E-Learning Acceptance in Business Administration Department State Polytechnic of Banjarmasin

E-learning is learning using Internet media. Acceptance of E-learning at the Business Administration Department, State Polytechnic of Banjarmasin, is measured by the Technology Acceptance Model method. This research is descriptive and verification research. The sample is 171 people. The data analysis technique uses the Structural Equation Model. The study results show that perceived ease of use does not affect behavioral intention to use technology but attitudes toward using technology. Perceived enjoyment influences attitude towards using technology. Behavioral intention to use technology affects actual technology use but does not affect satisfaction. Attitude towards using technology affects actual technology use. Actual technology use affects satisfaction. The variables with the greatest direct effect are perceived enjoyment on attitude towards using technology, perceived ease of use on behavioral intention to use technology, attitude towards using technology on actual technology use, and actual technology use on satisfaction. The variables with the greatest total effect are perceived enjoyment on attitude, perceived ease of use on behavioral intention to use technology, attitude on actual technology use, and perceived enjoyment on satisfaction


INTRODUCTION
Online learning is flexible. Information and lecture materials can be accessed at any time. Varied learning methods will increase student learning achievement (Prasetya and Harjanto 2020). In the online learning method, lecture material is accessed online, and there is an interaction between lecturers and students (Zhafira and Chairiyaton 2020). Moreover, uploading and updating teaching materials is easy to do (Anhusadar 2020). E-learning is a learning activity using Internet media. Elearning is interactive, interesting, entertaining, and equipped with e-books (Prasetya and Harjanto, 2020). Implementation of e-learning such as material in the web version, integrated models of learning objects in the form of multimedia, courses, communications, discussions, educational tools, and others (Rahmawati and Narsa 2019).
The future era of education will require the development of educational programs that demand the readiness of all information technology installations and teachers who can answer the challenges of technological advances and the challenges of high standards in the learning system. The online lecture system positively contributes to encouraging disparities in the quality of higher education institutions in Indonesia. Indications include 1) Minimizing limited access to higher education with a certain quality. 2) Cutting the limited facilities that have been considered as one of the obstacles to the low quality of higher education. 3) Eliminate the definition of limitations on certain materials. 4) The online tuition system provides broad access to educational resources, especially at top universities (Mustofa, Chodzirin, and Sayekti 2019).
There are several student complaints regarding the implementation of e-learning. Complaints include problematic internet quota, internet network disruption, monotonous learning, unattractive power points, and lack of use of other media such as video films. Interaction among students is felt to be lacking. Students have difficulty understanding lecture material and many assignments.
Student satisfaction is one of the benchmarks for the quality of e-learning. In the e-learning context, TAM (Technology Acceptance Model) is a suitable theory for understanding user acceptance of e-learning. To measure the level of student acceptance of e-learning, Technology acceptance model (TAM) is a model that explains the factors that influence the acceptance of information technology.

Formulation of the Problem
The formula is as follows: 1) Does perceived ease of use affect behavioral intention to use technology in implementing elearning? 2) Does perceived ease of use affect attitudes towards using technology in implementing elearning? 3) Does perceived enjoyment affect attitude towards using technology in implementing elearning? 4) Does the behavioral intention to use technology affect the actual use of technology in the implementation of e-learning? 5) Does behavioral intention to use technology affect satisfaction in the implementation of elearning? 6) Does the attitude towards using technology affect the actual use of technology in the implementation of e-learning? 7) Does actual technology use affect satisfaction in the implementation of e-learning? 8) What are the variables that have the greatest direct influence on attitude, behavioral intention to use technology, and actual use of technology in implementing e-learning? 9) What are the variables that have the greatest indirect effect on the actual technology and satisfaction variables in implementing elearning? 10) What are the variables that have the greatest total influence on attitude, behavioral intention to use technology, actual technology use, and satisfaction in implementing e-learning?

Theoretical Basis Previous Research
The development of e-learning uses valuable teaching materials, easy to understand through the mapping stages, implementing online media creation, implementation, and evaluation (Rusdiana and Nugroho 2020). E-learning benefits students regarding lecture information, teaching materials, assignments, and online quizzes (Suhendi 2015). Mastery of technology in the form of e-learning influences student satisfaction. Satisfaction is one of the main targets in human resource management because it will impact improving human resources capabilities (Septyarini and Cahya 2021).
The level of student satisfaction is one of the benchmarks for the quality of e-learning. The level of student satisfaction using e-learning can show that students enjoy the online learning process. Quality learning will have a high level of satisfaction for its users. One of the methods used to measure the effectiveness of learning is the Technology Acceptance Model. In the TAM method, several factors affect a media's user satisfaction level. The factors used by this method are perceived ease of use, perceived enjoyment, and actual technology use.
There is a positive and significant influence of technology mastery on student satisfaction in online learning. Mastering technology can facilitate activities, increase effectiveness and efficiency and increase satisfaction. Mastery of technology will improve students' abilities (Septyarini and Cahya 2021).

E-learning
E-learning is a learning activity utilizing internet media, is interactive, engaging, and e-books are available (Prasetya and Harjanto 2020). Elearning is an option. They are implementing elearning, such as material on web servers, integrated learning objects models in multimedia, courses, communications, discussions, educational tools, and others (Rahmawati and Narsa 2019). E-learning utilizes multimedia technology, virtual classes, video streaming, voice messages, animated online text, CD ROM, e-mail, telephone conferences, and online video streaming (Garini et al. 2022). Elearning can increase knowledge and skills. The most popular online learning media are WhatsApp and Google Classroom (Zhafira and Chairiyaton 2020).
The benefits of e-learning include the effectiveness of learning time, economic study costs, interaction, and sharing of information on learning materials at any time (Sulastri and Ahmadi 2021). Elearning facilitates students in controlling content, managing learning stages, learning speed, streamlining time, choosing media, and managing access to e-learning methods and materials. Students play an active role during learning; this will improve learning outcomes by selecting appropriate media. The application of e-learning must ensure the quality demanded and needed by higher education institutions so that e-learning practices can be successful (Prasetya and Harjanto, 2020). E-learning provides innovation to changes in the teaching and learning process, such as teaching materials can be visualized more dynamically and interactively in the form of files, music videos, and animations (Mustarin and Wiharto 2018).

Technology Acceptance Model
To measure the level of student acceptance of online learning using learning media, you can use the Technology Acceptance Model concept. Technology Acceptance Model (TAM) is a model that explains the factors that influence the acceptance of information technology. TAM is also referred to as the level of acceptance of technology users, which can be determined by six factors: ease of use, usefulness, attitude toward using, behavioral intention to use, and actual system usage. The purpose of this technology acceptance rate is to explain and measure an information technology's acceptance level (Krisnanto and Anistyasari 2021).
The TAM model is an adaptation of the Theory of Reasoned Action (TRA) model by Fishbein and Ajzen, in which there are two main constructs: 1) Usability (perceived usefulness/PU), the situation when using the system can improve performance. 2) Ease of use (perceived ease of use/PEU), that is, there is no need to work hard to use the system (Rahmawati and Narsa 2019), (Que and Tanaamah 2017), (Maita and Almarozi 2022) The TAM technology acceptance model is used to measure the level of user acceptance of technology, especially information technology (Napitupulu 2017). Perceived usability and ease of use are the main reasons for users. It will cause the user to accept the use of the information system (Wiardi et al. 2022). Predict and explain acceptance and use (Fecira and Abdullah 2020).

Indicators of the Technology Acceptance Model
Following are some indicators of the Technology Acceptance Model. 1) Perceived usefulness Perceived usefulness describes the level of user confidence that the system will improve its performance. Users believe that their achievement and performance will increase because it is beneficial for users (Rahayu, Budiyanto, and Palyama 2017).
2) Perceived Ease of Use Perceived ease of use describes the level of user confidence that technology is easy and simple (Rahayu et al. 2017), or ease of learning and applying new technologies and their applications (Setiawan and Sulistiowati 2018). Indicators of ease of use of information technology include being easy to learn, according to user wants, increasing skills, and being easy to operate (Indrayani and Suwarman 2018). It is in accordance with Davis' statement: easy to learn, easy to access, easy and clear, flexible, easy to use, easy to become skillful, attitude, behavioral intention, actual usage.

3) Attitude Towards Use
Attitude towards use is an attitude towards a particular pleasant object or vice versa. It reflects of feelings for objects in the form of good or bad conditions, beneficial or detrimental (Rahayu et al. 2017). Attitude consists of cognitive, affective, and components related to behavior (Indrayani and Suwarman 2018). Indicators of Attitude toward using technology include fun technology, good ideas, and use is considered necessary, encouraging all parties to use, and wise ideas (Adhiputra 2015).

4) Behavioral Intention to Use
Behavioral intention to use is a person's desire to keep using certain technologies, such as motivation to keep using, motivating other uses (Rahayu et al. 2017), (Indrayani and Suwarman 2018).

5) Actual System Usage
Actual usage technology or natural conditions of use of information systems (Rahayu et al. 2017) is an externally measured psychomotor response actual use, and increases productivity (Adhiputra 2015). It measures the frequency and duration of technology use (Indrayani and Suwarman 2018).

Student Satisfaction
Satisfaction is the feeling of pleasure or disappointment after comparing it to performance (Utami 2021). Student satisfaction obtained is an indicator of the implementation of e-learning according to student expectations and needs (Ulinuha and Novitaningtyas 2021). According to Kotler and Keller, satisfaction is a feeling of pleasure or disappointment that arises after comparing performance. If performance is below expectations, then dissatisfaction, but if performance meets expectations, consumers are satisfied. Satisfaction is an assessment that the service has provided a level of enjoyment where this level of fulfillment can be more or less. Satisfaction is the result that is felt for a performance or service that is used continuously and is in accordance with expectations, wants, and needs (Garini et al. 2022). The quality of e-learning can be known from the satisfaction felt by students. E-learning users enjoy the online learning process. Quality learning will have a high level of satisfaction for its users (Utami 2021).
Student satisfaction is essential for higher education as a provider of educational services for students. The impact of the implementation of elearning is analyzed to know student satisfaction. Student satisfaction is used as evaluation material to improve the quality of online learning (Ulinuha and Novitaningtyas 2021). College student satisfaction with a system must always be considered to improve the performance it produces (Septyarini and Cahya 2021). The level of student satisfaction shows how much e-learning is accepted. In addition, it is also a benchmark for the quality of online learning. Several factors measure the level of student satisfaction, such as content, accuracy, form, ease of use, and timeliness (Prasetya and Harjanto 2020). And satisfaction affects user behavior in the future. The more satisfied, the more inclined to use E-Learning as a learning medium for the future (Gaffar and Septyandi 2020).

Hypothesis
The variables in this study consist of exogenous and endogenous variables. Exogenous variables consist of perceived enjoyment and perceived ease of use. Meanwhile, endogenous variables consist of behavioral intention to use technology, attitude towards using technology, actual use of technology, and level of student satisfaction. Student satisfaction regarding the implementation of e-learning is measured using e-learning indicators, discussing satisfaction in using e-learning with others, elearning performance, and using e-learning is the right decision.
This research is formulated in the model as follows:

Characteristics of Respondents
The sample in this research is 171 respondents. 63.7% were female. And 36.3% are male. Most respondents came from the Business Administration Study Program (64.3%). The remaining 35.7% is from the Informatics Management Study Program.

Picture 2. Research Model Validity Testing
Convergent Validity is used to test the validity.

Picture 3. CFA Exogenous Variables
Validity testing can be seen from the value of the loading factor. If the loading factor value on the PE6 item is less than 0.5, the item is declared invalid, so it is removed from the model.

Picture 4. CFA Endogenous Variables
If the loading factor value in BTUT4, ATUT1, and K5 items is less than 0.5, the item is declared invalid, and removed from the model.

Reliability Testing
Construct Reliability (CR) is one indicator of reliability. Reliability can be measured by calculating the CR and VE values with the following formula:

SEM Assumption Testing
SEM assumptions include the assumption of the adequacy of the sample, the assumption of normality, and the assumption of outliers.

Evaluation of the Goodness of Fit Criteria Index
The goodness of fit value of the research model can be seen in the following table.

Model modification
The model modification was done to obtain acceptable goodness of fit model criteria. The output value of the modification indices identifies the possibility of modifying the research model.

Picture 5. Model Modification Results
The goodness of fit index is shown in the table below.

Analysis of Direct Effects, Indirect Effects, and Total Effects
The direct influence of this research model is presented in the following table According to the measurement results, the variable with the biggest direct impact on attitude is perceived enjoyment, which has a value of 0.7, and the variable with the biggest direct impact on behavioral intention to use technology is perceived ease of use, which has a value of 0.177. The attitude variable, at 0.784, has the most direct impact on the actual technology use variable. Actual technology use, which has a direct impact on satisfaction of 0.433, is the most significant factor.
From the measurements, the variable with the greatest indirect effect on the actual technology use variable is the perceived enjoyment variable, equal to 0.527. The variable that has the greatest indirect effect on the satisfaction variable is the attitude variable, which is similar to 0.527.  The results of measuring the total effect between variables are shown in the table below. Based on the measurement results, it is known that the perceived enjoyment variable, with a value of 0.672, has the greatest overall influence on attitude, and the perceived ease of use variable, with a value of 0.177, has the greatest overall influence on behavioral intention to use technology. The attitude variable, with a value of 0.784, has the biggest overall impact on the actual technology use variable. The variable measuring felt enjoyment, with a value of 0.794, has the most overall impact on satisfaction.  2) The effect of perceived ease of use on attitude towards using technology in implementing elearning. Hypothesis 2 states that the test results show that perceived ease of use affects attitude towards using technology in the implementation of elearning, demonstrated in the acquisition of a regression weight value of β = 0.204, S.E. = 0.086, C.R. = 2.374, p = 0.018. This research shows that the higher the perceived ease of use, the higher the attitude towards using technology in implementing e-learning.

Hypothesis test
Proven concepts that support research on elearning systems are easy and self-explanatory. Very helpful in completing tasks more quickly and effectively. E-learning facilitates sharing and discussion without being bound by geography and time (Fecira and Abdullah 2020). Perceived ease of use significantly affects usage attitudes (Khoiruddin, Wahyuningsih, and Nuryakin 2022).
3) The effect of perceived enjoyment on attitude towards using technology in implementing elearning. Hypothesis 3 states that the test results show that perceived enjoyment affects attitude toward using technology in the implementation of e-learning, shown in the acquisition of a regression weight value of β = 0.835, S.E. = 0.141, C.R. = 5.915, p = 0.000.
This study shows that the higher the perceived enjoyment, the higher the attitude towards using technology in implementing e-learning.
A proven concept provides research support (Fecira and Abdullah 2020) that e-learning systems have unique benefits such as flexibility in the learning process, automation, interactive, fun, and broad access to upload and download lecture material. The use of technology is acceptable because students enjoy being in the e-learning system (Pradana, Bachtiar, and Priyambadha 2019). With the implementation of e-learning, students feel happy, enjoy, interested, feel comfortable, and the visuals of the lecturer's teaching material are interesting. The effect of perceived enjoyment on satisfaction in implementing e-learning.
Hypothesis 4 states that the test results show that perceived enjoyment affects satisfaction in the implementation of e-learning, shown in the acquisition of a regression weight value of β = 0.775, S.E. = 0.126, C.R. = 6.151, p = 0.000. This research shows that the higher the perceived enjoyment, the higher the satisfaction with implementing e-learning.
The tested concept supports perceived enjoyment research influencing student satisfaction in online learning during the Covid 19 period at the Banjarmasin State Polytechnic Business Administration Department (Huwaida, Rofi'i, and Imelda 2021). Satisfaction can be increased by perceived enjoyment through proven indicators. With the implementation of e-learning, students feel happy, enjoy, interested, and comfortable, and the visuals of the lecturer's teaching material are interesting. 4) The effect of behavioral intention to use technology on actual technology use in implementing e-learning. Hypothesis 5 states that the test results show that behavioral intention to use technology affects actual technology use in the implementation of elearning, shown in the acquisition of a regression weight value of β = 0.154, S.E. = 0.076, C.R. = 2.027, p = 0.043. This research shows that the higher the behavioral intention to use technology, the higher the actual use of technology in the implementation of e-learning.
The tested concept provides research support that behavioral intention to use technology influences actual technology use (Rahmawati and Narsa 2019). Actual technology use can be increased by behavioral intention to use technology through proven indicators such as the intention to use elearning every day, updating materials, and checking personal levels. 5) The effect of behavioral intention to use technology on satisfaction in implementing e-learning. Hypothesis 6 states that the test results show that behavioral intention to use technology has no effect on satisfaction in the implementation of elearning, shown in the acquisition of a regression weight value of β = -0.087, S.E. = 0.076, C.R. = -1.143, p = 0.253. 6) The effect of attitude towards using technology on actual technology use in implementing e-learning. Hypothesis 7 states that the test results show that attitude towards using technology affects actual technology use in the implementation of e-learning, shown in the acquisition of a regression weight value of β = 0.727, S.E. = 0.114, C.R. = 6.379, p = 0.000. This research shows that the higher the attitude towards using technology, the higher the actual use of technology in implementing e-learning.
The tested concept provides research support. Attitude toward using has a direct positive and significant relationship to the actual usage (Adhiputra 2015). Actual technology use can be increased by an attitude towards using technology through proven indicators such as the need for elearning, a pleasure to use, references for completing creative assignments, developing innovative ideas, discussing with lecturers and classmates, and encouraging classmates to use e-learning. 7) The effect of actual technology use on satisfaction in the implementation of e-learning. Hypothesis 8 states that the test results show that actual technology use affects satisfaction in the implementation of e-learning, shown in the acquisition of a regression weight value of β = 0.516, S.E. = 0.122, C.R. = 1.741, p = 4.226. This research shows that the higher the actual technology use, the higher the satisfaction with implementing elearning.
The tested concept supports research on actual technology use, which has a positive and significant effect on student satisfaction in online learning during the Covid 19 period at the Department of Business Administration, State Polytechnic of Banjarmasin (Huwaida et al. 2021).
Practically, this research provides an understanding that satisfaction can be increased by actual technology use through proven indicators such as using e-learning to support learning activities, always accessing lecture materials, downloading and uploading assignments, checking levels, and being satisfied with using e-learning.

CONCLUSION
The conclusions of the study are as follows: 1. Perceived ease of use does not affect behavioral intention to use technology in the implementation of e-learning, shown in the acquisition of a regression weight value of β = 0.2, S.E. = 0.115, C.R. = 1.741, p = 0.082. 2. Perceived ease of use affects attitude towards using technology in the implementation of elearning, shown in the acquisition of a regression weight value of β = 0.204, S.E. = 0.086, C.R. = 2.374, p = 0.018. This research shows that the higher the perceived ease of use, the higher the attitude towards using technology in implementing e-learning. 3. Perceived enjoyment affects attitude towards using technology in the implementation of elearning, shown in the acquisition of a regression weight value of β = 0.835, S.E. = 0.141, C.R. = 5.915, p = 0.000. This study shows that the higher the perceived enjoyment, the higher the attitude towards using technology in implementing elearning. = 0.126, C.R. = 6.151, p = 0.000. This research shows that the higher the perceived enjoyment, the higher the satisfaction with implementing e-learning. 4. Behavioral intention to use technology affects actual technology use in the implementation of elearning, shown in the acquisition of a regression weight value of β = 0.154, S.E. = 0.076, C.R. = 2.027, p = 0.043. This research shows that the higher the behavioral intention to use technology, the higher the actual use of technology in the implementation of e-learning. 5. Behavioral intention to use technology does not affect satisfaction in the implementation of elearning, shown in the acquisition of a regression weight value of β = -0.087, S.E. = 0.076, C.R. = -1.143, p = 0.253. 6. Attitude towards using technology affects actual technology use in the implementation of elearning, shown in the acquisition of a regression weight value of β = 0.727, S.E. = 0.114, C.R. = 6.379, p = 0.000. This research shows that the higher the attitude towards using technology, the higher the satisfaction with implementing elearning. 7. Actual technology use affects satisfaction in the implementation of e-learning, shown in the acquisition of a regression weight value of β = 0.516, S.E. = 0.122, C.R. = 1.741, p = 4.226. This research shows that the higher the actual technology use, the higher the satisfaction with implementing e-learning.
8. The perceived enjoyment variable, which is equal to 0.7, and the perceived ease of use variable, which is equal to 0.177, are the variables that have the biggest direct impacts on the attitude and behavioral intentions to use technology, respectively. The attitude variable, at 0.784, has the most direct impact on the actual technology use variable. Actual technology use, with a coefficient of 0.433, is the factor that directly affects the happiness variable the most. 9. The perceived pleasure variable, with a value of 0.527, has the biggest indirect influence on the actual technology usage variable. The attitude variable, which is similar to 0.527, has the most indirect impact on the satisfaction variable. 10. The perceived enjoyment variable, with a total influence of 0.672, and the perceived ease of use variable, with a total influence of 0.177, respectively, have the biggest effects on attitudes and behavioral intentions to use technology, respectively. The attitude variable, with a value of 0.784, has the highest overall impact on the variable indicating real technology use. The variable measuring felt enjoyment, with a value of 0.794, has the most overall impact on satisfaction.