Factor Analysis of Employee Job Satisfaction at Nene Mallomo Hospital Sidrap

From the results of the extraction process using the Principal Component Analysis method, it shows that the variable satisfaction with salary has a very close correlation with a factor of 0.846 or 84.6%, satisfaction with the attitude of superiors has a very close correlation with a factor of 0.833 or 83, 3%, satisfaction with the social aspects of work has a very close correlation with a factor of 0.794 or 79.4%. In comparison, satisfaction with the work environment is closely correlated with a factor of 0.615 or 61.5%. In comparison, satisfaction with rewards and sanctions has a fairly close correlation with a factor of 0.565 or 56.5%. Satisfaction with job security has a fairly close correlation with a factor of 0.495 or 49.5%. From the results of the Rotated Component factor analysis that the first-factor Component consists of the variables Satisfaction with the Work Environment, Satisfaction with Social Aspects in Work, Satisfaction with Job Security and Satisfaction with rewards and sanctions, these Component factors can be named or interpreted as a factor of satisfaction with comfort and safety at work. At the same time, the second-factor Component consists of two variables, namely Satisfaction with Salary and Satisfaction with the attitude of superiors. The second-factor Component can be named or interpreted as a prime policy factor. The analysis results using the Varimax rotation method with Kaiser Normalization show that on the diagonal, the factors (components) of the six variables are 0.835a, 0.808a, 0.711a, 0.747a, 0.471a, and 0.568a. Two factors will represent the six factors, which have the highest Component Transformation Matrix value, namely the Satisfaction with Salary factor of 0.835 or 83.5%, and Satisfaction with the attitude of superiors 0.808 or 80.8% affects team member job satisfaction at home. Sick of Nene Mallomo.


INTRODUCTION
Good work professionalism cannot be separated from the organization's attention on job satisfaction which is a picture of the desires and expectations that are felt as a result of their work (1). Job satisfaction is a condition in which a team member feels proud, happy, treated fairly, recognized and cared for by superiors, valued, feels safe because his work can produce something that fulfills his personal needs, desires, hopes, and ambitions so that he feels physical and mentally satisfied (2).
Nene Mallomo Hospital, one of the major hospitals in Sidrap, successfully passed the assessment stage of the Hospital Accreditation Commission (KARS) team of the Ministry of Health (Kemenkes) of the Republic of Indonesia. Nene Mallomo Hospital (Nemal Hospital) is located in Pangkajene City, the district capital. Previously, Arifin Nu'mang Sidrap Hospital won the complete predicate. The RI Ministry of Health's KARS assessment results recently stated that Nene Mallomo Sidrap Hospital managed to get the main title. That predicate, one grid under the complete predicate or five stars.
Given the importance of the existence of the Nene Mallomo Hospital in Sidrap Regency, the performance of the hospital employees must be good. The performance of the hospital will be good if the job satisfaction factor is met.
The fulfillment of job satisfaction of employees of Nene Mallomo Hospital in Sidrap Regency is expected to have an impact on high work motivation and optimal work productivity levels, and realizing a high level of job satisfaction will be a prerequisite for optimally improving the performance of employees of Nene Mallomo Hospital in Sidrap Regency. . Therefore, researchers are interested in examining the factors of job satisfaction including salary, promotion, supervision, co-workers, the work itself, and the work environment at Nene Mallomo Hospital, Sidrap Regency.

METHODOLOGY
This research is explanatory or explanatory, determining the type of explanatory research. The variables used in this study are team member job satisfaction (dependent variable), job satisfaction factor (independent variable). In this study, the object of this research was Nene Mallomo Hospital, Sidenreng Rappang Regency, South Sulawesi. The data used in this study are primary data and secondary data derived from interviews, questionnaires, and documentation. The data analysis technique used in this research is factor analysis.
In this study, the analytical tool used is factor analysis, a model with no independent and dependent variables. Factor analysis does not classify independent and dependent variables but looks for interdependence relationships between variables to identify the dimensions or factors that compose them. Where: Fi = factor I estimate WI = factor weight or factor coefficient score X K = number of variables The main principle of factor analysis is a correlation, so the assumptions related to the statistical correlation method are: 1) The magnitude of the correlation or correlation between independent variables must be strong enough. 2) Large partial correlation, the correlation between two variables by assuming the other variables remain. 3) Testing a correlation matrix is measured by the Barlett Test Of Spericity or by the Measure Sampling Adequacy (MSA) (3).

Factor Analysis Results
Before further analysis using the KMO test (Kaiser Meyer Olkin), the Bartlett Test, and the MSA (Measures of Sampling Adequacy) test, provided that the KMO value must be above 0.5 while the Bartlett Test has a significance level below 0.05 as well as the value of MSA must be above 0.5, for further analysis, here are the results of the KMO and Bartlett Test as shown in the following table: Of the six variables that have been determined to have values above 0.5 or MSA values > 0.5, then all of these variables can still be predicted and can be analyzed further.

Rotational Factoring Analysis
After all the variables have sufficient values, the next step is to carry out the core process of factor analysis, which is to extract a set of existing variables so that one or several factors are formed. In carrying out this extraction process, the method used is Principal Component Analysis, after six factors are formed, then analyze to find out which variables will be included in which factor, then a rotation process is carried out using the varimax method (part of the orthogonal).  (4).
In looking at the formed factors, it must be seen in the Eigenvalues in the Total Variance Explained table. According to Singgih Santoso (2004:43), explains that the Total Variance Explained table describes the number of factors formed. The eigenvalue must be seen to determine the formed factor; it must be above one (1). If it is below one, then it is not correct. Eigenvalue shows the relative importance of each factor in calculating the variance of the total variables present (4). The number of eigenvalues, the arrangement is always sorted from the largest to the smallest, as shown in the following table: In table 4 above, it can be seen that there are six variables (components) that are included in the factor analysis, namely job satisfaction which consists of satisfaction with salary, satisfaction with superiors' attitudes, satisfaction with social aspects, satisfaction with the work environment, satisfaction with rewards and sanctions and satisfaction with job security and of the six variables according to the value of Initial Eigenvalues only two factors are formed because with two factors, the total eigenvalue is already 0.691 < 1 therefore only two factors are limited, as well as in the Rotation column Sums of Squared Loadings there are two factors that are formed. After knowing that according to Initial Eigenvalues 2 factors, the component matrix table shows the distribution of the six variables on the two formed factors. While the numbers in the table are factor loading, which shows the magnitude of the correlation between a variable and a factor, the process of determining which variables will be included in the factor is carried out by comparing the magnitude of the correlation in each row, as shown in the following table: In Table 5 it appears that the variable satisfaction with salary has a correlation with a factor of 0.846 or 84.6%, this shows that satisfaction with salary has a correlation value that is very closely related to the factor, while satisfaction with the attitude of superiors has a correlation with a factor of 0.833 or 83.3%, this shows that satisfaction with the attitude of superiors has a correlation value that is closely related to factors, as well as satisfaction with the social aspect of work has a correlation with a factor of 0.794 or 79.4%, this shows that satisfaction with social aspects in work has a correlation value that is very closely related to factors, while satisfaction with the work environment has a correlation with a factor of 0.615 or 61.5%, this shows that satisfaction with the work environment has a correlation value that is closely related to factors, satisfaction with rewards and sanctions have a correlation with fac tor of 0.565 or 56.5%, this shows that satisfaction with rewards and sanctions has a correlation value that is closely related to factors, as well as satisfaction with job security has a correlation with a factor of 0.495 or 49.5%, this shows that satisfaction to salary has a correlation value that is quite closely related to the factor. According to Singgih Santoso (2004:45), the Component Matrix shows the distribution of existing variables with the formed factors. While the numbers in the Component Matrix table are Factor Loading which shows the magnitude of the correlation between a variable and the existing factors (4). The component matrix resulting from the rotation process (rotated component matrix) shows a clearer and more significant distribution of variables. It can be seen that now the loading factor that used to be small is getting smaller (not shown), and the large loading factor is getting bigger. Below will be explained into which factors a variable exists, namely: From the table above, it provides an illustration that if two factors are formed, each factor will explain several predetermined variables, namely: factor 1 consists of the variables Satisfaction with the Work Environment, Satisfaction with Social Aspects in Work, Satisfaction with Job Security and Satisfaction with Awards, and sanctions; factor 2 consists of satisfaction with the attitude of superiors and satisfaction with salary. According to Singgih Santoso (2005:47), the Component Matrix of the rotation process (Rotated Component Matrix) shows a clearer and more real distribution of variables. With the rotation process, the loading factor that used to be small is getting smaller, and the large loading factor is getting bigger (5).
Based on the Component Matrix table grouping six variables into two factors and the Rotated Component Matrix table, grouping these variables into two factors, then the next transformation will be carried out from the six variables into fewer variables, meaning that the six variables will be represented by several variables as shown in the following table:  Table 7 above, it shows that on the diagonal of the factors (components) the six variables are 0.835a, 0.808a, 0.711a, 0.747a, 0.471a and 0.568a respectively. On the diagonal, showing a number above 0.5, there are five factors and only one factor whose number is below 0.5, namely factor 5 (component 5).
After going through the analysis of the factors mentioned above, there is only one factor on the diagonal that shows a value below 0.5; it can be concluded that the five factors can represent the six factors determined. However, of the five factors based on the Extraction Method: Principal Component Analysis, there are only two factors formed, for that of the five factors, two factors will be determined that will represent the six factors, namely those with the highest Component Transformation Matrix value which is at (Component Transformation Matrix). 1) is 0.835a and (Component 2) is 0.808a.

DISCUSSION
After going through factor analysis, the next step is to interpret the factors that have been formed. This is done to represent the member variables of the factor. From the results of the study, it can be seen that the factors of Satisfaction with Salary, Satisfaction with the attitude of superiors, Satisfaction with social aspects in work, Satisfaction with the work environment, Satisfaction with rewards and sanctions, and Satisfaction with job security can affect job satisfaction at Nene Mallomo Hospital, the results This research is in line with Agung and Aini's research (2019) which shows that factors of salary/welfare, interpersonal/co-worker relationships, quality of supervision, job characteristics and opportunities for growth/promotion, affect team member job satisfaction.
Based on the results of the Rotated Component factor analysis that the first-factor Component consists of the variables Satisfaction with the Work Environment, Satisfaction with Social Aspects in Work, Satisfaction with Job Security and Satisfaction with rewards and sanctions, these Component factors can be named or interpreted as a factor of Satisfaction with comfort and safety at work. At the same time, the second-factor Component consists of two variables, namely Satisfaction with Salary and Satisfaction with the attitude of superiors. The second-factor Component can be named or interpreted as a prime policy factor.
From the results of the Component Transformation Matrix analysis of this study, it shows that if increasing team member job satisfaction at Nene Mallomo Hospital has limitations in terms of cost, time, and level of difficulty, then the analysis results show that the factors of Satisfaction with Salary and Satisfaction with superiors' attitudes have The strongest influence on team member job satisfaction at Nene Mallomo Hospital, it is also