IDENTIFY FACTORS AFFECTING STUDENT ACHIEVEMENT USING THE BINARY LOGISTIC REGRESSION ANALYSIS

The success of students in completing their study program is indicated by the achievements reflected in their Grade Point Average (GPA). Grade Point Average (GPA) is expressed in numerical form and is an academic achievement calculated based on all courses taken in all semesters that have been passed by students. This study aims to obtain a model of external factors that affect Grade Point Average (GPA) and variables that affect Grade Point Average (GPA). The response variable in this study is Grade Point Average (GPA) which is categorical and divided into two categories. The method used is binary logistic regression analysis. This study used primary data with the research subjects being second, third, and fourth-semester students in the Mathematics Education Department of Alauddin Makassar State Islamic University. The indicators on the instrument can be categorized as valid and reliable to measure service variables, learning, facilities and infrastructure, and student interest in entering college. The results of binary logistic regression analysis show that service has a significant effect on Grade Point Average (GPA), besides that learning, facilities, infrastructure, and the number of students enrolling in college have an effect but are not significant on Grade Point Average (GPA).


INTRODUCTION
ducation is one of the main focuses of every individual in this modern era.Therefore, education is a fundamental aspect that needs attention.The main purpose of education is to improve the quality of Human Resources (HR) both at the personal and community levels.Education, as a process of human interaction, exchanges knowledge, mentality, character, and morals.The development of education has a significant impact on various fields such as economics, politics, socio-culture, information technology development, and others.One reflection of the quality of superior Human Resources (HR) is shown by their achievements while studying basic to advanced education, one of which is higher education.
Universities are places for higher education, research, and community service.A university has specific criteria such as bachelor's, professional, master's, and doctoral degrees.Research is a rule-based learning activity in various disciplines to find truth and solve problems.Community service is the application of science to society to improve the quality of human resources (HR).Higher education is a level of higher education with various elements and organizes academic and professional education in various scientific disciplines (Indrajit & Djokopranoto, 2004).
The university's mission is to continuously improve the quality of education.Quality education means human resources (HR) that are ready to E compete globally.Therefore, a quality university must constantly improve the quality of its activities.As a unique institution in the world of education, universities must have specific goals for the internal and external characteristics of the educational environment have their own goals and ideals, which are stated in the vision and mission, which are developed with stakeholders in the process of formation (Sayidah, Ady, Supriyati, Sutarmin, Winedar, Mulyaningtyas, & Assagaf, 2019).
Some strategic elements that require special attention in developing the university are the quality of academic programs, the quality of human resources (HR), the quality of services, and the quality of supporting facilities and infrastructure.To improve these strategic elements, an implementation method is needed, i.e. good and quality governance.Competition in the world of higher education in Indonesia is so great that all universities require fundamental changes to be able to compete and produce competent graduates in the era of advances in information and communication technology.One indicator of the quality and success of college graduates is the Grade Point Average (Suti, Syahdi, & Didiharyono, 2020).
The success of students in completing their study program is indicated by the achievements reflected in their Grade Point Average (GPA).Grade Point Average (GPA) is expressed in numbers and is an academic achievement calculated based on all courses taken in all semesters that have been passed by a student.Academic mastery will be proportional to the student's Grade Point Average (GPA).The better the student's academic mastery, the better the GPA, and vice versa.Many factors affect a student's grade point average (GPA).
The student achievement index is influenced by two factors, namely internal student factors and external student factors (Tampil, Komaliq, & Langi, 2017).Students' internal factors include attitude, interest, and learning motivation (Nadziruddin, 2006).Factors from external students include facilities and infrastructure of the campus or school environment, learning system, interaction or communication between lecturers and students, quality of technology, and campus internet (Rugutt & Chemosit, 2005).In addition, race and culture, the number of credits received, communication between students and teachers, organizational activities, and activities in the field of education (Semer & Harmening, 2015).
Other studies that examine the factors that influence the student achievement index include Modeling Factors Affecting the Achievement Index of ITS Undergraduate Program Students Using Ordinal Logistic Regression (Dhania, 2010), Ordinal Logistic Regression Analysis of Factors Affecting Student GPA (Setyawati, 2020).Study of Factors Affecting Academic Achievement in Higher Education: A Structural Equation Modeling Approach (Rugutt & Chemosit, 2005).Exploring significant factors that impact the academic success of student veterans in higher education (Semer & Harmening, 2015).
From various studies that have been conducted by previous researchers, this study will describe the use of one method to identify factors that affect achievement.Data on the response variable is categorical which consists of two responses.The analysis used to determine the relationship between variables with the response variable consisting of two categories is logistic regression (Sweet & Martin, 2014).Logistic regression is a data analysis that is commonly used to detect relationships between variables with multicategorical responses (scaling data at various levels) (Hosmer & Lemeshow, 2000) or in special conditions in regression analysis where the response variable is not continuous and the predictor variables are qualitative or quantitative (Hair, Black, Babin, Anderson, Black, & Anderson., 2018;Peng, Lee, & Ingersoll, 2002).The term logistic regression is given based on the structure of the response variable in the form of a logit transformation (Bozpolat, 2016).Logistic regression analysis has assumptions, namely independence and multicollinearity tests (Senaviratna & Cooray, 2019).So, This research uses the binary logistic regression analysis method.

METHODS
This study used primary data, namely data on Mathematics Education students of Alauddin State Islamic University Makassar in semesters 2, 4, and 6.
Table 1 The response variable in this research is the student's Grade Point Average.GPA (Grade Performance academic) is the overall result of the grades obtained by students during the learning process, the greater the number obtained by students, the better the academic achievement they achieve (Yanti, Windarto, & Suhada, 2019).GPA is a measure that is conceptualized as a measure of academic achievement or an indication of how well students are doing during their studies in college.Four predictor variables influence the response variable, namely service, learning, facilities and infrastructure, and University entrance pathways.The composition of the data in this study is.(Syamsuryadin & Wahyuniati, 2017).The formula used is (Frankel, Wallen, & Hyun, 2020) The expected result is to receive with criteria or .After the validation test is carried out, a reliability test is carried out to determine the extent to which the instrument can be trusted in making measurements (Syamsuryadin & Wahyuniati, 2017).

With
(3) The hypotheses that can be tested are : : The Statistics of the Pearson Chi-Square are as follows: The result to be achieved is to reject with or (Agresti, 2019).b.Test the significance of ordinary logistic regression analysis, either partially or simultaneously.
This test is used to determine the significance of the β coefficient as a whole or simultaneously if the test shows significant results.It is concluded that the resulting model is suitable and the response variable can be simulated significantly.The test concept is as follows.
There is at least one The test statistics are with ( ) as the number of categories of response variables and p as the number of categories of predictor variables.
For significance rejected if with squared is the value of a random variable in the Chi-Square distribution table or p−value < α (Hosmer & Lemeshow, 2000).Simultaneous testing with β parameters on response variables has been carried out, the next step is to perform partial tests of β parameters on response variables.This test is performed to identify the significance of the predictor variable influencing the variable.The testing hypothesis is as follows Dengan statistik uji The statistics of the W teis st follow the chi-squared by rejecting if or (Hosmer & Lemeshow, 2000).c.A model suitability test was carried out to find out whether the model obtained was appropriate or not.This test is carried out to determine whether the model obtained using the parameter significance test has been feasible.The Goodness of Fit test is performed using the Goodness of Fit test Hosmer-Lemshow and the test hypothesis is as follows H0: There is no significant difference between the observations and the possible predictions of the model H1: There is a significant difference between the observations and the possible predictions of the model With test statistics as follows With: : The value of the response variable in the k-th group ̅ : average probability estimation : Number of category combinations in a concurrent model : Many observations of the k-th group The Hosmer-Lemeshow test statistics follow the Chi-Square distribution with a free degree of g−2 resulting in a decision to reject if ̂ or (Hosmer & Lemeshow, 2000).d.Interpret the results that have been obtained.
The predictive coefficient of the predictor variable represents the change in the value of the response variable for each unit change in the predictor variable.The odds ratio is used to determine the functional relationship academic service process, the willingness of library staff to provide assistance in borrowing books, ease of students carrying out the mentoring process with lecturers, and well-arranged lecture schedules.Based on the validity test for each indicator on the service variable, it is known that the correlation value obtained or and .So it can be concluded that H0 is rejected.So, there is a relationship between each indicator.There are five indicators in the teaching variable, namely the lecturer's attention to students who do not understand the lecture material, the lecturer's punctuality in starting and ending the lecture, the delivery of the material according to the plan for each meeting, the delivery of the material is given clearly and accompanied by examples of problems, the learning media used by the lecturer is very to help students in the learning process.Based on the validity test for each indicator on the Learning variable, it is known that the correlation value obtained or and .So it can be concluded that H0 was rejected.So, there is a relationship between each indicator.Reliability tests on Learning variables obtained Cronbach alpha = 0.7774.The value is greater than 0.6.Thus, H0 was rejected.which can be concluded that the indicators used in Learning variables are reliable or consistently used as a measuring tool.There are five indicators in the facilities and infrastructure variable, namely completeness of classrooms, facilities and completeness of books in the library, laboratory facilities, WIFI connection access, and completeness of learning media, Based on the validity test for each indicator on the variable of facilities and infrastructure, it is known that the correlation value obtained or and .So it can be concluded that H0 was rejected.So, there is a relationship between each indicator.Reliability tests on facilities and infrastructure variables obtained Cronbach alpha = 0.7181.The value is greater than 0.6.Thus, H0. rejected.It can be concluded that the indicators used in the variables of facilities and infrastructure are reliable or consistently used as measuring instruments.

Multicollinearity Test
The results of the multicollinearity test for predictor variables can be seen in the following table.

Modeling Using Binary Logistic Regression Method a. Independence Test
The hypotheses used to determine the relationship between predictor variables and response variables are as follows.
: : Here is a table of independence test results for each predictor variable Table 10.Independence test results

Variables
Based on the table above, information is obtained that the predictor variable that has a significant relationship with the response variable is X1.This is indicated by the p−value < α score, with α = 5%.The variables X1, X2 and X3 have a relationship but are not significant.

b. Concurrent tests
Concurrent tests were conducted to determine the influence of predictor variables on response variables with hypothesis tests as follows there is at least one Based on the table, the results of the first partial test are that variable X1 has an effect in a manner significant to variable Y. Variables X2, X3, and X4 have an effect but are not significant to variable Y.The resulting model is as follows.
( ) The next step is looking for a second logit model with the removed variable predictor of having that more tall and other.The hypothesis that will tested on model fit is as follows : There is no significant difference between the observed results and the possible prediction results of the model : There is a significant difference between the observed results and the possible prediction results of the model Table 17.Uji Goodness of fit ̂ Because ̂ it is accepted, it can be concluded that There is a significant difference between the observed results and the possible prediction results of the model.

2) Interpretation of Models and Parameters
Interpretation is done on the best model, which is model 4. The value of Pseuduo-R 2 =0.038 or 3.8% is obtained, meaning that 3.8% of the variation in the response variable can be explained by the model formed.

Classification Predictions
Actual ( ) Based on the predicted information, from 126 observations, 58 students obtained GPAs with classification 1, where the model managed to correctly predict 30 people, and the rest were predicted by classification 2. There were 68 students with a GPA with classification 2, where the model can correctly predict as many as 37 while the rest are predicted by classification 1.The results of the APER calculation can be concluded that the model can correctly predict by 53.1%.
respondents : Score each item on the instrument : Score each item on the criteria With the following test criteria:: No relationship : There is a relationship . Variables research

Table 2 .
Data structure

Table 4 .
Reliability test for service variable The value is greater than 0.6.Thus, H0. rejected.It can be concluded that the indicators used in service variables are reliable or consistently used as a measuring tool.

Table 6 .
Reliability test for learning variable

Table 7 .
Test the validity of the Facilities and Infrastructure variable

Table 8 .
Reliability test for Facilities and Infrastructure variable

Table 9 .
Multicollinearity test result So receive H0.It can be concluded that there is no multicollinearity or relationship between predictor variables.

Table 11 .
Concurrent test results Partial tests were conducted to find out which predictor variables had a significant effect on the response variable, the hypothesis is as follows.

Table 12 .
The results of the first partial test

Table 13 .
The results of the second partial test Based on the table above, it can be stated that model 4 is the best because it obtains the smallest AIC. 1) Model Fit Test