JOURNAL INFORMATIC.
EDUCATION AND MANAGEMENT (JIEM)
Vol 8 No 1 .
: September 2025 - February 2026, pp.
ISSN: 2716-0696.
DOI: 10.
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A 199
The Influence of Service Quality.
Discipline, and Workload on Employe Performance at the Peudada Sub-District Office Khairul Ambia 1*.
Sri Balkis 1.
Hulaimi 1.
Muhammad Nazar 1.
Muhammad Fajri 1 Universitas Islam Kebangsaan Indonesia Article Info Article history:
Received 23 October 2025 Revised 25 October 2025 Accepted 26 October 2025 Keywords:
Service Quality.
Discipline.
Workload.
Performance of Peudada Sub district Office Employees ABSTRACT This study aims to determine the effect of work discipline and work This study aims to determine the simultaneous influence of service quality, discipline and workload on employee performance at the Peudada Sub-district Office.
The research location is at the Peudada Sub-district Office.
The population of this study was 40 civil servants.
The sample of this study was 40 civil servants.
The method used is a quantitative method with a multiple linear regression model, to analyze the influence of the independent variable .
on the dependent variable .
using SPSS.
Based on the results of the study, it is known that the results of the partial test with a value of t count> t table .
,077> 2,.
a significant probability of 0.
025 <0.
indicating that the Service Quality variable affects employee performance at the Peudada Sub-district Office.
The results of the partial test with a value of t count >t table .
,145> 2,.
a significant probability of 0.
045 <0.
05, indicating that the Discipline variable affects employee performance at the Peudada Sub-district Office.
The partial test result of t count < t table (-2,919 < 2,.
significant probability of 0.
026 < 0.
indicates that the Workload variable affects employee performance at the Peudada Sub-district Office.
The partial test result of F count > F table .
,041 > 2,.
with a significance of F of 0.
000 with a probability of < 0.
So it can be concluded that H0 is rejected H3 is accepted.
This means that the variables of Service Quality.
Discipline and Workload together .
have a significant influence on employee performance variables at the Peudada Sub-district Office.
This is an open access article under the CC BY-SA license.
Corresponding Author:
Khairul Ambia | Universitas Islam Kebangsaan Indonesia Email: khairulambia725@gmail.
Introduction Human resources (HR) are a vital component in achieving organizational goals because they possess intelligence, creativity, and the ability to make decisions that technology or capital alone cannot provide (Gomes, 2.
In public organizations, the quality of human resources determines the efficiency and effectiveness of service delivery.
Thus, developing highperforming employees is essential to ensure that government institutions can provide public Journal homepage: http://w.
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services effectively and meet community expectations.
Employee performance reflects the ability and responsibility of individuals in carrying out assigned tasks in accordance with established standards (Mangkunegara, 2.
High employee performance is influenced by various internal and external factors, including service quality, work discipline, and Discipline serves as a behavioral foundation that encourages employees to comply with organizational regulations, while service quality reflects the institutionAos capability to meet public needs effectively.
In addition, workload management plays an important role in determining employee satisfaction and productivity (Hidayat, 2016.
Neksen et al.
, 2.
In recent years, public expectations toward government performance have increased Communities now demand fast, transparent, and responsive public services.
However, in practice, many government offices still face challenges in meeting these At the Peudada Sub District Office, for example, several public complaints have been reported, including lengthy service procedures, unclear administrative requirements, slow document processing, and unresponsive employee behavior.
Such issues reflect the low level of service quality and discipline among employees, which in turn affects public trust in local government institutions.
Another challenge that contributes to performance issues is the imbalance in workload distribution.
Some employees are burdened with excessive responsibilities, leading to fatigue and stress, while others handle relatively lighter tasks that reduce motivation and efficiency.
This uneven workload negatively impacts both individual productivity and overall organizational performance.
To achieve better results, workload allocation should be proportionate to each employeeAos capacity and job description (Neksen et al.
, 2.
Given these conditions, it becomes crucial to enhance service quality, foster strong work discipline, and manage workloads effectively in order to improve employee performance and public service outcomes.
Improving these aspects is expected to increase motivation, accountability, and efficiency among employees.
Therefore, this research was conducted to analyze the influence of service quality, discipline, and workload on the performance of employees at the Peudada Sub District Office.
The results of this study are expected to provide practical recommendations for local government management in optimizing human resources, as well as contributing to the development of performance-based public administration theory.
Research Methodology The method used in this research is a quantitative research method, which is analyzed using statistical techniques.
According to Sihotang in Ferri et al.
, quantitative research is a research approach that focuses on the collection and analysis of numerical data.
This method employs statistical principles and techniques to test hypotheses and identify patterns as well as cause-and-effect relationships that can be generalized.
In quantitative research, the researcher designs a structured study, such as an experiment, survey, or secondary data The quantitative approach aims to produce objective, replicable, and generalizable findings that can represent a wider population.
The results of quantitative research are often presented in the form of numbers, graphs, and both descriptive and inferential statistics.
This Journal homepage: http://w.
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approach is widely used in various fields, including natural sciences, social sciences, and management, due to its ability to identify statistically significant patterns and relationships.
The research was conducted at the Peudada Sub-District Office (Kantor Camat Peudad.
, which serves as the administrative center for local governance and public services in the Peudada sub-district.
Bireuen Regency.
This location was selected because it represents a government institution directly involved in providing public services, making it relevant to the focus of this study on service quality, discipline, workload, and employee performance.
The research was carried out during the month of May 2025, beginning from the date the official research permit was issued until all stages of data collection were completed.
The period of implementation included the distribution of questionnaires, collection of responses, and initial verification of data accuracy to ensure that the research process ran systematically and effectively.
Research Design The research design serves as a framework that guides the overall process of conducting the This research employed a causal design, which aims to examine the cause-and-effect relationships among variables.
Specifically, the study investigates the influence of service quality (X.
, discipline (X.
, and workload (X.
on employee performance (Y).
This design allows the researcher to analyze how variations in the independent variables contribute to changes in the dependent variable, thereby providing a clearer understanding of the factors that affect employee performance.
Research Variables According to Sugiyono .
, a research variable is anything determined by the researcher to be studied in order to obtain information about a particular phenomenon, from which conclusions can then be drawn.
In line with the title of this study, which examines the influence of product quality on customer satisfaction, there are two types of variables involved: independent variables and dependent variables.
Independent Variable According to Sugiyono .
, an independent variable is a variable that influences or causes changes in another variable, known as the dependent variable.
In this study, the independent variables are Service Quality (X.
Discipline (X.
, and Workload (X.
Dependent Variable According to Sugiyono .
, a dependent variable is a variable that is affected or influenced by the independent variable.
The dependent variable in this study is Employee Performance (Y).
Population and Sample Population According to Sugiyono .
, a population is a generalization area consisting of objects or subjects that possess certain qualities and characteristics determined by the researcher to be Journal homepage: http://w.
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studied and from which conclusions are drawn.
The population in this study includes all civil servants (PNS) working at the Peudada Sub District Office, totaling 40 employees.
Sample According to Arikunto .
, a sample is a portion or representative of a population being If only part of the population is examined, the research is referred to as sample This study used a non-probability sampling approach, specifically the saturated sampling method, which means that all members of the population were included as research samples.
Therefore, the sample in this study consists of all 40 civil servants at the Peudada Sub-District Office.
Data Collection Technique The data collection technique used in this study was a questionnaire.
A questionnaire is a method of collecting data by providing a set of written questions or statements to respondents for them to answer (Sugiyono, 2.
The questionnaire was used to obtain respondentsAo opinions regarding the research variables.
Respondents were asked to provide answers by marking one of the available options according to their level of agreement.
Measurement Scale This research used a Likert scale in an ordinal form, where the data represent the respondentsAo responses to statements presented in the questionnaire.
Each statement was designed with several response levels, each assigned a different score or weight.
Higher scores indicate more favorable responses.
The Likert scale measures responses ranging from very positive to very negative, consisting of the following categories:
Table 1.
Measurement Scale Response Category Strongly Disagree Disagree Neutral Agree Strongly Agree Source: Sugiyono, 2016 Scale Value Validity Test A valid measuring instrument is one that can accurately reveal data and provide a precise description of the information being measured.
Precision means that the measurement is able to capture even the smallest differences between one subject and another.
In this study, several statement items were used to accurately represent each variable being measured.
Each item is considered valid if the Pearson Correlation coefficient is greater than 0.
(Sugiyono, 2.
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Reliability Test Reliability refers to a measurement tool used to assess the consistency of a questionnaire that serves as an indicator of a variable.
A questionnaire is considered reliable if a respondentAos answers to the statements remain consistent or stable over time.
The reliability test in this study was conducted using the CronbachAos Alpha coefficient.
A CronbachAos Alpha value greater than 0.
60 indicates that the instrument is reliable, meaning that if the study were repeated under different conditions or at different times, it would yield similar conclusions.
Conversely, a CronbachAos Alpha value lower than 0.
60 suggests that the instrument is less reliable, as it may produce different results under varying conditions.
Furthermore, the closer the CronbachAos Alpha value is to 1, the higher the internal consistency and reliability of the instrument (Ghozali, 2.
Normality Test The normality test aims to determine whether the residual values are normally distributed or A good regression model is one in which the residuals are normally distributed (Marhawati et al.
, 2.
According to Duli .
, the basis for decision-making in the normality test is as follows:
If the significance value is greater than = 0.
05, the data are considered normally If the significance value is less than = 0.
05, the data are considered not normally Heteroscedasticity Test The heteroscedasticity test is an analysis used to determine whether there is an unequal variance of residuals across all observations in a linear regression model.
If the assumption of homoscedasticity is not met .
, heteroscedasticity exist.
, the regression model is considered invalid as a predictor.
This test is conducted to identify any deviations from the classical assumptions required in linear regression, where the model must meet the condition of having no variance inequality among residuals.
In this study, the absolute values of the residuals were analyzed to ensure that no negative values were included.
If the test results show a constant significance value greater than 0.
it indicates that the regression model passes the heteroscedasticity test, meaning that there is no heteroscedasticity and the model is valid for predictive use.
Multicollinearity Test In classical assumption testing, the multicollinearity test is used to determine whether there is a correlation among the independent variables in a regression model.
According to Ghozali .
, this test aims to identify the presence or absence of multicollinearity, which occurs when two or more independent variables are highly correlated with each other.
In a good regression model, there should be no significant correlation among the independent Journal homepage: http://w.
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To detect multicollinearity within the model, the following criteria are applied (Ghozali.
If the tolerance value > 0.
10 and the Variance Inflation Factor (VIF) < 10, it can be concluded that no multicollinearity exists among the independent variables.
If the tolerance value < 0.
10 and the VIF > 10, it indicates that multicollinearity exists among the independent variables in the model.
Data Analysis Method The method used to support this research is a quantitative approach, employing a multiple linear regression model to analyze the influence of independent variables on the dependent This model allows the researcher to determine how variations in service quality, discipline, and workload affect employee performance.
The regression equation can be formulated as follows:
Y = 1X1 2X2 3X3 e Where:
Y = Employee Performance = Constant CA.
CC.
CE = Regression Coefficients XCA = Service Quality XCC = Discipline XCE = Workload e = Error term Correlation Coefficient Analysis The multiple correlation coefficient analysis (R) is used to explain the strength and direction of the relationship between the independent variables and the dependent variable.
According to Sugiyono .
, the correlation coefficient provides an interpretation of how strongly and in what direction the independent variables are related to the dependent variable.
A higher correlation coefficient value indicates a stronger relationship, whereas a lower value indicates a weaker relationship.
The interpretation of the correlation coefficient values can be summarized as follows:
Tabel 2.
Guidelines for Interpreting the Correlation Coefficient Correlation Interval
0,00-0,199
0,20-0,399
0,40-0,599
0,60-0,799
0,80-1,000
Source : (Sugiyono, 2.
Level of Relationship Very Weak Weak Moderate Strong Very Strong Journal homepage: http://w.
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Coefficient of Determination Analysis (RA) The coefficient of determination (RA) analysis is used to determine the extent to which independent variables influence the dependent variable.
A higher RA value indicates a greater influence of the independent variables on the dependent variable.
The result of the determination coefficient can be observed from the R-square value, which explains the percentage of the effect of the independent variables (X) on the dependent variable (Y).
Hypothesis Testing The analytical tool used in this study is SPSS, which assists in processing cross-sectional research data.
To test the influence of the independent variables (X) on the dependent variable (Y), both partially and simultaneously, two statistical tests are conducted: the t-test and the F-test.
Partial Test .
-Tes.
The t-test is a type of statistical test used to determine the extent to which each independent variable individually explains the dependent variable.
The test is conducted with a 95% confidence level and a 5% significance level ( = 0.
, using a degree of freedom .
fCA = n Oe .
This significance level is applied to assess the validity of the hypothesis.
The basis for decision-making in the t-test, according to Ghozali .
, is as follows:
If the t-count > t-table and the significance value < 0.
05, then H1.
H2, and H3 are accepted, meaning that the independent variables Ai service quality, discipline, and workload Ai have a simultaneous and significant effect on the dependent variable, namely the employee performance at the Peudada District Office.
If the t-count < t-table and the significance value > 0.
05, then H1.
H2, and H3 are rejected and H01.
H02, and H03 are accepted, meaning that the independent variables Ai service quality, discipline, and workload do not have a simultaneous and significant effect on the dependent variable, namely the employee performance at the Peudada District Office.
Simultaneous Test (F-Tes.
The F-test is a statistical method used to test a null hypothesis involving more than one It works by determining whether the overall regression equation is significantly suitable compared to the null hypothesis.
The F-test is conducted at a 95% confidence level with a 5% significance level ( = 0.
The degrees of freedom are dfCA = .
Oe .
for the numerator and dfCC = .
Oe .
for the denominator, where k represents the number of parameters .
in the linear regression model, and n is the number of observations.
The F-test is used to assess the simultaneous influence of the independent variables Ai such as competence and organizational climate on the dependent variable .
The basis for decision-making is as follows (Ghozali, 2.
If the F-count > F-table and the significance value < 0.
05, then Ha4 is accepted, meaning that the independent variables service quality, discipline, and workload have a simultaneous and significant effect on the dependent variable, namely the employee performance at the Peudada District Office.
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If the F-count < F-table and the significance value > 0.
05, then Ha4 is rejected and H04 is accepted, meaning that the independent variables service quality, discipline, and workload do not have a simultaneous and significant effect on the dependent variable, namely the employee performance at the Peudada District Office.
Result and Discussion Result This research aims to determine the effect of service quality, work discipline, and workload on the performance of employees at the Peudada Sub-District Office.
The analysis was conducted using a quantitative approach through multiple linear regression with the help of the SPSS statistical software.
Data were collected through questionnaires distributed to all 40 civil servants (PNS), representing the total population of the study.
Validity and Reliability Test The validity test results show that all statement items in the questionnaire have a Pearson Correlation value greater than 0.
30, meaning all items are valid and able to measure the research variables accurately.
The reliability test results, using the CronbachAos Alpha method, also indicate that all variables have values above 0.
60, proving that the questionnaire is reliable and consistent in measuring the intended constructs.
Table 3.
Validity and Reliability Test Variable Service Quality (X.
Discipline (X.
Workload (X.
Employee Performance (Y) Source: SPSS Data Processing .
CronbachAos Alpha Description Reliable Reliable Reliable Reliable Classical Assumption Test The results of the normality test show a significance value greater than 0.
05, which indicates that the residuals are normally distributed.
The multicollinearity test results show that all independent variables have a tolerance value greater than 0.
10 and a VIF value less than 10, which means there is no multicollinearity among variables.
The heteroscedasticity test also shows that the regression model is free from heteroscedasticity because the significance value is above 0.
Thus, the model is declared feasible and valid for further analysis.
Multiple Linear Regression Analysis The results of the multiple regression analysis show that service quality, discipline, and workload positively affect employee performance.
The regression equation is as follows:
Y=5.
325X1 0.
298X2 0.
274X3 e Journal homepage: http://w.
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This equation implies that each variable contributes positively to employee performance.
When service quality, discipline, and workload increase proportionally, employee performance also improves significantly.
Partial Test .
-Tes.
The partial test .
-tes.
was conducted to determine the influence of each independent variable individually on the dependent variable.
Table 4.
Partial Test Variable t-count t-table .
Sig.
Conclusion Service Quality (X.
002 Significant Discipline (X.
005 Significant Workload (X.
013 Significant Source: SPSS Output .
From the table above, all independent variables have t-count > t-table and significance < 05, indicating that each variable service quality, discipline, and workload has a positive and significant influence on employee performance at the Peudada Sub-District Office.
Simultaneous Test (F-Tes.
The F-test results show that the value of F-count = 15.
784 is greater than F-table = 2.
87, with a significance level of 0.
000 < 0.
This indicates that the variables service quality (X.
, discipline (X.
, and workload (X.
simultaneously have a significant effect on employee performance (Y).
Thus, it can be concluded that all three independent variables together contribute substantially to the performance of employees at the Peudada Sub-District Office.
Coefficient of Determination (RA) The RA value = 0.
721, which means that 72.
1% of the variation in employee performance can be explained by the variables service quality, discipline, and workload.
The remaining 9% is influenced by other factors outside this study, such as motivation, leadership, work environment, and compensation.
Table 5.
Coefficient of Determination (RA) Model Summary R RA Adjusted RA Std.
Error Regression Model 0.
Discussion The results of this study confirm that service quality, discipline, and workload play a significant role in shaping employee performance at the Peudada Sub-District Office.
Employees who provide high-quality services demonstrate responsibility and responsiveness to community needs, leading to higher public satisfaction and institutional performance.
Moreover, discipline influences punctuality, adherence to regulations, and accountability, which directly enhance productivity.
Workload that is balanced with an employeeAos capacity Journal homepage: http://w.
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ensures optimal energy use, preventing fatigue and work stress, and supporting consistent In line with previous research Neksen et al.
, 2021, this study reinforces the finding that effective service delivery, good work discipline, and proper workload management contribute to the overall improvement of public sector performance.
Hence, organizational leaders are encouraged to improve work discipline systems, workload distribution, and service delivery training to strengthen employee performance and community trust.
Conclusion This study concludes that service quality, discipline, and workload have a significant and simultaneous influence on employee performance at the Peudada Sub-District Office.
The quantitative analysis using multiple linear regression revealed that all three independent variablesAiservice quality (X.
, discipline (X.
, and workload (X.
Aipositively contribute to the improvement of employee performance (Y).
The results of the partial t-test showed that each variable had a significant effect on performance, while the simultaneous F-test confirmed their collective influence.
The coefficient of determination (RA = 0.
indicates 1% of the variation in employee performance can be explained by these three factors, whereas the remaining 27.
9% is influenced by other elements such as leadership, motivation, and organizational climate.
The findings highlight that enhancing service quality through responsiveness, reliability, and empathy increases public satisfaction and strengthens organizational effectiveness.
Discipline fosters punctuality, responsibility, and compliance with organizational rules, directly impacting work efficiency.
Meanwhile, a well-managed workload promotes balance, prevents fatigue, and sustains long-term productivity.
In practical terms, the results suggest that government institutions, particularly at the subdistrict level, should prioritize developing comprehensive strategies that integrate these three aspects improving service delivery standards, enforcing consistent disciplinary systems, and optimizing workload distribution.
Implementing training programs, clear performance evaluation systems, and transparent workload policies will further enhance employee motivation and accountability.
Finally, this study contributes to public administration literature by reinforcing that employee performance is a multifactorial construct influenced by organizational, behavioral, and operational variables.
Future research is encouraged to include moderating and mediating factors such as leadership style, job satisfaction, and digital service innovation to provide a broader understanding of employee performance determinants in public sector
References