INFLUENCE OF ORGANIZATIONAL COMMITMENT, WORK SATISFACTION, WORK MOTIVATION TOWARD ORGANIZATIONAL CITIZENSHIP BEHAVIOR

DOI:10.31933/DIJEMSS Abstract: The purpose of this study was to measure the effect of organizational commitment, job satisfaction and work motivation on organizational citizenship behavior in SMK Satria Srengseng teachers, this research is quantitative. The population is all teaching teachers and staff. The technique in taking samples uses nonrandom sampling in the form of saturated samples with a total of 70 teachers. Data obtained from the results of the study were analyzed using multiple linear regression. T-test results for the variable organizational commitment, job satisfaction and partial work motivation are positive and significant for organizational citizenship behavior (OCB). F-test results for the variable organizational commitment, job satisfaction and work motivation together are significant for organizational citizenship behavior (OCB). Suggestions for further research are to include other variables that also have a strong influence in shaping organizational citizenship behavior (OCB).

achieving a goal. When associated with work motivation, the perseverance of trying from an individual is directed at the goals of his work.
According to McClelland (in Latif and Latief, 2018: 115) work motivation has three dimensions, namely: a) The need for achievement (motivation for achievement). This need is in the form of encouragement to outperform and relate to something that is standardized and wrestles towards success. b) The need for power (motivation for power). This need is the need to make other individuals behave in a way that is coercive or a manifestation of one's expression to influence and control the behavior of other individuals. c) The need for affiliation / belonging (motivation for affiliation / ownership). This need is a need to establish relationships with other individuals who are familiar and friendly where someone tries to express a close desire to establish friendships with other parties.

RESEARCH METHODS
Based on the title and background of the problem previously discussed, this type of research falls into the category of causality research. According to Ferdinand (2014: 7) causality research is a form of research with the aim of seeking understanding of the causal relationship between more than one variable that has been developed in management.
According to Ferdinand (2014: 171) population is a combination of all the elements that can form events, things or individuals who have similar characteristics or characteristics and become the focus of a researcher. The study population of 70 people.
Sampling in this study uses the category of nonprobability sampling in the form of saturated sampling. According to Indra and Cahyaningrum (2019: 54) nonprobability sampling is a way of determining samples where members of the population do not get the same opportunity during the sample selection process. According to Sugiyono (2014: 126) saturated sample is a sampling technique if all members in the population are sampled and this technique is often used in populations with small numbers or in studies that aim to generalize with relatively small error rates.
According to Ferdinand (2014: 229) descriptive statistics are used to get an empirical picture through data obtained through research where the types can be frequency distribution, average statistics or index numbers.
Validity test is carried out using the Statistical Package Service Solution (SPSS) number 25 program using correlations from Pearson Product Moment. The principle of validity testing on Pearson Correlation Product Moment is by connecting the score of each item with the total score obtained. The process of knowing whether an item is valid or not can be seen in the magnitude of significance where if the significance is less than 0.05 then the item is considered valid or valid but conversely if the significance is greater than 0.05 then an item is considered invalid or invalid. Another method is to compare the r count (the result of data processing) with the r table score (obtained from r table), if the value is positive and r the calculated result is greater than r in the table then the item is considered valid, applies vice versa if r count results are more smaller than r in the table then the item is considered invalid (Purnomo, 2017: 70).
According to Ferdinand (2014: 218) a scale or measurement instrument is considered to be reliable if the instrument consistently or consistently produces relatively similar values each time a measurement is made. According to Herlina (2019: 70) the reliability test using the Cronbach alpha method is generally divided into categories: • Cronbach's alpha with a value <0.06 = reliability is not good • Cronbach's alpha with a value of 0.6 -0.79 = reliability is accepted • Cronbach's alpha with a value> 0.08 = good reliability Normality test is used to see the value of residuals having a normal distribution or even having an abnormal distribution. The model in regression is said to be good if the value of the residuals is fairly or normally distributed. In the normality test that is tested is the residual value only and not each variable (Ansofino et al., 2016: 94). In this study the normality test used by the name Kolmogorof-Smirnov. According to Ismail (2018: 193) the Kolmogorof-Smirnov test was carried out by comparing the distribution of research data with the standard normal distribution in which the standard normal distribution was distributed in the form of z-scores.
Multicollinearity test is used to determine the presence and absence of a high relationship between each independent variable in the multiple linear regression model. If there is a high relationship between independent variables, it can interfere with the relationship that occurs between the independent variable and the dependent variable (Ansofino et al., 2016: 94). This multicollinearity test uses Pearson correlation on each independent variable.
Heteroscedasticity testing is used to determine the presence or absence of dissimilarities from one observation residual to another. A regression model can qualify if the residuals of observations from one to another have similar variance (Ansofino et al., 2016: 94). The method used in this heteroscedasticity test is Glejser. The Glejser method is done through the process of regressing independent variables on absolute residual values. If the significance value of the independent variable over the absolute residual value is greater than 0.05, it is stated that there are no symptoms of heteroscedasticity.
According to Ghozali (2013: 96) multiple regression analysis is an analysis used to measure the effect of two or more independent variables on the dependent variable and also to determine the direction of the relationship that occurs between the independent variable and the dependent variable.
Multiple regression analysis is required to meet the requirements of the classic assumption test which has a normal distribution of data, non-multicollinearity and nonheteroscedasticity.
This study uses a hypothesis test in the form of a determination test (R2), F-test, t-test, and correlation test between dimensions.
F statistical test is used to determine whether the independent variables contained in the regression model have a joint or simultaneous effect on the dependent variable. The results of the F test calculation are then compared with the F table values. If the value of F from the calculation results is greater than F in the table, together or simultaneously the independent variables affect the dependent variable. F-test decision making can be seen from the large significance. If the significance is below 5% or 0.05, together or simultaneously the independent variable has an influence on the dependent variable, and vice versa if the significance is above 5% or 0.05, then simultaneously or simultaneously the independent variable has no effect on dependent variable (Ma'arif in Wati and Primyastanto, 2018: 196).
Partial or t-test is used to find out individually or partial influence of the independent variable on the dependent variable. T test is carried out by making comparisons of the calculated t value with the t value in the table or seeing the significance value. For example the value of t arithmetic> table t and the value of the significance <α 0.05 can be said to be the independent variable has a significant effect on the dependent variable. The reverse also applies if the value of t arithmetic <from t table and the value of the significance> α 0.05, it can be said that the independent variable does not have a significant effect on the dependent variable (Rusmana et al., 2019: 200) The use of the coefficient of determination (R2) can get a picture of how closely the regression relationship between the independent variables with the dependent variable. The higher the value of R2 (0 ≤ R2 ≤ 1) the higher the estimate of a regression model (Nugraha, 2014: 191). The degree of determination coefficient can be calculated using the formula: Correlation analysis in statistical analysis is used to measure the level of relationship that occurs between the independent variables with the dependent variable. Correlation analysis between dimensions is used to determine the relationship of the dimensions of the independent variable to the dependent variable. Data processing in correlation analysis between dimensions uses SPSS (Statistical Product for Service Solution) version 25.

FINDINGS AND DISCUSSION Validity and Reliability Test
The results of the validity and reliability test obtained valid and reliable items as many as 19 items of organizational commitment variables, 36 items of job satisfaction variables, 13 items of work motivation variables, 19 items of organizational citizenship behavior variables

Normality Test
Normality test is used to determine whether or not the distribution of samples from the study population is normal. The normality test can only be carried out after passing the stages of the validity and reliability tests. The normality test in this study uses the nonparametric test that is Kolmogorov-Smirnov. The normality test has the results which can be seen in the table below: The assessment criteria of the Kolmogorov-Smirnov Test are if asymp. Sig less than 0.05, you could say the residual data are not distributed in normal form. If asymp. Sig less than 0.05, you could say the residual data is distributed in normal form. In Table 1 shows the results of 0.2 which means greater than the value of 0.05. This indicates that the residual data is normally distributed.

Multicollinearity Test
The second classic assumption test is the multicollinearity test. The results of this multicollinearity test were seen based on the amount of tolerance and variance inflated factor (VIF). The results of multicollinearity testing in this study will be displayed in the table below: Referring to table 2, the tolerance score for each variable is greater than 0.1 and the VIF value for each variable is smaller than 10 so that it can be concluded that the regression model in this study is free from multicollinearity between independent variables.

Heteroscedasticity Test
The last classic assumption test is the heteroscedasticity test. This test is done after passing the normality test and multicollinearity test. The heteroscedasticity test in this study used the Glejser method by conducting a regression analysis of the independent variables on the absolute residual value. The results of heteroscedasticity testing can be seen in the following table: Referring to Table 3 found that all Sig. the independent variable or the independent variable is greater than 0.05. This indicates that all independent variables do not have symptoms of heteroscedasticity in residual data or in other words homocedasticity.

Multiple Linear Regression Analysis
Linear regression is needed to measure the magnitude of the relationship that occurs between two variables or it can be more. Linear regression is also needed in determining the direction of the strength of the relationship that occurs between the independent variable with the dependent variable, the direction can be positive or negative. Regression analysis in this study is used to analyze whether there is an influence of the independent variable Organizational Commitment (X1), Job Satisfaction (X2) and Work Motivation (X3) on the dependent variable, Organizational Citizenship Behavior (Y), and how much influence the independent variable has on the independent variable bound together. The following table will be presented based on the results of the multiple linear analysis test: Based on Table 4

T-test
T-test was conducted to find out the magnitude of the effect of the independent variables individually on the dependent variable. This t-test is also intended to estimate the extent of the contribution of changes that occur in each of the independent variables influencing the magnitude of changes in the dependent variable. The criteria for drawing conclusions from the t-test are explained in the sentence below: • If the value of t is calculated> t in the

F-Test
The F test is also called the simultaneous test or ANOVA. The F test is used to determine whether all independent variables in the regression series have a joint influence on the dependent variable. F-test results using SPSS 25 will be presented in the Based on Table 5 it is known that the results of the Sig. of 0 and smaller than 0.05. This indicates the variable Organizational Commitment, Job Satisfaction, and Work Motivation simultaneously have a significant effect on Organizational Citizenship Behavior (H4 accepted).

R 2 Test
R 2 test is used to obtain information on the amount of contributions or contributions that the independent variable gives to the dependent variable. The results of the determination test can be seen in the following table: Based on Table 6, the R2 value of 0.702 is obtained, which means that the variable Organizational Commitment, Job Satisfaction, and Motivation contribute 70.2% to the Organizational Citizenship Behavior variable. The remaining contribution of 29.8% can be explained by other variables.

Matrix Correlation
Correlation test between dimensions is performed to determine the magnitude of the relationship between each dimension of all independent variables with each dimension on the dependent variable. Correlation test results using the Pearson's Correlation two-tailed method and will be described in the following table: Based on the results of data processing carried out using SPSS 25, several research results were obtained regarding "The Effect of Organizational Commitment, Job Satisfaction, Work Motivation Against Organizational Citizenship Behavior". In the discussion will be answered some of the problems that exist in the research hypothesis which is supported by some literature-literature which later whether will support the hypothesis or reject the hypothesis.
The results of the study indicate that Organizational Commitment has a significant positive effect on Organizational Citizenship Behavior. The results of this study are supported by the results of previous studies conducted by Yuliani and Katim (2017: 401). the aspect with the strongest relationship is aspect Continuation with Civic Virtue aspect which can be interpreted that teachers want to remain in the organization due to the benefits gained personally so that they are willing to follow organizational governance and school policy debates. The weakest dimension is the Affective dimension with the Altruism dimension which can be interpreted that teachers have an emotional attachment to the organization by engaging in various activities and enjoying their membership. This emotional attachment prevents teachers from creating problems with their fellow teachers.
The results of the study indicate that Job Satisfaction has a significant positive effect on Organizational Citizenship Behavior. The results of this study are supported by the results of previous studies carried out by Musringudin et al. (2017). The strongest dimension of the relationship is the Coworkers dimension with Courtessy which can be interpreted that the teachers feel happy and comfortable with their colleagues so that they make them pay more attention to the rights of other teachers and do not want to cause problems with other teachers. The weakest dimension is Promotion dimension with Conscentiousness dimension which can be interpreted that teachers feel happy and have the same opportunities for promotion so that they are willing to work beyond the specified time but still follow the standard rules and try to improve the quality of service.
The results of the study indicate that Work Motivation has a significant positive effect on Organizational Citizenship Behavior. The results of this study are supported by the results of previous studies carried out by Nurnaningsih and Wahyono (2017: 365). The most powerful dimension is the Need of Achievement dimension with the Civic Virtue dimension, which means the need for achievement possessed by teachers results in them being willing to follow organizational governance and school policy debates. The weakest dimension is the Need of Affiliation dimension with the Conscentiousness dimension which can be interpreted that teachers have the desire to establish friendships with other fellow teachers so that they encourage their enthusiasm to work beyond the specified time and try to improve the quality of service.
The results of research based on the F test show that Organizational Commitment, Job Satisfaction and Work Motivation simultaneously or simultaneously have a significant influence on Organizational Citizenship Behavior.

CONCLUSION AND SUGGESTION
Based on the results of data processing that has been shown in the previous chapter on "The Effect of Organizational Commitment, Job Satisfaction, Work Motivation on Organizational Citizenship Behavior of Srengseng Vocational School teachers" then can draw conclusions as below: 1) Organizational commitment has a significant positive effect on Organizational Citizenship Behavior. The Continuation dimension correlates highest with the Civic Virtue dimension. 2) Job Satisfaction has a significant positive effect on Organizational Citizenship Behavior. The Coworkers dimension correlates highest with the Courtessy dimension.

3) Work motivation has a significant positive effect on Organizational Citizenship
Behavior. The Need of Achievement dimension correlates highest with the Civic Virtue dimension. 4) Organizational Commitment, Job Satisfaction and Work Motivation simultaneously have a significant effect on Organizational Citizenship Behavior Based on the results of the study and the conclusions previously presented, the researcher provides input for school leaders and researchers who will further research as follows: 1) Researchers can then enter the variables or other factors that are suspected to affect Organizational Citizenship Behavior in addition to the variables or factors that exist in this study. 2) The foundation or the principal should increase simultaneously the Organizational Commitment, Job Satisfaction and Work Motivation of the teachers to be more effective in improving Organizational Citizenship Behavior. Teacher job satisfaction is a top priority to be improved. 3) The foundation or principal is expected to be able to further enhance the facilities obtained by teachers such as transportation funds, health and pension funds, as well as other funds. 4) The foundation or the principal is expected to be able to further enhance the competence of the teachers so that they feel comfortable and are not burdened by less competent teachers. 5) The foundation or the principal is expected to further increase the motivation of achievement of teachers through training or increasing rewards.