Statistical Analysis in Determining the UIN Kiai Haji Achmad Siddiq (UIN KHAS) Jember Students' Readiness Toward the Internet-Based Learning System

: This research focuses on finding out how far the readiness of UIN KHAS Jember students in facing this e-learning learning system is, so research is carried out in the form of a survey. From the survey results, it is hoped that data can be obtained regarding what factors influence the e-Learning-based learning system to be applied to UIN KHAS Jember students. The variables used are Study Habits and Technology Habits. The data analysis method used in this research is the 1 sample proportion test and the Chi-Square test. Respondents used were UIN KHAS Jember students Class of 2015, 2016, 2017, and 2018. From a series of data collection to the analysis stage, several conclusions can be drawn, namely the number of UIN KHAS Jember students ready to face the e-learning learning system by 60% (or more than 50% of students). The variables that affect student readiness in dealing with e-learning learning systems are study habits and technology habits. From each of these variables, it turns out that only the attitude factor towards the learning system and the length of time on the internet are very significant or affect student readiness .


INTRODUCTION
Electronic learning or E-Learning is a form of distance education that uses electronic media based on web technology to deliver material and communication between lecturers and students. E-learning is currently being developed in developed and developing countries as one of the learning media that can overcome the problem of limited time, place, and facilities. The purpose of this e-learning teaching method is to achieve the proper teaching method so that it can increase the percentage of student graduation, and it is hoped that the highest failure rate is around 10%. 1 Previous research on the use of e-learning in distance learning during the Covid-19 pandemic is a qualitative descriptive study on the use of elearning in distance learning during the pandemic, which results in a level of understanding of the material on learning using e-learning. 2 The E-Learning learning model in improving the quality of learning, which in this study discusses the literature review regarding learning and teaching models using e-learning, produces a breakthrough in the field of education. Another research on the use of elearning for teachers and lecturers in the digitalization era 4.0 resulted in a statement that students and educators are required to be technology literate, or master technology and must have specific technical skills to learn and teach creatively. On the contrary, this 1 Chandrawati, 'Pemanfaatan E-Learning Dalam Pembelajaran', Jurnal Cakrawala Kependidikan, 8.2 (2010), 101-203. 2 Hariani. P.P, 'Pemanfaataan E-Learning Pada Pembelajaran Jarak Jauh Di Masa Pandemi Covid -19', Jurnal Kajian Konseling Pendidikan, 3.2 (2020), 41-49. study was conducted to prove the readiness of students to conduct elearning using a proportional survey method in taking respondents, and their readiness numbers were proven quantitatively (numeric based on numbers) using statistical analysis methods.
Using the e-learning method, expect to increase time efficiency considering the amount of material that must be given is temporarily limited. This method is also expected to make it easier for students to understand the lectures as one of the technology campuses that have used the internet network in each faculty at UIN KHAS Jember. This campus tries to promote this system as the latest learning system because it is considered that the process will be straightforward.
To find out how far the readiness of UIN KHAS Jember students in facing this e-learning learning system, a survey was conducted. From the results of this survey, it is hoped that data can be obtained regarding what factors affect the e-Learning-based learning system to be applied to students of UIN KHAS Jember.
In this study, several issues were raised as study material, including: 1. How are the students of UIN KHAS Jember ready to face the e-learning system? 2. How do determine the variables that affect the readiness of UIN KHAS Jember students in facing the e-learning learning system? 3. How are these variables related to the readiness of UIN KHAS Jember students to face the e-learning learning system?
The objectives of this research are as follows: 1. To find out how readiness UIN KHAS Jember students are to face the e-learning learning system 2. 2. To find out the variables that affect the readiness of UIN KHAS Jember students in facing the elearning learning system 3. 3. To find out the relationship between these variables on the readiness of UIN KHAS Jember students to face the e-learning learning system.

E-Learning
Electronic learning or Elearning is a learning process system that utilizes information technology in the form of computers equipped with telecommunication facilities, such as the internet, intranet, extranet, and multimedia such as graphics, audio, and video. Both are the primary media in delivering material and interaction between teacher or lecturer and learners or students. 3 Information and communication technology-based learning models using e-learning result in changes in learning culture in the context of learning. There are at least four essential components in building a learning culture using the e-learning model; There are two models of online learning material development. In the first model, the lecturer builds a material with parts of the material using a computer. Each section can be read and studied offline by downloading it from the internet or a shared CD. 5 In the second model, the lecturer builds learning materials with online material development facilities. Lecture material is entered into the system piece by piece, which is assembled as a whole in the system. Students can only attend lectures in full through the same system online. With this model, offline distribution can only be done after the development of lecture material is complete or chapter by chapter.

Sample Proportion Test
The following hypothesis is used to determine 1 sample proportion of the population: H0 : p = p0 H1 : p ≠ p0or p > p0or p < p0 where p = populationproportion p0 = early estimate/proportionhipotesis Reject condition H0if Iz-countI > ztableor p-value < alpha (0,05). Proportion test approach with standard normal distribution ( N (0,1) ) using the z test statistic withformula: where p is the proportion obtained from the sample to estimate the population proportion. 7

Proportion Difference
For the first line subject,   Figure 2X2 , relative risk is a ratio

Relative Risk
The ratio is a non-negative real number. Relative Risk is worth 1, indicating the relationship is mutually independent. Comparison in the second response used a different relative risk. 9   Pearson (1900Pearson ( ,1922  for all I, j, and k.Wjconover. 12

METODE
The research procedure used in this study can be seen in the following chart. The variables used as materials for making questionnaires and further analyzed in this study were divided into three, namely:

RESULT AND DISCUSSION
Before further discussing this research, it is necessary to know how the objectives and general description of the respondents are the objects of research..

Respondents Descriptive Statistics
As previously explained, the target of this research was UIN KHAS Jember students from both undergraduate or strata 1 and postgraduate strata 2 levels from various generations, 2015, 2016, 2017, and 2018 academic years. This survey was conducted using proportional simple random sampling. Undergraduate and postgraduate students use the online method via WhatsApp Application assisted by the google form application due to the pandemic period of students studying at home. The respondents who were successfully surveyed can be described as follows: The number of respondent questionnaires used as material for analysis is 99 respondents. This number has a sufficient proportion because it can be seen from the number of students of UIN KHAS Jember, the majority of whom are women, and many are from Strata S1. The class selection is based on the number of students who are still in college and the length of time they have been in college. It is described in more detail as follows: It can be seen that after validating the less valid questionnaires, the results of graphs and descriptive statistics are not much different from expectations. The questionnaire determines validity by the presence of the information needed for analysis and the steps to deal with this problem by adding respondents and, if possible, returning the questionnaire that is considered less valid.

A B
Visually, previous charts and graphs show the readiness of UIN KHAS Jember students for the elearning learning system, and it can be stated that 60% of students answered ready, and 13 % of students answered not ready. The remaining 27% answered they did not know/were doubtful. We have to prove this statement not only with graphs or descriptive statistics but need to be analyzed further to make sure that UIN KHAS Jember students are ready to implement an e-learning learning system and know what factors influence it.
To ensure that 60% of UIN KHAS Jember students are ready to face the e-learning learning system, a 1 sample proportion test is carried out as follows: The hypothesis used: H0 : p = p0 H1 : p ≠ p0or p > p0or p < p0 where p = the value of the proportion of observations (sampel) and p0 = the estimated value of the proportion of a population. Reject H0if p-value < alpha (0,05) Based on the proportion test analysis series of 1 sample, p-values are obtained, which can be seen in table 4.3 below: Based on the p-value above, it can be seen that the p-value will be more than 0.05 when the p0 value is equal to 0.6. So, the percentage of UIN KHAS Jember students ready to face the e-learning learning system is 60% or more than 50%.

Relationships between Variables Detection
Previously identified two variables affecting student readiness for e-learning learning systems, study and technology habits. The measurement of the influence of these variables is reviewed from several aspects, for example, learning duration, learning system, duration of stay in front of a computer, and software that is mastered.
The relationship between each variable and student readiness was detected. To detect this relation, the researcher carried out individual chisquare tests. The steps are almost the same when analyzing each of the variables. In this case, one example will be given for the analysis of the Student Readiness variable (Y) on the Study duration variable (X11): or p-value Reject area : In the example, the p-value is 0.156 > alpha (0.05), and the Chi-square count is 6,647, which is smaller than the table value of 9,488. So, accepting H0 or length of study does not affect student readiness, and the full results can be shown in the following table: Learning Habit Variable It can be seen that the X18 variable has a relationship with student readiness because it has a chisquare count that is greater than the chi-square table (p-value less than 0.05). The X18 variable indicates that the X17 and X15 variables have an influence but are less significant. A variable like this is analyzed individually because the possibility of a joint bond with other variables exists. The next step is to increase the table's dimensions into a 3dimensional table by including two variables study habits to student readiness. Not all combinations are tested because it is seen whether they have a mutual relationship between the two or not. As a result, four possibilities will be tried and can be seen in the following table. It turns out that after testing the relationship simultaneously with other variables that may have an effect, the results are not so significant. Only the variable X13 (student activity) with the variable X18 (attitude towards new learning) interact can affect the readiness of UIN KHAS Jember students to face the e-learning learning system. This finding is indicated by the p-value, which is less than 0.05 (alpha), so that if the two variables interact, it will affect student readiness. Meanwhile, the interaction of the other variables has no effect. Moreover, there are still many possible interactions between variables that may affect but have not been discussed in this study. It can be seen that the variable X21 (internet surfing duration) has a relationship with student readiness because it has a chi-square count that is greater than the chi-square table (pvalue less than 0.05). X21 variable is possible that there are indications such as the X22 variable that has an influence but are less significant. Both variables should be analyzed in unison because there may be dependencies with other variables. The next step is to increase the table's dimensions into a 3dimensional table by including two  variables from technology habits to  student readiness. Not all combinations are tested because it is seen whether they have a mutual relationship between the two or not. As a result, three possibilities will be tried and can be seen in the following table. It turns out that after testing the relationship simultaneously with other variables that may have an effect, the results are not so significant. Only the variable X21 (internet length) with the variable X22 (internet location) that interact with each other can affect the readiness of UIN KHAS Jember students in facing the e-learning learning system. This is indicated by the p-value, which is less than 0.05 (alpha) so that if the two variables interact, it will affect student readiness. Meanwhile, the interaction of the other variables has no effect. Furthermore, there are still many possible interactions between variables that may affect but have not been discussed in this study.

CONCLUSION
From data series collection to the analysis stage, the following conclusions show: 1. The number of UIN KHAS Jember students ready to face the e-learning system is 60% or more than 50% of students. 2. The variables that affect student readiness in facing the elearning learning system are study habits and technology habits. 3. From each of these variables, it turns out that only the attitude factor towards the learning system and the length of time on the internet are very significant or affect student readiness.