https://dinastipub.
org/DIJEFA Vol.
No.
4, 2025
DOI: https://doi.
org/10.
38035/dijefa.
https://creativecommons.
org/licenses/by/4.
Analysis of Accounting Knowledge.
Perception, and Business Scale on the Use of Accounting Information Among MSMEs (UMKM) Actors in Cirebon City Akmal AsAoad1.
Muhamad Najmi2.
Rizal Saputra3.
Roni Mulyatno4 Universitas Swadaya Gunung Jati.
Cirebon.
Indonesia, akmalasad05@gmail.
Universitas Swadaya Gunung Jati.
Cirebon.
Indonesia, muhamadnajmicrb@gmail.
Universitas Swadaya Gunung Jati.
Cirebon.
Indonesia, saputrarzl890@gmail.
Universitas Swadaya Gunung Jati.
Cirebon.
Indonesia, ronimulyatno@ugj.
Corresponding Author: akmalasad05@gmail.
Abstract: This study analyzes the effect of accounting knowledge, perceptions, and business scale on the use of accounting information in MSMEs in Cirebon City.
Using quantitative methods with multiple linear regression, data were collected from 97 MSME respondents with annual revenue of at least Rp 50 million.
Data collection was conducted during FebruaryMarch.
The results showed that accounting knowledge has a significant effect on the use of accounting information, while perception and business scale do not have a significant effect However, the overall model is valid, indicating that all variables collectively have an impact on the use of accounting data.
The findings support the importance of improving accounting literacy among MSMEs to improve financial decision-making.
Keywords: Accounting Knowledge.
Perception.
Business Scale.
Accounting Information.
MSMEs in Cirebon.
INTRODUCTION
As one of the developing countries.
Indonesia consistently strives to promote economic growth through various development programs.
Among the key economic actors contributing to this growth is the micro, small, and medium enterprise sector, commonly known in Indonesia as UMKM (Usaha Mikro.
Kecil, dan Menenga.
(Heriston Sianturi and Nurul Fathiyah 2.
MSMEs (Usaha Mikro.
Kecil, dan Menengah / UMKM), as the backbone of the national economy, operate across various sectors such as trade, agriculture, industry, and services.
Their presence has significantly contributed to poverty alleviation and job creation.
Therefore, the government consistently promotes their development as a means of reinforcing the peoplecentered economy.
MSMEs (Usaha Mikro.
Kecil, dan Menengah / UMKM), play a vital role in enhancing the welfare of low-income communities through economic empowerment, equitable access to business opportunities, and contributions to national revenue.
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on UMKM stems from their significant impact on the economy, as well as their role in fostering entrepreneurial independence, innovation, and creativity Ai all of which contribute to inclusive economic growth (Risawati.
Azizah, and Dihardjo 2.
Accounting knowledge, perception, and business scale are the three main factors that may influence the utilization of accounting information by UMKM (Micro.
Small, and Medium Enterprises / MSME.
With sufficient accounting knowledge, economic actors are able to comprehend financial statements and make informed, data-based decisions.
Perceptions of the usefulness of accounting information also determine the extent to which businesses are willing to adopt a more structured financial recording system.
Additionally, the scale of the business plays a crucial roleAithe larger the scale, the more complex the accounting information Cirebon City, as one of the regions with active MSMEs activity, presents an appropriate context for analyzing the impact of these factors.
This study aims to identify and analyze the extent to which accounting knowledge, perception, and business scale affect the use of accounting information by MSMEs stakeholders in Cirebon City.
The findings are expected to provide valuable insights for economic actors, government, and stakeholders in supporting the development of MSMEs in Indonesia.
Theory of Reasoned Action (TRA) The Theory of Reasoned Action is an approach that describes how a person's beliefs, attitudes, desires, and intentions can influence their behavior.
Within this framework, individuals are considered to consciously evaluate the information they possess and then make decisions based on rational considerations of the relationships between that information, whether those relationships are explicit or implicit (Jamil and Hidayat 2.
Analysis of the Influence of Accounting Knowledge on the Utilization of Accounting Information in MSMEs This process aims to generate relevant financial outputs that support the business decision-making process.
Research findings indicate a positive correlation between the level of mastery of accounting principles and the intensity of financial data utilization.
In other words.
MSME/UMKM actors with adequate accounting knowledge are more likely to actively utilize financial reports in their daily operations, thereby enhancing the quality and accuracy of their decision-making (Pranata.
Cita Ayu, and Andayani W 2.
The accounting knowledge of MSME (UMKM) actors is reflected in the way they manage their business financial records.
The accounting practices applied within a business entity indirectly indicate the owner's level of understanding of accounting principles.
One observable indicator is the active participation of MSME owners in various capacity-building activities in accounting, such as workshops or financial literacy training.
Mastery of accounting concepts provides entrepreneurs with greater access to and utilization of essential financial data that serves as a key support for daily business operations.
There is a positive relationship between the financial literacy level of MSME owners and the quality of the accounting information they produce.
The better their ability to present financial information, the more likely it is to enhance the overall performance and sustainability of their MSMEs.
(Amanda and Suwandi 2.
According to research by (Kaligis and Lumempouw 2.
, accounting knowledge enhances the ability of MSME (UMKM) actors in managing their business finances.
The more frequently UMKM players engage with accounting concepts, the more skillful they become in utilizing financial information to support business decision-making.
H1: There is a significant positive relationship between accounting knowledge and the extent of accounting information utilization in MSME decision-making processes.
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The Influence of Perception on the Use of Accounting Information The use of accounting data is the process of processing financial information to help make business decisions, choose the best strategies, and oversee business operations.
The better business owners' understanding of accounting, the more optimally they utilize financial reports.
This is because business owners who are more knowledgeable about accounting usually have a better understanding of their business conditions and how to manage their books correctly (Pranata.
Cita Ayu, and Andayani W 2.
Owners' perceptions can be understood as a process in which individuals process the data they receive to understand the business situation and external factors.
This also includes financial data that serves as an indicator of business performance and progress over a specific period (Heriston Sianturi and Nurul Fathiyah 2.
According to (Darea.
Sumual, and Lambut 2.
, as SME actors become more aware of the importance of accounting, their businesses tend to become more stable and grow.
This understanding also encourages them to actively utilize financial reports, as accounting data helps evaluate business development over time.
H2: The influence of perception has a significant effect on the use of accounting information The Effect of Business Scale on the Use of Accounting Information The use of accounting data involves processing financial information to support business decision-making, selecting the best strategies, and monitoring business operations.
The better the business owners understand accounting, the more effectively they utilize financial reports.
This is because entrepreneurs with stronger accounting knowledge generally have a clearer grasp of their business condition and manage their bookkeeping properly.
(Kustina and Utami According to (Ketut Tanti Kustina 2.
, the level of business development shows a positive correlation with the intensity of accounting data utilization.
This means that MSMEs with larger operational scales tend to be more active in using financial reports to manage their Similarly, research by (Crystshoya Pondawa et al.
found that business scale development is directly proportional to the intensity of accounting information system Companies with larger operations tend to rely more heavily on financial reports for business decision-making.
H3: Business scale has a significant effect on the use of accounting information METHOD This study employs a quantitative method through systematic primary data collection.
Data was gathered using a field survey technique by physically distributing questionnaires to selected MSME actors located in the Cirebon City area.
The quantitative approach focuses on analyzing a selected population or sample with specific characteristics.
The study examines three main variables: .
Accounting Knowledge, .
Perception, and .
Business Scale.
RESULTS AND DISCUSSION
Research Profile This study involved respondents from MSMEs in Cirebon who met the registration requirements of the DKUKMPP government agency (Department of Cooperatives.
Small and Medium Enterprises.
Trade, and Industry/Dinas Koperasi Usaha Kecil dan Menengah Perdagangan dan Perindustria.
of Cirebon City, with a minimum annual income of IDR 50,000,000.
Data were collected through questionnaires, with a total sample size of 97 MSME respondents in Cirebon.
The data were sourced from completed questionnaires filled out by MSME actors in Cirebon City.
The three main variables examined in this study are.
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Accounting Knowledge, .
Perception, and .
Business Scale, particularly in relation to the utilization of accounting data among MSMEs in Cirebon.
Descriptive Statistical Analysis Descriptive analysis is a method used to analyze data aimed at evaluating how far the research findings can be generalized to a larger population based on a single sample.
This descriptive analysis is conducted through hypothesis testing using a descriptive approach.
The results provide information on whether the hypothesis proposed in the study can be broadly This analysis involves one or more independent variables and therefore does not focus on comparisons or relationships between variables.
Based on the descriptive statistical analysis shown in the table below, the minimum and maximum values for accounting knowledge (X.
are 5 and 25, respectively.
The average score for accounting knowledge is 16.
5155, with a standard deviation of 4.
Table 1.
Descriptive Statistical Analysis Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025.
Data Quality Validity Test The validity test is a step to assess the extent to which a measurement instrument can be trusted.
In this context, the instrument refers to the components within the questionnaire.
A questionnaire is considered valid if its questions accurately reflect the concepts intended to be measured(Janna and Herianto 2.
From the data examination, it was found that for the accounting knowledge variable (X.
, each statement item had a Sig.
-taile.
value below 0.
Similarly, for the perception variable (X.
, all question items showed a Sig.
-taile.
value less than 0.
The same applies to the business scale variable (X.
, where each question item had a Sig.
-taile.
value below 0.
Meanwhile, for the use of accounting information variable (Y), the statistical test results indicated that all question items had a two-tailed significance value (Sig.
2-taile.
Thus, it can be confirmed that all statements in this research instrument meet the validity criteria Reliability Test Reliability is a parameter that indicates the extent to which a measuring instrument can be trusted or relied upon.
Therefore, a reliability test is used to assess how consistently the instrument performs when used repeatedly.
A measurement instrument is considered reliable if it consistently produces the same results across multiple measurements.
(Janna and Herianto 2.
Based on the results in the table above, the CronbachAos Alpha values for all question items under the Accounting Knowledge variable (X.
exceed the minimum threshold of 2, indicating that this instrument can be categorized as reliable.
The same applies to the Perception variable (X.
, where all items also show CronbachAos Alpha values above the specified threshold.
Likewise, the Business Scale (X.
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(Y) variables each show CronbachAos Alpha values greater than 0.
Therefore, it can be concluded that all questionnaire instruments used in this study meet the reliability criteria and are suitable for data collection from respondents.
The normality test results using the One-Sample KolmogorovAeSmirnov method show a significance value (Sig.
2-taile.
Since this value is above the 0.
05 threshold, the data distribution in this study can be considered normal.
Table 2.
One-Sample Kolmogorov-Smirnov Test Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025 Classical Assumption Test Normality Test One of the fundamental methods for testing normality is by visualizing the frequency distribution of the data.
This approach examines how well the observed data aligns with the expected distribution pattern.
If the sample size is sufficiently large but the distribution deviates significantly from a normal curve, the interpretation of the results may be misleading.
Therefore, over time, researchers have developed various statistical techniques to more accurately assess whether the data follows a normal distribution.
(Usmadi 2.
Table 3.
Normality Test (P-P Plot Of Regression Standardized Residua.
Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025.
The P-P plot visualization shows that the residuals closely follow the diagonal line, indicating a normally distributed residual pattern.
This is further supported by the Kolmogorov- 2977 | P a g e https://dinastipub.
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Smirnov test, which yields a significance value of 0.
reater than = 0.
, thereby satisfying the normality assumption required for regression analysis.
Table 4.
Normality Test (Grafik Histogra.
Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025 The histogram of regression residuals shows a distribution that closely approximates a normal distribution, forming a bell-shaped curve.
The mean value of 5.
16E-16, which is nearly zero, and the standard deviation of 0.
984 indicate that the residuals are symmetrically distributed around zero.
These findings suggest that the normality assumption in the regression model has been satisfied.
Multicollinearity Test To assess whether there is a strong linear relationship among the independent variables, a multicollinearity test was carried out using the Variance Inflation Factor (VIF) and Tolerance A model is considered free from multicollinearity if the VIF value is less than 10 and the Tolerance value is greater than 0.
Based on the test results, all independent variables met these criteria, indicating that there are no multicollinearity issues in the regression model.
Table 5.
Multicollinearity Test Results Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025.
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Based on the table shown, it can be seen that for the TOTALX1 variable, the Variance Inflation Factor (VIF) value obtained is 2.
For the TOTALX2 variable, the VIF is 1.
while for the TOTALX3 variable, the VIF is 2.
Each variable shows a VIF value below 10, so it can be concluded that this regression model is free from significant multicollinearity tolerance indicators can also be used as a multicollinearity detection tool.
The analysis results show the tolerance value as follows:
Variable TOTALX1: 0.
Variable TOTALX2: 0.
Variable TOTALX3: 0.
Since all tolerance values are above the threshold of 0.
1, this regression model can be said to be free from multicollinearity problems Heteroscedasticity Test The heteroscedasticity test carried out aims to identify whether in the regression model there is consistency in the variance of the residual or error between each observation with one (Uthami, 2.
Table 6.
Heteroscedasticity Test Results Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025.
The consequence of the presence of heteroscedasticity in a regression model is that the resulting estimator becomes inefficient.
If heteroscedasticity is present, it is necessary to correct it so that the resulting equation can be accurate.
There are two methods to determine the presence of homoscedasticity, namely by using graphs or conducting statistical tests.
detecting heteroscedasticity, it must be adjusted to the type of statistical test used in repeated (Andriani 2.
Based on the heteroscedasticity test results, only TOTALX1 has a significant effect on TOTALY with a significance value of 0.
While TOTALX2 and TOTALX3 have no significant effect, respectively with a significance value of 0.
867 and 0.
Multiple Linear Regression Test Multiple linear regression analysis is used to determine how much influence the independent variables, namely Accounting Knowledge (X.
Perception (X.
, and Business Scale (X.
, have on the dependent variable, namely the Use of Accounting Information (Y).
The results of the calculation of the regression coefficient show that X1 is worth 0.
023, and X3 reaches 0.
058, with a constant value of 6.
Thus, the regression equation formed can be written as follows:
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Y = 6,627 0,501 X1 0,023 X2 0,058 X3 e Table.
7 Multiple Linear Regression Test Results Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025.
The constant value of 6.
627 indicates the baseline use of accounting information when all independent variables are zero.
In other words, without the influence of accounting knowledge, perception, or business scale, the level of accounting information use will be at this figure.
The accounting knowledge variable (X.
shows a positive coefficient of 0.
This reveals that every one-unit increase in accounting understanding will be followed by an increase 501 units in the use of accounting information, assuming other variables remain The perception variable (X.
has a relatively small effect with a coefficient of 0.
This figure implies that the contribution of perception to the increase in the use of accounting information is minimal, where a 1-unit increase in perception only results in a 0.
023-unit increase in the dependent variable.
The regression coefficient for variable X3 (Business Scal.
with a value of 0.
058 indicates a positive relationship, although not very significant, with the dependent variable.
This means that if there is a 1-unit increase in the Business Scale variable, the dependent variable will increase by 0.
In the context of SMEs in Cirebon City, the regression model findings indicate that the higher the level of accounting understanding, positive perception of financial information, and business scale, the greater the tendency to utilize accounting Hypothesis Testing Partial Test .
-tes.
This test aims to evaluate the extent to which each independent variable influences the dependent variable individually.
In this analysis, a significance level of 10% ( = 0.
is used.
The t-table value is determined based on a two-tailed test with degrees of freedom .
- 3 = .
, resulting in a t-table value of 1.
Accounting Knowledge (X.
shows a calculated t-value of 4.
916, which is far above the t-table value .
Additionally, the significance level of 0.
000 indicates that the effect is highly significant.
Therefore, the first hypothesis (H.
is accepted, meaning that variable X1 makes a significant contribution to the use of accounting information (Y).
Perception (X.
has a t-value of 0.
167, which is below the t-table threshold.
Its significance level is recorded at 0.
867, exceeding the set significance level.
Therefore, this variable does not have a significant influence on the dependent variable, so the second hypothesis (H.
is rejected.
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Business Scale (X.
yields a t-value of 0.
516, also below the t-table value.
Its significance level reaches 0.
607, indicating no significant influence on the use of accounting information.
Therefore, the third hypothesis (H.
is rejected.
Table 8.
Partial Test Results Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025.
Simultaneous Test .
The F test is performed to check whether all independent variables simultaneously have a significant effect on the dependent variable in the regression model.
In other words, this test is performed to evaluate whether the multiple regression model constructed is suitable for explaining the relationship between variables (Simamora & Janrosl, 2.
To obtain the F table value, two types of degrees of freedom are required:
or the numerato.
is calculated using the formula .
umber of independent variables - .
, which is .
- .
= 2 df2 .
or the denominato.
is calculated using the formula .
umber of samples - number of variable.
, i.
, .
- .
= 94
The significance level used in this study is 10% ( = 0.
Based on these calculations, the F table value obtained is 3.
The decision-making steps in the F test are as follows:
If the F significance value is less than 0.
1 Ie the model is considered significant, meaning that all independent variables simultaneously influence the dependent If the F significance value is greater than 0.
1, then the model is not significant, meaning that the independent variables collectively do not influence the dependent variable.
From the regression analysis results, it is known that the calculated F value is 21.
with a significance value of 0.
Since the calculated F value far exceeds the F table value .
087 > 3.
and the significance value is also far below the limit .
000 < 0.
, it can be concluded that the regression model as a whole is significant.
Thus, the first hypothesis (H.
can be accepted, indicating that the variables of accounting knowledge, perception, and business scale collectively influence the use of accounting information by SME actors in Cirebon City.
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Table 9.
Simultaneous Test Results Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025.
Test the coefficient of determination (R.
The coefficient of determination is used to determine how much influence the independent variable has on changes in the dependent variable in a model.
If the independent variable has no influence at all, the RA value will be zero.
Conversely, if the independent variable can explain all changes in the dependent variable, the RA value will reach its maximum value, indicating the full influence of the independent variable.
(Simamora dan Janrosl, 2.
Based on the analysis results, the coefficient of determination (RA) in this study was recorded at 0.
405, while the correlation coefficient (R) was 0.
These figures indicate that 5% of the variation in the utilization of accounting information by SME actors can be explained by the three independent variables used, namely accounting knowledge, perception, and business scale.
Meanwhile, the remaining 59.
5% comes from the contribution of other factors outside this research model that have not been analyzed.
This means that although this model is quite capable of explaining most of the influence on the dependent variable, there is still considerable room for exploration of other external factors.
Table 10.
Test the coefficient of determination Results Source: Primary data processed using IBM SPSS Statistics version 25, accompanied by validity and reliability tests conducted in 2025.
Discussion The influence of accounting knowledge on the use of accounting information among MSME actors in the city of Cirebon Statistical analysis reveals a significant impact of accounting competence on the use of financial data in MSMEs.
This finding is reinforced by:
The t-statistic value .
exceeds the critical value .
The significance level of 0.
000 is well below the threshold of 0.
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Based on this empirical evidence, the first hypothesis (H.
regarding the positive influence of accounting understanding on the utility of financial information is proven valid This study also explains that the accounting learning process can improve the understanding of small business owners.
As accounting knowledge increases, the utilization of accounting information by SME owners in Cirebon City also increases.
Previous studies, such as those conducted by Hudha .
, indicate that accounting knowledge does not have a significant effect.
However, other studies conclude that accounting skills can contribute to increased utilization of financial data by small business owners.
(Pranata.
Cita Ayu, dan Andayani W 2.
The Influence of Perception on the Use of Accounting Information among MSME Actors in Cirebon City Statistical analysis indicates that:
The t-statistic value of the Perception variable (XCC) only reaches 0.
167, which is significantly lower than the critical value of 1.
The significance level of 0.
867 exceeds the =0.
1 limit.
The implication is that the perception of SME actors toward accounting does not contribute significantly to explaining the variation in the use of financial information.
This finding leads to the rejection of the second hypothesis (HCC).
The results of studies showing that perception has an impact on the use of accounting information are (Kaligis and Lumempouw 2.
Meanwhile, the results of studies indicating that perception does not impact the use of accounting information are (Heriston Sianturi dan Nurul Fathiyah 2.
The Influence of Business Scale on the Use of Accounting Information among MSME Actors in Cirebon City The statistical test results show that:
Business Scale (XCE) has a t-statistic value of 0.
516, far below the critical value of 1.
The significance level of 0.
607 exceeds the =0.
1 limit.
Based on these findings, it can be concluded that:
There is no significant influence between business scale and the use of accounting information in MSMEs The third hypothesis (HCE) is rejected because it does not meet the statistical Furthermore, these results indicate that the size of a business does not necessarily have an impact on the use of accounting information, especially if it is not accompanied by an adequate level of understanding and perception from the business actors.
However, academic views remain divided.
A study by Mubarokah and Srimindarti .
, for example, states that business scale can influence the use of accounting information.
However, there are also other studies that find that this variable does not show a consistent effect on this aspect (Umami.
Kaukab, dan Romandhon2.
The Influence of Accounting Knowledge.
Perception, and Business Scale on the Use of Accounting Information among MSMEs Actors in Cirebon City Based on the ANOVA test results, it is known that the F value is 21.
087 with a significance level of 0.
This value exceeds the F-table threshold of 3.
09 and indicates that the significance level is well below 0.
This means that, overall, this regression model is valid and can explain the significant relationship between the independent variablesAiaccounting 2983 | P a g e https://dinastipub.
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knowledge, perception, and business scaleAiand the level of accounting information Therefore, the general hypothesis is accepted, while the second and third hypotheses are not partially proven.
From these findings, it can be concluded that improving understanding in the field of accounting has an important contribution in encouraging SMEs to optimize accounting information in managing their businesses.
In other words, as their knowledge increases, their ability to absorb and use accounting data also becomes more effective and impacts more structured business decision-making CONCLUSION Based on the results of data evaluation and hypothesis testing regarding the use of accounting information among MSME actors in Cirebon City, it can be concluded that accounting knowledge has a significant positive influence on the use of accounting This means that MSME actors with higher levels of accounting knowledge are more capable of utilizing financial data effectively to support their business operations and decision-making processes.
In contrast, the perception of MSME actors toward accounting information did not show a significant influence, indicating that even a positive perception alone is not sufficient to encourage the use of accounting practices without adequate accounting knowledge.
Similarly, business scale also did not have a statistically significant impact, suggesting that the size of the business is not the main determinant in adopting accounting information systems.
However, when considered collectively, the variables of accounting knowledge, perception, and business scale significantly influence the use of accounting information.
This highlights the importance of strengthening accounting competencies among MSME actors while also considering their perceptions and business contexts.
These findings offer practical insight for policymakers and stakeholders to develop targeted financial literacy programs that focus not only on technical skills but also on fostering awareness of the benefits of accounting.
Promoting a financially literate and data-driven business environment among MSMEs is essential for improving accountability, transparency, and long-term sustainability.
REFERENCES