The Influence of Competence on Employee Performance at the Staffing Agency and Human Resource Development in Bone Regency

Employee competence in an organization is very decisive in achieving an organizational goal. The better the human resources in an organization, the more opportunities to achieve effective and efficient organizational goals will be realized. This research was carried out with the aim of knowing how competence influences employee performance at the Personnel Agency and Human Resource Development Agency Bone regency. The stages of the research method, starting from theoretical studies and empirical studies, preparation of proposals, testing the validity and reliability of instruments, data collection, analysis and discussion. The data analysis used is multiple linear regression analysis. The results of the study show that there is a strong relationship and a significant influence on Academic Knowledge, Expertise and Communication in influencing motivation, thereby increasing employee performance.


INTRODUCTION
In order to create "good governance" which is a synergy between government, private and community components, as well as support the competitiveness of local governments in developing regional potential, each regional government organization needs to develop professional and highly competent human resources who will become centers of regional excellence (Ayu et al., 2019;Dwiyanto, 2014;Niswaty et al., 2016).
In government organizations that have employees based on the competencies required by their main duties and functions, they will continue to have good organizational performance, thereby increasing the quality of the public services provided by these organizations. Adequate employee competence will make employees skilled in carrying out their main duties and functions. They will be more creative in carrying out tasks and quickly able to adapt to their work environment (Kartika & Sugiarto, 2016;Putri, 2015).
The quality of human resources is one of the factors to increase the performance productivity of an organization or agency. Therefore, it is necessary to have high competence human resources because competence will be able to support the improvement of employee performance. Employee competence affects performance (Akhmad, 2016;Muhammad Busro, 2018;Sedarmayanti, 2001). The higher the competency possessed by employees and in accordance with the demands of the job, the employee's performance will increase because competent employees usually have the ability and willingness to quickly overcome work problems they face, do work calmly and full of confidence, see work as a an obligation that must be carried out sincerely, and openly improve self-quality through the learning process. Psychologically this will provide meaningful work skills and a sense of personal responsibility regarding the results of the work done, which in turn will improve employee performance.
The purpose of carrying out this research is to find out how much influence the competencies (knowledge, communication and skills) possessed by employees of the Bone District Personnel Agency, education and training in improving their performance, so that they can provide good service to Civil Servants in terms of staffing. This research is also expected to provide a solution to the problems that have been experienced by employees of the Personnel and Human Resources Development Agency in Bone district who are still considered unresponsive in providing services, lack of sense of responsibility for the work given and there are still many employees who do not understand the tasks they should be carrying out. With this research, it is hoped that a new paradigm will emerge regarding the concept of good service and work according to capabilities and expertise by utilizing their knowledge and expertise, in order to achieve maximum and efficient performance.

METHODS
This research will be carried out at the Staffing and Human Resources Development Agency in Bone Regency. This research is explanatory in nature, namely a form of research that will explain the causal relationship between the independent variables consisting of academic knowledge and expertise and the dependent variable, namely employee performance at the Staffing, Education and Training Agency of Bone Regency. For this, researchers used two types of research to collect the necessary data, namely: library research and field research.
The population in general is defined as the entire object to be studied (Sugiyono, 2011), besides acting as (respondents) in providing information related to the research topic and population, namely as many as 60 people. The data collection techniques used are as follows: observation, questionnaire and documentation. Furthermore, the data processing technique used is quantitative, where to find out how the respondents' answers to the statements/questions posed in the questionnaire, Likerts Summated Ratings (LSR) will be used. For the purposes of data analysis, to find the partial magnitude of influence between Academic Knowledge (X1) on Employee Performance (Y), Expertise (X2) on Employee Performance (Y), Communication (X3) on Employee Performance (Y), as well as simultaneous analysis then multiple linear regression analysis will be used with the following equation: Y = a + b1 X1 + b2 X2 + c3 X3 Information: Y = Employee Performance X1 = Academic knowledge X2 = Skill X3 = Communication a = Constant Parameters; b1, b2, b3 = Estimator Parameters; The final stage is conclusions drawing/verification. At this stage the conclusions or verification obtained previously are still provisional, but if the conclusions or verification described previously are supported by real evidence, then the conclusions described are credible.

RESULTS AND DISCUSSION
Normality Test for X1, X2, X3, and Y To find out whether the variables X1, X2, X3, and Y used in the study are normally distributed or not, we must first know the theory about the basis for decision making for the normality test. The basis for decision making in the normality test is as follows: If the Significance value (Sig.) < 0.05, then the variable is not normally distributed. If the Significance value (Sig.) > 0.05, then the variable is normally distributed.
Based on the SPSS "TestsofNormality" output table above, it is known that the value of Sig. in the Shapiro-Wilk normality test are as follows.
IQ score Sig. is equal to 0.932 GPA Sig. is equal to 0.152 Sig. Motivational Value is equal to 0.066 Because the significance value (Sig.) for all the research variables above is > 0.05, it can be concluded that the variables X1, X2, X3, and Y are normally distributed. Thus, the basic assumptions or requirements in the partial correlation test have been fulfilled.   This SPSS output table provides information about the relationships formed between variables before and after the inclusion of control variables in the correlation analysis. To make sense of the Correlations output table above, there are 3 stages that we must go through, namely: (1) Determine the formulation of the research hypothesis. (2) See the theory about the basis of decision making in the partial correlation test. (3) Interpret the results of the analysis and draw conclusions.

Partial Effect of X1 on Y
The formulation of the research hypothesis in the partial correlation test of knowledge on motivation is as follows: H0: The relationship between Knowledge on Motivation with Skills and Communication as a control variable is not significant. Ha: The relationship between Knowledge on Motivation with Skills and Communication as a significant control variable. *Basic Decision Making in the Sig. Partial Correlation Test. (2-tailed) If the Significance value (2-tailed) > 0.05, then H0 is accepted and Ha is rejected. If the Significance (2-tailed) value is <0.05, then H0 is rejected and Ha is accepted.
The first output table "-none-a" shows the value of the correlation or relationship between the Knowledge and Motivation variables before the inclusion of the control variables (Skills and Communication) in the analysis. From the output above it is known that the correlation coefficient (Correlations) is 0.369 (positive) and the Significance value (2-tailed) is 0.000.. <0.05, it can be concluded that there is a positive and significant relationship between Knowledge and Motivation without any variables control (Skills and Communication). While the Correlations value of 0.369 is included in the moderate relationship category.
Based on the discussion in the partial correlation test above, it is known that the presence of the Skills and Communication variables as control variables will influence the relationship between Knowledge and Motivation variables. Thus it can be concluded that the variable Knowledge is not the only variable that determines motivation, because there are other variables also related to the value of motivation, namely the variable Skills and Communication.
These results are in accordance with the results of research conducted by other researchers, one of which was carried out by (Alfiyah et al., 2021)entitled "The Influence of Knowledge Management and Motivation on Employee Performance". Where the resulting R Square value is 0.333, these results show that knowledge management has a variation of contribution in influencing motivation by 33.30% while the remaining 67% is influenced by other variables outside of data testing.

Partial Effect of X2 on Y
The formulation of the Research Hypothesis in the Partial Correlation Test of Expertise on Motivation is as follows: H0: The relationship between Expertise and Motivation with Knowledge and Communication as a control variable is not significant. Ha: The relationship between Expertise on Motivation with Knowledge and Communication as a significant control variable.
*Basic Decision Making in the Sig. Partial Correlation Test. (2-tailed) If the Significance value (2-tailed) > 0.05, then H0 is accepted and Ha is rejected. If the Significance (2-tailed) value is <0.05, then H0 is rejected and Ha is accepted.
The first output table "-none-a" shows the correlation value or relationship between the Skills and Motivation variables before the inclusion of the control variables (Knowledge and Communication) in the analysis. From the output above it is known that the correlation coefficient (Correlations) is 0.457 (positive) and the Significance value (2-tailed) is 0.001 <0.05, so it can be concluded that there is a positive and significant relationship between expertise and motivation without any control variable ( Knowledge and Communication). While the Correlations value of 0.457 is included in the moderate relationship category.
Based on the discussion in the partial correlation test above, it is known that the presence of Knowledge and Communication variables as control variables will influence the relationship between Skills and Motivation variables. Thus it can be concluded that the variable Expertise is not the only variable that determines motivation, because there are other variables also related to the value of motivation, namely the variables Knowledge and Communication.
These results are in accordance with the results of research conducted by (Parta & Mahayasa, 2021), in his article entitled "The Influence of Work Skills, Team Work, and Motivation on Employee Performance in the Production Section at the Cahaya Silverdi Art Shop Celuk, Gianyar", which explains that work skills or expertise will affect an employee's work motivation, and as a whole will affect Employee performance.

Partial Effect of X3 on Y.
Formulation of the Research Hypothesis in the Partial Correlation Test of Communication on Motivation as follows: H0: The relationship between Communication on Motivation with Knowledge and Expertise as a control variable is not significant. Ha: The relationship between Communication on Motivation with Knowledge and Expertise as a significant control variable.
*Basic Decision Making in the Sig. Partial Correlation Test. (2-tailed) If the Significance value (2-tailed) > 0.05, then H0 is accepted and Ha is rejected. If the Significance (2-tailed) value is <0.05, then H0 is rejected and Ha is accepted.
The first output table "-none-a" shows the correlation value or relationship between the Communication and Motivation variables before the inclusion of the control variable (Knowledge and Skills) in the analysis. From the output above it is known that the correlation coefficient (Correlations) is 0.503 (positive) and the Significance value (2-tailed) is 0.000.. <0.05, it can be concluded that there is a positive and significant relationship between communication and motivation without any variables control (Knowledge and Expertise). While the correlation value of 0.503 is included in the moderate relationship category.
Based on the discussion in the partial correlation test above, it is known that the presence of Knowledge and Expertise variables as control variables will have an influence on the relationship between the Communication variables and Motivation. Thus it can be concluded that the variable Communication is not the only variable that determines motivation, because there are other variables also related to the value of motivation, namely the variable Knowledge and Expertise.
This result is in accordance with what was obtained by (Haryati & Kesumadewi, 2015), in his article entitled "The Influence of Communication on Employee Motivation at Pt Swasti Tunggal Mandiri Medan" explains that the independent variable (x) used, namely communication, has a positive and significant influence on the dependent variable (y), namely motivation, from the results R² = 0.422 where 42.2% of work motivation at PT Swasti Tunggal Mandiri is influenced by communication variables and another 57.8% is influenced by other variables not discussed in the study.
Similar results were also obtained by research conducted by (Prakoso & Putri, 2017), in his article entitled "The Influence of Organizational Communication on Work Motivation of Regional Office Employees of the Ministry of Law and Human Rights of West Java". Where in his research it was explained that good organizational communication will affect work motivation.

Multiple Linear Regression X1, X2, X3, against Y
Before we carry out multiple regression analysis or multiple linear regression to test the research hypothesis, there are several assumptions or requirements that must be met in the regression model. These requirements or assumptions are proven through a series of classic assumption tests including: 1). Normality test, where the assumptions that must be met are normally distributed regression models; 2). Linearity test, where the relationship formed between the independent variable and the partially dependent variable is linear; 3). Multicollinearity test, where a good regression model is where there are no symptoms of multicollinearity; 4). Heteroscedasticity test, in the regression model there are no symptoms of heteroscedasticity; 5). Autocorrelation test (specifically for time series data), the requirement that must be fulfilled is that there is no autocorrelation. In processing this data, I the researcher assumes that the data has met a series of assumptions and requirements above. The "Model Summary" table provides information about the value of the coefficient of determination, namely the contribution or influence of variables X1, X2, and X3 simultaneously (together) on variable Y.
The results of the Summary Model are used to determine the relationship between two or more variables in the regression equation. Here what is seen is the R-Square value. The R-Square value in this result shows a value of 0.263 or 26.3%. This value means that the effect of (X1), (X2), and (X3) on (Y) is 26.3%, the remaining 73.7% is influenced by other variables outside the model. The terms of the relationship between variables are good or not if the R-Square value is above 50% it means good, if it is below 50% it means it is not good. . b1 = -0.012. This means that assuming the value of Skills and Communication is fixed (unchanged), then every increase in Knowledge by 1 Unit will decrease Motivation by 0.012. This result is significant at Alpha 5% of the t test results. b2 = 0.140. This means that assuming Knowledge and Communication have a fixed value (does not change), then every increase in skill by 1 Unit will increase motivation by 0.140. This result is significant at Alpha 5% of the t test results. b3 = 0.338. This means that assuming knowledge and skills are of fixed value (do not change), then every increase in communication by 1 Unit will increase motivation by 0.338. This result is significant at Alpha 5% of the t test results.
The results of this simultaneous influence have actually been described indirectly in a study conducted by (Riyadi, 2017), where the results of the study concluded that there was a direct effect of Communication on Job Satisfaction of 3.6 percent, a direct effect of Work Motivation on Job Satisfaction of 20.3 percent, a direct effect of Competence on Job Satisfaction of 12.9 percent. Simultaneously the effect of the independent variable on Job Satisfaction by 70 percent, the remaining 30 percent is another factor that is not examined but also influences Job Satisfaction, and the effect of Job Satisfaction on Lecturer Performance is 73.5 percent, the remaining 26.5 percent is another factor that not studied but also influences. This implies that there are indeed several other factors that have not been examined in the research that has been conducted.
Similar results have actually been described in research conducted by (Taoreh, 2014). Where the results show a strong relationship and significant influence of motivation, training, leadership, communication, and teamwork on work performance. This means that an increase in motivation, training, leadership, communication, and teamwork will also increase work performance.

CONCLUSION
Variable knowledge is not the only variable that determines motivation, because there are other variables that are also related to the value of motivation, namely the variable skills and communication. Expertise is not the only variable that determines motivation, because there are other variables that are related to motivational values, namely knowledge and communication. Communication variable is not the only variable that determines motivation, because there are other variables also related to motivational values, namely knowledge and expertise variables.