Industry Standard Procedure for Data Mining) will be implemented in this research.
• Business Understanding, the target is to understand the real problem which is faced by University including business context that influence the problem. • Data Understanding, the target is to determine the relevant parameters, variables and attributes related to the problem. • Data Preparation is an activity to manipulate data so that it's ultimately valid. This valid data will then be inputs to the modeling process. • Modeling, is a process of determining the best model that will be used to solve the problems. • Evaluation is a group of activities intended to evaluate in detail the accuracy of the output of the chosen model inclusive of changing business context that may alter the chosen model. The model has to be able to solve the defined problem. • Deployment the target is to use the output of the chosen model as a business tool to solve the problems.

Business Understanding
Once a prospective student accepted at universities and begin the process of learning, the student data will be entered into the database University. And then based on the progress of learning outcomes shown by GPA per semester, the student performance can be classified into four quadrants. By grouping based on this quadrant, the university can more easily monitor the progress of learning outcomes and immediately act to anticipate setbacks student achievement index by holding a mentoring program so that the expected progress is achieved. Target to be achieved is to understand the problems facing the following business context that influence.  Figure 3 Having perform  The pr process average amounts

Research Constraints
Complete supporting data will be greatly helpful to track the characteristics of the learning process of students inhibitor category. Unfortunately, such data cannot be obtained easily because it is in many cases very confidential. However with the available data, this study should be able to make the positive contribution.

Conclusion and Recommendation
The accuracy of the predictions against the actual data completion of the curriculum is from 87 % to 88.034 %. This proves that the resulting model can be used for the data set used. Model results from IBM SPSS Modeler streams will produce different accuracy when directly applied to the different data groups, for example to other universities, because the resulting parameter values will be different. Nevertheless forming method applied models have been tested, so that the same method, and different data sets should be re-modeling, to form the corresponding model. The prediction results and recommendations can be used by the counselor in a motivating student to intensify efforts to achieve learning success. For prediction accuracy, external information like activities, academic activities of students and family background data, should be included as a part of student demographic data.