Analysis of the Level of Education and Training on the Work Productivity of the Community Making Ulos Fabric in the Huta Raja Ulos Weaving Village

Kampung Tenun Ulos Huta Raja developed into a village that contains cultural values, especially for the culture of the Batak people, and was rearranged by completing various infrastructures and several facilities such as clean water and sanitation, attractive landscapes, and providing training to local residents so that at the same time they This study uses quantitative research. Based on the results of data processing, the first hypothesis is rejected, which means there is a negative and significant effect of the level of education (X1) on work productivity in Ulos village. Then, based on the hypothesis testing criteria, the second hypothesis is partially accepted, which means there is a positive and significant effect of community training (X2) on work productivity in Ulos village.

Kampung Tenun Ulos Huta Raja developed into a village that contains cultural values, especially for the culture of the Batak people, and was rearranged by completing various infrastructures and several facilities such as clean water and sanitation, attractive landscapes, and providing training to local residents so that at the same time they This study uses quantitative research. Based on the results of data processing, the first hypothesis is rejected, which means there is a negative and significant effect of the level of education (X1) on work productivity in Ulos village. Then, based on the hypothesis testing criteria, the second hypothesis is partially accepted, which means there is a positive and significant effect of community training (X2) on work productivity in Ulos village.

INTRODUCTION
ulos is very much needed in training which aims to educate participants who are guided by instructors. Furthermore, in conducting the training, there are methods carried out by the organizers by analyzing, designing, developing, and implementing all forms of business in making ulos cloth training. Human resources have the potential and functions which are very important for a company or organization. Even though human resources are one of the important elements, the quality of human resources in carrying out their work activities still lacks the level of education, training, and work productivity, especially in Kampung Ulos Huta Raja.

THEORETICAL REVIEW
Ulos is everyday clothing. When worn by men, the upper part is called hande-hande, the lower part is called singkot, as a head cover it is called talitali, bulang-bulang or detar. When worn by women (women), the lower part is called haen, worn up to the limit of the chest. To cover the back it is called hobahoba and is worn in the form of a shawl called ampe-ampe, to cover the head it is called saong. When a woman carries a child, the ulos used is called a parompa and the back cover is called hohop-hohop.
In many old records it is written that traditional Indonesian trains have high cultural value, especially from an aesthetic point of view, have symbolic meaning and have a philosophy that underlies their manufacture. If we examine the history of Indonesian woven fabrics, especially the technique of woven woven maim, we can see that what happened in Indonesia is interesting because it turns out that this technique has been known since Prehistoric times. In the remote areas of Sumatra, for a long time, people have known complex weaving patterns, all of which were produced by making their own looms, looking for trees for fiber and dyeing them with natural dyes taken from the forests around them where they live. It is estimated that this skill was possessed by people who lived during the bronze or bronze period from the 8th to the 2nd century BC.
The diversity and uniqueness of the decorative woven fabrics is clearly reflected in the elements associated with ancestor worship and the greatness of nature. Each region has its own characteristics in its decoration which is related to the socio-cultural function of that area. In every family or religious ritual activity, a piece of woven cloth is almost always a very important part.
Historically, the initial phase of the development of this weaving business was one of the side activities carried out by housewives and girls to meet the needs of clothing materials or as an attribute of traditional ceremonies. In oral stories such as those found in turi-turian, it is often described that the ability to weave is one of the things that is highly demanded from a girl. So that during the royal period, the lower part of the traditional house was often used for weaving. In the past, this weaving activity did not lead to economic problems, moreover those who were good at weaving were the children of kings and aristocrats. So, weaving at that time became a prestige or social status for a girl, that she came from the royal and noble lineage so that she would get special treatment from her environment and society.
Ulos (sheets of traditional Batak woven cloth) are essentially the result of the civilization of the Batak people at a certain time. According to the records of several experts, ulos (read: textiles) was already known to the Batak people in the 14th century in line with the entry of hand looms from India. This can be interpreted that before the entry of looms into Batak Land, the Batak people were not familiar with ulos (textile). This means that there is no culture of giving and receiving ulos (mangulosi).
Some sources also say that the Sumatran region knows weaving since it has connections with India, China and Arabia. It was from them that weaving technology began to be obtained. While the development of the motifs that are owned are motifs that are the result of the original creativity of the Toba Batak tribe which are influenced by natural elements and human life.

METHODOLOGY
This research was conducted in the ulos weaving community, located in Huta Raja, Lumban Suhi-Suhi Toruan Village, Jl. Simanindo, Pangrururan District, Samosir Regency. This research began in early January 2023 online and offline. The sampling process in this study aims to obtain the necessary data, so that relevant conclusions can be drawn from the data collected. According to Suharsimi Arikunto (2010: 108), the population is the entire research subject. In this study, the population was the ulos weavers of Huta Raja, Lumban Suhisuhi Village, Samosir Regency, totaling 60 people. According to Suharsimi Arikunto (2010: 109), the sample is part or representative of the population to be studied. Sampling for research according to Suharsimi Arikunto (2010: 112), if the subject is less than 100 people, all of them should be taken, if the subject is large or more than 100 people, 10-15% or 20% -25% can be taken. Therefore the sample in this study were 60 ulos weavers Huta Raja, Lumban Suhi-suhi Village, Samosir Regency.

Data Processing Techniques Validity Test
The validity test in this study used item analysis, namely correlating the score of each item with the total score which is the sum of each item's score. If there are items that do not meet the requirements, then these items will not be examined further. These conditions according to Sugiyono (2019:179): a. If r ≥ 0.30, then the statement items from the questionnaire are valid. b. If r ≤ 0.30, then the statement items from the questionnaire are invalid.

Reliability Test
According to Sugiyono (2019: 64) reliability testing with internal consistency is carried out by trying the instrument only once, then the data obtained is analyzed using certain techniques. Testing the reliability of the instrument with a split technique from Spearmen Brown. Calculation of reliability in this study using the analysis developed by Alpha Cronbach. Test criteria as follows: 1. α ≥ 0.5 means a reliable instrument 2. α < 0.5 means the instrument is not reliable

Classic Assumption Test
According to Ghozali (2017: 33) if the classical assumptions are met, then the regression estimate with the ordinary least squares (OLS) will be BLUE (Best Linear Unbiased Estimator), meaning that decision making through the F Test and T Test should not be ordinary.

Normality Test
The normality test is used to evaluate whether the independent variable, the dependent variable, or both in a regression model have a normal distribution or not. If a variable is not normally distributed, the statistical test results can be affected. One method that can be used to test data normality is the One Sample Kolmogorov-Smirnov test. This test has the following conditions: 1. If the significance value is > 0.05, the data is normally distributed.
2. If the significance value is <0.05, the data is not normally distributed

Multicollinearity Test
Multicollinearity test aims to test whether the regression found a correlation between independent variables. A good regression model should not have correlation between independent variables (Ghozali, 2009:60). An analysis is said not VIF (Variance Inflation Factor) < 10 (Ghozali, 2009:60).

Heteroscedasticity Test
Heteroscedasticity test is used to test whether there is uneven variation of the residuals between one observation and another in a regression model. If there are different variations, this is called Heteroscedasticity. One method for detecting the presence of heteroscedasticity in multiple linear regression models is through a scatterplot or through a comparison between the predicted value of the dependent variable (SRESID) and the residual error (ZPRED).

Hypothesis Testing Multiple Linear Regression
Multiple linear regression analysis is a method used to predict or describe how changes in the dependent variable (the variable you want to predict) occur when the values of two or more independent variables (predictor factors) are manipulated or changed. This method helps in understanding the relationship between the dependent variable and the independent variable by taking into account the effect of each of the independent variables together. By performing this analysis, we can gain information about how each independent variable contributes to the variation in the dependent variable, as well as the strength and direction of the relationship between these variables. Y= a +b1X1 + b2X2 + e

Coefficient of Determination (R2)
Basically, the coefficient of determination (R²) measures the extent to which the model is able to explain the variations in the dependent variable.

Simultaneous Test (Test F)
The F statistical test is basically used to determine whether all the independent variables included in the model have a jointly significant effect on the dependent variable. The criteria for evaluating the hypothesis in this F test are: 1. H₀ Accepted if: F count ≤ F table at α = 5%. 2. Ha Accepted if: F count > F table at α = 5%.

Partial Hypothesis Testing (t test)
According to Ghozali (2013: 98), the t statistical test basically indicates the extent to which one independent variable influences the dependent variable, assuming that other variables remain constant. The criteria for evaluating the hypothesis in this t test are: 1. H₀ Accepted if: -t

Descriptive Statistical Analysis
This research was conducted by the author in Kampung Ulos village with the independent variable, namely the level of community education and training, and the dependent variable, namely work productivity. Descriptive statistics relate to how data can be described or described or can also be concluded. Table 1 shows that the measurement ratio of the Education Level variable (X1) has a mean value of 5.51, while the standard deviation is 1.480. In the Community Training variable (X2) with a mean value of 16.98, the standard deviation is 4.368. Then, on the work productivity variable (Y) with an average value of 17.31, the standard deviation is 3.610.

Characteristics of Respondents
This study involved 65 respondents who met predetermined criteria. Respondents' identities were analyzed based on several key pieces of information including name, gender, last education, and age. The collection of identity information is carried out through online questionnaires that have been East Asian Journal of Multidisciplinary Research (EAJMR) Vol. 2, No. 6, 2023: 2321-2338 2327 distributed to respondents. The following is a detailed result of identifying the identity of the respondent.

Characteristics of Respondents Based on Gender
The first characteristic of the respondent to be calculated is the gender of the respondent. Respondent's data related to gender is shown in Table III The data in the table indicates that the number of female respondents is greater than that of male respondents. The results of the analysis related to the gender of the respondents can also be easily seen through the diagram presented below.

Figure 1. Respondent's Gender Chart
The data obtained based on the pie chart above shows that of the 65 respondents who were analyzed, all of the respondents were women. So it can be concluded that the business owners in Ulos Village are all female.

Characteristics of Respondents by Age
The first characteristic of the respondent to be calculated is the age of the respondent. Age-related respondent data is shown in Table 3. Based on the table above, the results of the analysis show that the number of respondents aged >45 years is more dominant than those under their age. The results of the analysis related to the age of the respondents can also be seen more clearly in the diagram below.

Figure 2. Respondents Age Chart
The data obtained based on the diagram above shows that of the 65 respondents analyzed, there were 20 people aged 17-24 years or the equivalent of 32%, 14 people aged 25-35 years or the equivalent of 10%, 7 people aged 36-45 years or the equivalent of 10%, and 24 people aged <45 years or the equivalent of 37%.

Characteristics of Respondents Based on Last Education
The next characteristic of the respondent that is calculated is the respondent's last education. Respondent data related to recent education is shown in Table 4. Based on the table above, the results of the analysis show that the number of respondents with high school graduates is more dominant than respondents with non-high school graduates. The results of the analysis related to the respondent's last education can also be seen more clearly in the diagram below. The data obtained based on the diagram above shows that of the 65 respondents analyzed, of them there were 3 elementary school graduates or the equivalent of 4%, 2 junior high school graduates or the equivalent of 3%, 44 high school graduates or the equivalent of 67%, 16 people undergraduate graduate or equivalent to 24%. So it can be concluded that the dominant business owners in Ulos village are high school graduates.

Data Validity Test Validity test
The total research variables contain 44 statements that must be answered by respondents. Respondents used in the data validity test were 40 teachers at SMP Negeri 24 Medan (outside the research sample). In this study, the criteria used to determine whether a statement is valid or not are as follows: 100% confidence level (α = 10%), degrees of freedom (df) of n -2 = 44 -2 = 42, and the value of r is obtained table of 0.2907. If the calculated r value is greater than the r table value and r is positive, then the statement is considered valid. Based on the analysis that has been carried out, the results of validity testing can be seen in the following table: From the analysis carried out in Table 5, it can be seen that the calculated r value for each tested indicator is positive and is greater than the specified r table value. Thus, it can be concluded that all the indicators used in this study were declared valid after going through a validity test.

Reliability Test
The results of the reliability test are in table 6 below: From the reliability that has been carried out on the output above, it can be seen that the alpha coefficient of each variable describes different results. However, all items from the independent or dependent variable statement items have a coefficient value of > 0.70, which means that it can be concluded that the instrument used is reliable.

Classic assumption test Normality test
The normality test aims to test whether the residual distribution in the regression model is normal or not. A model that is considered good is a model that produces a residual sig value that is greater than the alpha value. The normality test that is applied is the Kormogorov Smirnov in table 7 below.

Source: data processed by researchers in 2023
From the results of the analysis using SPSS, it can be concluded that all variables in the data show sig > α (0.05) based on the asymp column. Sig. (2tailed) which has a value of 0.051. Therefore, it can be concluded that the sample data has a normal distribution.

Multicollinearity Test
The purpose of this multicollinearity test is to test whether there is a correlation between variables in this study. Good research is the reference model used that has no correlation with each other. In detecting whether this is present, it is observed through the tolerance value and the Variance Inflation Factor (VIF). If the tolerance value is > 0.1 and VIF < 10, it can be concluded that there is no multicollinearity between the independent variables in the regression model. From the output above, it appears that each variable has a tolerance value of > 0.1 and VIF < 10. So the conclusion is that there is no multicollinearity between the independent variables in this regression model.

Heteroscedasticity Test
The heteroscedasticity test was used to evaluate whether there is a difference in the residual variation across the observations of the regression model, which is a violation of the classical assumptions. If the sig value > 0.05, it can be concluded that there is no heteroscedasticity in this study. The Glejser test method was used to test heteroscedasticity, and the results can be found in Table 9 below. To identify heteroscedasticity, you can use a scatter plot between ZPRED and SRESID. If the points on the scatter plot do not form a special pattern and are scattered above and below zero on the Y axis, it can be concluded that there is no heteroscedasticity in the regression model. The following is the scatter plot generated by the regression model:

Figure 4. Heteroscedasticity Test Results
East Asian Journal of Multidisciplinary Research (EAJMR) Vol. 2, No. 6, 2023: 2321-23382333 The graph above shows that the points on the scatter plot do not show a special pattern, and are scattered above and below zero on the Y axis. Therefore, it can be concluded that there is no heteroscedasticity in the regression model.

Multiple Linear Regression Analysis
Multiple linear regression analysis in this study is used to identify the relationship between two or more independent variables with one dependent variable. This analysis model is used to evaluate the effect of the level of community education and training on work productivity. Multiple linear regression results have been calculated using SPSS 24.0 software, and the following are the results: From the results of data processing in the table above, the multiple linear regression equation model in this study is as follows: Y = a + b1X1 + b2X2 + e Y = 7,250 + 0.247 X1 + 0.512 X2 + ….e A constant value of 7,250 states that if the community's level of education and training is assumed to be zero then work productivity is 7,250. The regression coefficient for education level (b1) is 0.247 which states that every 1% addition or increase in education level will increase work productivity by 0.247. On the other hand, if each value of the education level decreases by 1% then work productivity will decrease by 0.247 assuming that the other independent variables are of a fixed value. Furthermore, the regression coefficient of community training (b2) of 0.512 states that every 1% addition or increase in community training will increase work productivity by 0.512.
On the other hand, if the value of community training decreases by 1%, work productivity will decrease by 0.401 assuming that the other independent variables have a fixed value.

Hypothesis Testing Test Partial Test (t test)
Partial hypothesis testing (t-test) is used to evaluate the effect of community education and training levels on work productivity separately. The criterion used is if the t-count value > t-table value with a significance level <0.05, then the hypothesis is accepted. Conversely, if the t-count value < t-table value with a significance level of > 0.05, then the hypothesis is rejected. To find the t-table value, the formula df = n -k is used, with n = 65 and k = 3. So, df = 65 -3 = 62 at a significance level of α = 5%. Using this calculation, the t-table value is 1.99. The results of the t test analysis using the SPSS 24.0 program can be seen in the following table: Based on the results obtained above, it can be seen that the results of the t test for the education level variable (X1) show a tcount of 0.795 and a significance value of 0.430. Thus tcount>ttable (0.795<1.99) and the significance value at the level of = 5% is 0.430>0.05. So based on the hypothesis testing criteria partially that the first hypothesis is rejected which means there is a negative and significant effect between the level of education (X1) on work productivity in Ulos village. Furthermore, the results of the t test for the community training variable (X2) show a tcount of 4.875 and a significance value of 0.000. Thus tcount>ttable (4.875>1.99) and the significance value at the level of = 5% is 0.00<0.05. So based on the hypothesis testing criteria partially that the second hypothesis is accepted which means there is a positive and significant effect of community training (X2) on work productivity in Ulos village.

Simultaneous Test (Test F)
Simultaneous hypothesis testing (F test) is used to evaluate whether the variable level of education (X1) and community training (X2) have a simultaneous effect on work productivity in Kampung Ulos (Y). The hypothesis is tested by comparing the F-count value with the F-table value, provided that if the F-count value > F-table value or a significance value <0.05, then the hypothesis is accepted. Conversely, if the F-count value <F-table value or significance value> 0.05, then the hypothesis is rejected. To find the F-table value, use the formula df(1) and df(2) with df(1) = K -1 and df(2) = n -K, where K is the number of predictor variables and n is the number of samples. In this study, df(1) = 1 and df(2) = 65 -3 = 62. Based on this calculation, the F-table value used is 4.00. The results of the F test analysis using the SPSS 24.0 program can be seen in the following table: Based on the results obtained above it is known that the results of the F test showed an Fcount value of 28.789 with a significance value of 0.000. Thus, it means that Fcount>Ftable (28.789 > 4.00) and the significance value at the = 5% level is 0.000 <0.05. This means that the third hypothesis is accepted, so it can be concluded that there is a simultaneous influence between the level of education (X1) and community training (X2) on work productivity in Ulos village (Y).

Coefficient of Determination (R2)
The coefficient of determination is used to determine the percentage contribution or contribution of independent variables, such as level of education (X1) and community training (X2), to the dependent variable, namely work productivity in Ulos Village (Y). To calculate the contribution of the influence of variables X1 and X2 on variable Y, the SPSS 24.0 program is used. The magnitude of the coefficient of determination can be found in the following table, which shows the extent to which variables X1 and X2 contribute to variations in variable Y: Based on the results obtained above, it can be seen that the coefficient of determination (R2) is 0.482 or 48.2%. Thus this value means that the variable level of education (X1) and community training (X2) contributes 48.2% to work productivity (Y)., while the remaining 51.8% is influenced by other variables that do not discussed in this study.

DISCUSSION The Effect of Education Level on Work Productivity
Based on the results of data processing, it can be seen that the results of the t test for the education level variable (X1) show a tcount of 0.795 and a significance value of 0.430. Thus tcount>ttable (0.795<1.99) and the significance value at the level of = 5% is 0.430>0.05. So based on the hypothesis testing criteria partially that the first hypothesis is rejected which means there is a negative and significant effect between the level of education (X1) on work productivity in Ulos village. This is in line with Astuti's research (2020) which states that education is not the main factor causing an increase in the work productivity of BUMDes Makmur Adil Sejahtera (M.A.S) employees in Banyumas.
The results of the study show that to achieve a high level of productivity, employees of BUMDes Makmur Adil Sejahtera (M.A.S) in Banyumas do not need to have a higher level of education. In this case, the level of education is not something that influences the process of work activities, especially the work productivity of the craftsmen in Ulos village. This is because everyone who works, especially those who work as craftsmen in Ulos Village, are free from any educational background, be it elementary school to university. The higher a person's education does not necessarily mean that his productivity is high and vice versa. The lower a person's education level does not mean that his work productivity is also low. This is because work productivity depends on how much enthusiasm is in working and carrying out the responsibilities that have been given. Then, someone who is able to maintain work productivity certainly has a high sense of work commitment and considers that the work given is an authority as well as a mandate that must be carried out. So that it is more dominant in the direction of one's morals and character as a responsible worker. Someone who is moral and responsible certainly doesn't look at the level of education because everyone from behind different levels of education certainly has a variety of different characters.
Tirta Umbu, South Nias Regency is an example of a company that provides training to all employees, both new and those who have worked for a long time. Through this training program, employees gain knowledge and skills that enable them to carry out their duties effectively, efficiently and with quality, so that high work productivity can be achieved. It is expected that every employee in a company is able to work effectively and efficiently, both in terms of quality and quantity. To improve the ability of employees, companies need to provide training. The purpose of this training is to improve, maintain and increase the work productivity of employees. Through training, employee work productivity can be increased, and the quality and quantity of production will be better. This is because training helps improve technical skills, interpersonal skills, and managerial skills of employees. This training and development aims to maintain and improve the good performance of employees. With effective training, employees will be more motivated to increase their work productivity. When employees are able to attend training properly, the knowledge, skills and expertise they gain from training will contribute to increasing their work productivity.

The Influence of Community Education and Training Levels on Work Productivity
Based on the results obtained above it is known that the results of the F test showed an Fcount value of 28.789 with a significance value of 0.000. Thus, it means that Fcount>Ftable (28.789 > 4.00) and the significance value at the = 5% level is 0.000 <0.05. This means that the third hypothesis is accepted, so it can be concluded that there is a simultaneous influence between the level of education (X1) and community training (X2) on work productivity in Ulos village (Y). Productivity is a view of life and a mental attitude that focuses on improving the quality of life. The concept of productivity includes a comparison between the results achieved (output) and the resources used (input) within a certain period of time. The purpose of work productivity is so that work can be done with efficiency and effectiveness in order to achieve the desired goals. One way to increase employee work productivity is through education and job training. Productivity assessment is carried out by considering the knowledge, skills and attitudes of employees. Therefore, education and job training have an important role in increasing employee work productivity. Education in an organization is a development process in accordance with the goals of the organization. On the other hand, training is part of the educational process that aims to improve specific skills or abilities. Both education and training can be considered as a form of investment. Training is an effort made by companies to improve the quality of human resources in the context of work. Training is a process used to shape and prepare employees by enhancing their expertise, skills, knowledge and behavior.
Providing training or education to employees in a company is the responsibility of the manager. Through training and education, companies can ensure the availability of skilled, trained, and critical-thinking workforce. In addition, training also plays a role in maintaining employee stability and encouraging them to make long-term contributions. In the context of human resource development, education and training are efforts to develop human potential, especially in the development of intelligence and personality.

CONCLUSIONS AND RECOMMENDATIONS
Based on the analysis that has been done, the researcher concludes that the education variable does not have a significant effect on the work productivity variable. Based on the analysis that has been done, the researchers found that the job training variable (X2) has a significant influence on work productivity variables. Based on the analysis that has been done, the researchers concluded that simultaneously, the education variable (X1) and the job training variable (X2) have a significant influence on work productivity variables.