Teunuleh Scientific Journal The International Journal of Social Sciences Vol.
Issues.
March 2025
P-ISSN: 2807-193X | E-ISSN: 2746-4393
DOI: https://doi.
org/teunuleh.
ANALYSIS OF FACTORS INFLUENCING DATA ENTRY PARTNERS'
PRODUCTIVITY AT THE SOUTH ACEH REGENCY CENTRAL
STATISTICS AGENCY OFFICE
Jumaida Ukrawiyah1*.
Leli Putri Ansari2.
Puput Arisna3.
Aglis Andhita Harmawan4.
Afni Abdul Manan5 Universitas Teuku Umar.
Meulaboh.
Aceh Barat.
Indonesia 1maidaukrawiyah@gmail.
com, 2leliputriansari@utu.
id, 3puputarisna@utu.
aglisandhitahatmawan@utu.
id, 5afniabdulmanan@utu.
Abstract Data entry processing partners are contract workers compensated according to their agreements and are in high demand across various sectors, including banking, healthcare, and the Central Statistics Agency of South Aceh Regency.
This study investigates how wages, education, and work experience impact the productivity of these data entry partners at the agency.
The findings reveal that while wages have a positive but insignificant effect on productivity, education significantly influences it.
educational attainment correlates with improved contributions due to enhanced technical and analytical skills.
Conversely, work experience showed a negligible negative impact, potentially linked to factors like burnout or outdated skills.
When considering all three variables together, they collectively affect productivity significantly, with education being the most influential factor.
The regression analysis indicates that wages, education, and work experience account for 44.
9% of the variation in productivity, suggesting that other external factors also play a role.
This research is context-specific to the BPS Office of South Aceh Regency and is limited by its focus on a few variables and a cross-sectional design, which may restrict the generalizability of the results.
Keywords: Wages.
Education.
Work Experience.
Productivity and Data Entry Processing Partner.
AeAeAeAeAeAeAeAeAeAeAeAeAeAe Corresponding author Creative Commons Attribution-ShareAlike 4.
International License Published by Teunuleh Research Center Vol.
Issues.
March 2025
P-ISSN: 2807-193X | E-ISSN: 2746-4393
Jumaida Ukrawiyah.
Leli Putri Ansari.
Puput Arisna.
Aglis Andhita Harmawan.
Afni Abdul Manan Introduction Training and skills development, providing adequate work facilities, offering incentives and rewards, and establishing a pleasant working environment are ways that every agency can and should improve the productivity of its employees.
All departments in the agency, including the South Aceh District Statistics Office, will experience increased output thanks to these measures, which will make the best use of human resources.
(BPS,
A country's economic development and growth is influenced, in part, by the productivity of its workforce.
Economic efficiency and competitiveness are driven by a productive workforce, which allows for increased output with the same inputs.
On the other hand, a slowdown in economic growth can be caused by an inefficient workforce.
In addition, companies also gain additional benefits from high efficiency, such as happier customers, lower operational costs, and better products or services overall.
Therefore, in a highly competitive economic climate, highly productive agencies will have a higher chance of surviving and even thriving.
Partners in data entry processing are often independent contractors whose compensation is based on the terms of a written agreement.
Data entry processing work involves entering, updating and maintaining information into a computer system or data This task can include various types of data, such as numbers, text or other information relevant to the business or organization.
Data entry processing partners are needed in various sectors, such as banking, health, including the Central Bureau of Statistics of South Aceh District.
Based on official information from the Central Bureau of Statistics.
BPS data entry processing partners will receive a monthly salary while working.
The salary of BPS data entry processing partners for 2023-2024 is in the range of Rp.
4,000,000 million until the highest can reach Rp.
10,000,000 million (BPS, 2.
A number of factors related to the HDI affect the efficiency with which data entry processing partners at the Central Bureau of Statistics of South Aceh District perform their work.
HDI is a tool to measure the standard of living, level of education, and health in a region as a whole.
Where one of the factors affecting productivity in data entry processing is the quality of human resources (HR) involved.
HDI reflects the quality of a region's human resources through indicators of health, education, and life expectancy.
The higher the HDI of a region, the more likely the productivity of data entry processing partners at the South Aceh District Statistics Office will increase.
This is evident when considering the partners' educational background and technological expertise, both of which have an impact on their data processing capabilities.
When viewed as a whole, the HDI can provide an overview of living conditions that are closely related to the variables that influence partners' efficiency in processing data input.
Vol.
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March 2025
P-ISSN: 2807-193X | E-ISSN: 2746-4393
Analysis Of Factors Influencing Data Entry Partners' Productivity At The South AcehA The results of observations made by researchers, the problem of labor productivity at the Central Bureau of Statistics of South Aceh Regency is to see how efforts are made by data entry processing partners in increasing productivity so that data provision is achieved properly There are several factors that have an influence on the low or high productivity of data entry processing partners, such as wages, education and work experience factors.
The history of the problem and previous research has attracted the attention of academics, who want to learn more about the relationship between wages, education, and experience of data entry processing partners at the Central Bureau of Statistics office in South Aceh District with their productivity at work.
This study aims to analyze the productivity of data entry processing partners at the Central Bureau of Statistics office in South Aceh District in relation to factors such as salary, education, and work experience.
Theoretical Framework and Hypothesis Development Data Entry Processing Partner Data entry processing partners are statistical partners who are assigned to carry out processing activities of field data collection results from BPS census or survey AuLaw No.
16 of 1997 concerning Statistics and Government Regulation No.
of 1999 concerning the Implementation of StatisticsAy, both require the Central Bureau of Statistics to organize censuses and surveys on a regular basis.
The task of BPS data entry processing partners is to collect data and distribute questionnaires for surveys.
Important information about BPS data entry processing partners is as follows:
Recruitment of BPS data entry processing partners is not a recruitment of civil servants, pK or permanent employees.
BPS data entry processing partners are prioritized for those who do not have ASN status or do not have a permanent job.
BPS data entry processing partners who are already registered in the BPS database and as stated in the BPS Decree have the opportunity to participate in BPS census and survey activities.
BPS data entry processing partners are not involved in other contracts that may prevent them from completing the tasks assigned to them.
Productivity When we get things done, the resources we use will produce a certain amount of output, which is called productivity.
The term AuproductivityAy is defined by Karinsqie .
as the sum of the quality and quantity of work, adjusted for resource costs.
The term Auwork productivityAy is defined by Prayudo et al.
(Anshori & Iswati, 2.
as the ratio of output .
he amount of goods or services produce.
to input .
he total amount of money or other resources consume.
or both.
Vol.
Issues.
March 2025
P-ISSN: 2807-193X | E-ISSN: 2746-4393
Jumaida Ukrawiyah.
Leli Putri Ansari.
Puput Arisna.
Aglis Andhita Harmawan.
Afni Abdul Manan In running a successful company, one of the most important factors is employee Companies and workers alike will reap the rewards of high production, especially in terms of financial security.
A positive outlook on life is reflected in one's work ethic, which in turn is reflected in one's productivity.
Therefore, it is in everyone's interest for businesses and their employees to implement rules that can increase service productivity (Sulaiman, 2.
Wages The money received by an employee from an employer in return for his or her work is known as a worker's wage.
Wages are described as an employee's legal right to receive monetary compensation from the employer for services rendered (Labor Law No.
13 Year 2.
All parties involved, including employers and employees, are bound by the provisions of the applicable agreements, rules and regulations in terms of determining and paying wages.
Wages are a reward for services that have been or will be provided.
Workers earn wages as fair and acceptable compensation for the contributions they make to achieve organizational goals.
Workers' salaries are largely based on the hours they work, the goods they produce, and the services they provide.
Education In an effort to realize personal or collective growth, education is instructing and training individuals or groups in new ways of thinking and acting.
Education, defined as Auany effort aimed at developing attitudes and personality, knowledge and skillsAy (Desanti & Ariusni, 2.
, is the foundation of a nation's growth and determines its rank and When a country invests in its education system, it produces citizens with a strong moral compass, innovative spirit, work ethic and intelligence.
To ensure that their citizens have access to quality education, all industrialized countries have made education policy a top priority.
Education, according to Siagian and Pohan .
, is an ongoing process that uses organized and methodical methods, and it is through this process that management staff acquire generalizable conceptual and theoretical knowledge.
Therefore, according to Hariandja, the education level of employees can improve the competitiveness and performance of the company.
Work Experience What counts as Auwork experienceAy is the amount of time spent performing job An applicant's work history is a key factor in hiring decisions as it can indicate a candidate's character and work ethic (Febianti et al.
, 2.
The amount of time spent doing a job is directly proportional to the amount of experience an individual has in that field (Attaqi, 2.
According to Arischa .
, if workers have sufficient training and experience, they will hone their skills.
A person's work experience is very beneficial for their future employment, as Mulyati .
In many cases, companies prefer to hire Vol.
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Analysis Of Factors Influencing Data Entry Partners' Productivity At The South AcehA someone who is already experienced.
Having relevant work experience makes prospective workers more marketable (Siagian & Pohan, 2.
A worker's level of expertise in his profession is indicated by the amount of time he has spent working in that field.
A worker's tenure is a commonly used metric to measure their level of experience in the field.
Workers with more experience productive than new workers because they have honed their skills over the years (Rampisela & Lumintang, 2.
Framework of Thought The basic concepts, ideas, or hypotheses of a research or study can be better explained by using a framework.
By using a framework, one can organize arguments, describe the interrelationships between variables or components, and plan research The framework of this study looks like in Figure 1.
Figure 1.
Framework of Thought Based on the framework above, it is clear that the productivity of data entry partners is influenced by factors such as salary, education level, and length of work The productivity of data entry processing partners is influenced by, among others, salary, education level, and length of work experience.
The productivity of data entry processing partners is influenced by all three elements simultaneously (Kartini.
Methods The research technique of a study is an organized and methodical process that collects, analyzes, and interprets data to answer research questions and test hypotheses.
The approach, data collection methods, and data analysis strategies are all determined by the research method.
These factors work together to achieve the research objectives.
Research Location Located in Lhok Ketapang.
Kec.
Tapak Tuan.
South Aceh Regency, this research took place at the Office of the Central Bureau of Statistics of South Aceh Regency.
This research was conducted in 2023 and 2024 at the Office of the Central Bureau of Statistics of South Aceh Regency.
Vol.
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March 2025
P-ISSN: 2807-193X | E-ISSN: 2746-4393
Jumaida Ukrawiyah.
Leli Putri Ansari.
Puput Arisna.
Aglis Andhita Harmawan.
Afni Abdul Manan Population and Sample A total of twenty-eight people who work as data entry processors at the Office of the Central Bureau of Statistics of South Aceh District became the population and sample of this study.
Saturated sampling was used as a sample selection strategy because the population was limited.
When the entire population is used as a sample, it is called a saturated sampling approach (Hidayat, 2.
Due to the small population size, the authors used a saturated sampling strategy for the sample.
So that the total number of participants in this study was 28 people.
Data Type and Source Primary data is the basis of this research.
The term Auprimary dataAy refers to information collected from the research subjects themselves.
The data for this study came from a survey sent to members of the data entry processing partners of South Aceh District at the Central Bureau of Statistics.
Data Collection Technique The data for this study were collected through careful observation of an object and questionnaires given to data entry processing partners at the South Aceh District Central Bureau of Statistics Office.
The questionnaire was filled in and recorded by the research Data Analysis Method Multiple linear regression analysis is the data analysis approach used in this study.
The purpose of multiple linear regression, an association analysis method, is to examine the impact of a set of interrelated factors on a single, interval-bound dependent variable (Hartono, 2.
This study uses sequential testing procedures which include validity, reliability, classical assumptions, multiple linear regression, partial .
, simultaneous (F tes.
, and determination tests.
The SPSS application was used to process the data in this study to make it simpler and less prone to human error.
Result and Discussion Validity Test The purpose of the validity test is to determine whether a measurement tool is In this context, the questions in the survey serve as measurement tools.
If the answers to the questions in the survey can provide an overview of the construct being assessed, then the survey can be considered valid.
The validity test of the questionnaire in this study resulted in the following findings, as stated in Table 1:
Vol.
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Analysis Of Factors Influencing Data Entry Partners' Productivity At The South AcehA Table 1.
Validity Test of Questionnaire Because they meet the validity criteria - specifically having a correlation value 3 and a significance value below 0.
05 - then all indicators on all research variables .
Vol.
Issues.
March 2025
P-ISSN: 2807-193X | E-ISSN: 2746-4393
Jumaida Ukrawiyah.
Leli Putri Ansari.
Puput Arisna.
Aglis Andhita Harmawan.
Afni Abdul Manan (X1.
X2.
X3, and Y) are considered valid, so they can be used as research instruments.
This conclusion is drawn from the validity test results which show that all variables are valid because they have a significant correlation to the total value.
Reliability Test According to (Anggraini et al.
, 2.
, the reliability test determines how well the measuring device performs in certain situations.
The extent to which data measurement findings are consistent when repeated with the same measuring device is indicated by The Cronbach Alpha coefficient is the gold standard for evaluating dependability.
When a research instrument is structured using a Likert scale, this reliability metric is the most suitable one to use.
The reliability and consistency of variable measurement can be inferred from a Cronbach Alpha score greater than 0.
Table 2 below displays the results of the research reliability test:
Table 2.
Reliability Test The reliability of the measuring instrument in this study is determined by the results of the questionnaire reliability test.
All variables or indicators in the research questionnaire have a Cronbach's coefficient alpha value greater than 0.
Classical Assumption Test Normality Test Normal probability plots and histograms can be used for visual normality tests.
The histogram, normal probability plot, and histogram were created using SPSS software.
Figure 2 and Figure 3 below illustrate the results.
Vol.
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Analysis Of Factors Influencing Data Entry Partners' Productivity At The South AcehA Figure 2.
Histogram Figure 3.
Normal Probability Plot It can be concluded that the regression model in this study fulfills the assumption of normality based on the histogram and normal probability plot images shown above.
The histogram shape is similar to a bell, and the normal probability plot of the error distribution is still around the straight line (Marina et al.
, 2.
Multicollinearity Test A very high linear relationship in the regression model for each independent variable is known as multicollinearity, which was first presented by Ragner Frisch.
The calculated regression becomes unstable and the variable regression coefficients become too large when multicollinearity occurs, making the regression technique inappropriate.
When the VIF and TOL values are more than 10.
00 and less than 0.
10, we say that the data has considerable collinearity.
(Azizah, 2.
Vol.
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March 2025
P-ISSN: 2807-193X | E-ISSN: 2746-4393
Jumaida Ukrawiyah.
Leli Putri Ansari.
Puput Arisna.
Aglis Andhita Harmawan.
Afni Abdul Manan Table 3.
Multicollinearity Test by Looking at the VIF Value The variables X1.
X2, and X3 provide a VIF value of less than 10.
00 and a Tolerance Value of more than 0.
1, as seen in the VIF value test results in the table above.
This proves that the research data does not show any symptoms of multicollinearity.
Heteroscedasticity Test A scatterplot of the independent variable vs the dependent variable standardized residuals was used to conduct the heteroscedasticity test.
The graph in Figure 4 was created using the following statistical processing findings for heteroscedasticity:
Figure 4.
Scatterplot In this study, the regression equation used is a homoscedasticity equation.
This means that heteroscedasticity does not occur, because the points on the graph above spread erratically and do not follow a certain pattern.
(Sulaeman et al.
, 2.
Autocorrelation Test In order to determine the presence of autocorrelation, this test is carried out through collecting and calculating Durbin-Watson (D-W) statistics on residual data.
Table 4.
Autocorrelation Test with Durbin Watson The dw result is 1.
373, as seen in the table above.
The range of values for DurbinWatson autocorrelation is < 1 to > 3, as described by Refiyana and Vefia .
The regression model does not show any positive or negative autocorrelation because dw 373 is smaller than 3.
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Analysis Of Factors Influencing Data Entry Partners' Productivity At The South AcehA Multiple Linear Regression Analysis Table 5.
Multiple Linear Regression of the Effect of Wages.
Education and Work Experience on the Productivity of Data Entry Processing Partners at the Office of the Central Bureau of Statistics of South Aceh Selatan.
LN PM = LN b1 UP LN b2 PD LN b3 PK e .
LN PM = 1,535 LN 0,184 UP LN 0,448 PD Ae LN 0,75 PK .
Wage Variable (X.
The regression coefficient of 0.
184 for the wage variable (X.
indicates that, if all other things being equal, there will be an increase in productivity of 0.
184 for every one unit increase in wages.
With a significance level of 0.
igher than 0.
and a t-value 559, the statistical test results show that wages have no significant effect on This indicates that an increase in wages does not necessarily correlate directly with an increase in productivity of data entry processing partners at the South Aceh District BPS Office.
This phenomenon can be caused by several factors, such as work motivation that is not solely based on the number of wages, the existence of nonfinancial factors that are more influential, or a compensation system that is considered adequate by the partners, this is in line with the results of Prasetyo's research .
, where higher wages are not always directly proportional to increased productivity.
This could be due to the disconnect between financial incentives and employees' intrinsic For example, if a worker does not feel connected to his or her duties or does not have a strong sense of responsibility towards his or her work, an increase in wages may not be enough to significantly increase productivity.
Education Variable (X.
The regression coefficient of 0.
448 for the education variable (X.
indicates that, all other things being equal, productivity increases by 0.
448 for every one unit increase in schooling.
With a significance level of 0.
ess than 0.
and a calculated t-value of 898, the statistical test findings indicate that education significantly impacts on The results show that the productivity of data entry processing partners increases as their level of education increases.
This could be due to a higher level of education, which is associated with increased analytical ability, technical proficiency and understanding of the job.
A similar study has been conducted by Romadon et al.
, .
Vol.
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P-ISSN: 2807-193X | E-ISSN: 2746-4393
Jumaida Ukrawiyah.
Leli Putri Ansari.
Puput Arisna.
Aglis Andhita Harmawan.
Afni Abdul Manan According to their findings, training and education significantly improve workers' efficiency in the workplace.
Together, these two variables accounted for 73.
1% of the variance in workplace productivity, according to the study's findings.
This highlights the need to provide training and education to employees to improve their performance.
Other studies have also shown the same thing: a person's productivity at work increases as their education level increases.
Why? Because people who have higher education have a better understanding of the importance of productivity and more insight into how to achieve their goals (Febianti et al.
, 2.
Work Experience Variable (X.
With all other factors held constant, the negative regression coefficient of -0.
for the work experience variable (X.
indicates that there is a 0.
75 percent decrease in productivity for every one unit increase in experience.
The significance level of 0.
igher than 0.
and the t-value of -0.
652 indicate that the impact of work experience is not statistically significant.
An interesting phenomenon to examine is the negative coefficient on work experience.
Partners with longer work experience may experience burnout, low motivation, or the absence of opportunities to refresh their skills.
Consistent with this.
Setiawan .
found that although experience does have a negative regression coefficient on productivity, other factors such as fatigue, decreased motivation, and lack of skill renewal are the main reasons why experience does not significantly affect workplace productivity.
This suggests that work experience does not always have a favorable impact on productivity, especially if there is no regular updating of skills and strong motivation.
Hypothesis Testing Partial Test .
-tes.
The t-test is used to ascertain the partial impact of each independent variable .
ducation, work history, and incom.
on the dependent variable .
Testing criteria: The effect is considered significant if Sig.
is less than 0.
05, and insignificant if Sig.
is more than 0.
05 (Aryani, 2.
The Coefficients table shows a Sig.
t value of 0.
(<0.
Constant (Free Variable = .
, and t count of 3.
842 according to Table 5 above.
Therefore, the regression model can be used to forecast productivity.
The independent factors in this study do affect productivity, and they do a good job of explaining variations in productivity.
The salary variable has a t-count value of 1.
559, a significance .
) of 0.
132, and a regression coefficient value of 0.
Therefore, at the 5% significance level, the salary variable is not significantly related to productivity .
132 > 0.
As a result, changes in productivity cannot be well explained by changes in the salary variable.
Vol.
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P-ISSN: 2807-193X | E-ISSN: 2746-4393
Analysis Of Factors Influencing Data Entry Partners' Productivity At The South AcehA Simultaneous Test (F Tes.
Table 6.
Simultaneous Testing Analysis F Test To see how well the model works, you can use the Annova table.
If the dependent variable is affected by all independent factors simultaneously.
Next, we use the F test.
There is a simultaneous influence of variable X on variable Y if the sig value is less 05 or F count is more than F table.
If the sig value is more than 0.
05 or F count is smaller than F table, then variable X has no effect on variable Y simultaneously.
(Aryani, 2.
The results of the Annova table show that salary, education, and work experience together have a significant impact on the productivity of data entry processing partners .
< 0.
Productivity can be accurately predicted using the given regression model.
This indicates that the independent variables of this study can explain the observed variance in productivity changes when viewed as a whole.
Coefficient of Determination (R.
Table 7.
Summary The reliability of the regression line can be approached using the coefficient of The coefficient of determination measures the extent to which the independent variables explain a fraction of the variance in the dependent variable Aminah, 2.
As can be seen in the Adjusted R-Squared value of the coefficient of determination model, 0.
434 indicates that the three variables of education, experience, and income together explain 43.
4% of the variance in productivity.
Variables not included in the research model explain the remaining 57.
6% .
% - 43.
4%).
The corrected value that takes into account the number of independent variables in the model is shown by the Adjusted R Square of 0.
This gives a more careful picture of the explanatory power of the independent variables on productivity.
Vol.
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Jumaida Ukrawiyah.
Leli Putri Ansari.
Puput Arisna.
Aglis Andhita Harmawan.
Afni Abdul Manan Conclusions The research findings relating to the impact of wages on productivity illustrate that the wage variable has a negative but insignificant effect on the productivity of data entry processing partners at the BPS Office in South Aceh District.
This finding is in line with the answers obtained from the research objectives.
This shows that an increase in wages does not always result in more output.
The positive and statistically significant impact of education on production is explained by the effect of education on A person's ability to increase output is directly proportional to his or her level of education.
You can do more with less effort if you have the technical and analytical skills that education provides.
Work experience has a small but noticeable negative impact on productivity, as explained in The Effect of Work Experience on Productivity.
Reasons such as fatigue or failure to refresh skills regularly may be to blame.
The Impact of Salary.
Education Level, and Work Experience Together Productivity is significantly affected by the combination of education, salary, and work experience.
Education is the most influential among the three.
The limitation of the study was the local context at the BPS Office of South Aceh District, with limited variables and cross-sectional research design, so the results may not be widely generalized.
Suggestions in this study are for the BPS Office of South Aceh District, it is recommended to provide continuous education and training development opportunities for partners, consider educational qualifications in the recruitment and placement process of partners and design sustainable capacity building programs.
For data entry processing partners, it is recommended to continue to improve the quality of education and attend relevant training and always update skills and knowledge according to technological developments.
To get a more complete picture, future research should expand its reach, use alternative research methodologies, and include more independent factors that may affect productivity.
Acknowledgments The help and support from many parties has been essential in the completion of this thesis throughout its preparation.
In her role as the first supervisor.
Mrs.
Leli Putri Ansari.
Si has been an invaluable resource for me in completing this thesis.
I am very grateful for all her help and guidance.
Mrs.
Puput Arisna.
has acted as a second supervisor and has been an invaluable resource for me during the process of writing this thesis.
Mr.
Umar and Mrs.
Ismaidar, the author's parents who are very dear to the author, for their unlimited love, who are always there for the author, both financially and morally, and who always pray for the author to succeed in school.
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Analysis Of Factors Influencing Data Entry Partners' Productivity At The South AcehA
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