UNIVERSITY XYZ STUDENT EXTRACURRICULAR INFORMATION SYSTEM TOWARDS USER SATISFACTION
2. MATERIALS AND METHODOLOGY
Structural Equation Modeling (SEM) is a multivariate analysis technique that was developed to cover the limitations possessed by previous analysis models that have been widely used in statistical research [3] . The models in question include regression analysis ( path analysis ), path analysis (path analysis), and confirmatory factor analysis ( confirmatory factor analysis ) [4] .
Partial Least Square (PLS) is a powerful analytical method because it can be used on any type of data scale (nominal, ordinal, interval, and ratio) and the terms of assumptions that are more flexible Ibel [5]. PLS is used for confirmation purposes (such as hypothesis testing) and exploration purposes.
Although PLS is preferred as exploration rather than confirmation, PLS can also predict whether or not there is a relationship and then pro position for testing. The main purpose is to explain the relationship between constructs and emphasize the notion of the value of the relationship.
The theory of the model used is [3] , which has a connection of 6 variables from Information Quality,
which can be seen in Figure 1 .
Figure 2. Information Quality Model Theory [1]
The hypotheses that will be raised in this study are as follows.
Premise 1
H 0 : There is no positive influence between the quality of information (Content, Connection, Context and Interaction) on the SKEM with student user satisfaction.
H 1 : There is a positive influence between the quality of information (Content, Connection, Context and Interaction) on the SKEM with student user satisfaction.
Premise 2
H 0 : Student user satisfaction does not have a positive effect on the purpose of using SI SKEM.
H 1 : Student user satisfaction has a positive effect on the purpose of using SI SKEM.
Premise 3
H 0 : There is no positive influence between the quality of information (Content, Connection, Context and Interaction) on the SI SKEM with the satisfaction of the lecturers' users.
H 1 : There is a positive influence between the quality of information (Content, Connections, Context and Interaction) on the SKEM with the satisfaction of the lecturer users.
Premise 4
H 0 : Lecturer user satisfaction does not have a positive effect on the purpose of using SK SK.
H 1 : Lecturer user satisfaction has a positive effect on the purpose of using SK SKEM.
Figure 3. Model Construct
The approach used in this research is quantitative and qualitative approaches. The study was conducted using a survey method. The survey was conducted on 2015, 2016 and 2017 students and XYZ University lecturers. Based on the results of sample calculations using the calculation of the automatic sample calculator at the site http: //
ww.raosoft.com/ samplesize.html [6] , the number of student samples is 310 respondents and the sample of lecturers is 37 respondents. Selection of respondents on the basis of respondents who were found at the location of research from each department. This instrument was developed with a rating scale method used to measure respondents' answers. In this case the Likert scale 1-6 will be used to determine the level of perception of students and lecturers relating to the questions asked. Data analysis was carried out by descriptive analysis and model analysis using Structural Equation Modeling - Partial Least Square (SEM-PLS) analysis . For descriptive analysis using Microsoft Excel, while for model analysis using Smart PLS software.
3. RESULT AND DISCUSSION
This section presents the results of studies that include demographic data, descriptive analysis data and analysis of measurement models and structural models.
3.1 Demographic data
In this demographic data descriptive data are presented regarding gender, department, year of force and age of respondents. In more detail the description of demographic data can be seen in the following graph:
Figure 4. Percentage of Student Respondents by Gender
From Figure 2 that the number of respondents with rounding, the male students were 36% and 64%
of women students. Furthermore, judging from the Department of student respondents, determined from shown in Figure 4.
Figure 5. Respondents Percentage Chart Students Based on the Origin of the Department
From Figure 4 shows that the number of respondents who filled out the highest questionnaire from students was majoring in Information Systems, Management and Accounting. Respondents from the
ICMST 2019 August, 1 2019 Information System were 23%. Management as much
as 17%. Whereas Accounting is 12%. For other majors it is quite balanced. Furthermore, seen from the year of the class Student respondents, determined as shown in Figure 6.
Figure 6. Percentage of Student Respondents Graph by Year Force
From Figure 5 , it is known that the year of the respondent group shows the frequency of submitting SKEM. Because p enulis using random samples, so spreading into 3 parts. For the year 2015, there were 33%. The 2016 class is 32% and for the 2017 class is 35%.
Figure 7. Respondents Percentage Chart Lecturer Based on Gender
From Figure 6 it is known that the number of respondents with rounding, namely Male lecturers as much as 28% and Female lecturers as much as 72%. Furthermore, judging from the Department of respondents lecturers, determined from shown in Figure 7.
Figure 8 Respondents Percentage Chart Lecturers based on the origin of the department
From Figure 5 shows that the number of respondents who filled out the highest questionnaire from lecturers in the Department of Management and Informatics. Respondents from Management lecturers were 21%. While Informatics lecturers are as much as 18%. For the Department of Chemical Engineering and Logistics Engineering, 12% is quite balanced. And the respondents from the Agriculture Industry Technology were the least number, namely 1 person (2%). Furthermore, judging from the age of the lecturer respondents, it is determined as shown in Figure 8.
Figure 9. Respondents Percentage Chart Lecturer by Year of the Force
From Figure 8 shows that the lecturer respondents are at most 72% from the age of 25-30 years. And as many as 26% from 31-35 years old.
3.2 Measurement Model Analysis
32.1. Evaluation of Measurement Model (Outer Model)
There are three criteria in using data analysis
techniques using SmartPLS to test the validity and reliability (Outer Model), namely Convergent Validity, Discriminant Validity and Composite Reliability.
3.2.2. Convergent Validity
This value will be accepted if the value of the loading factor is above 0 , 7 . However, the loading factor value that ranges between 0, 5 and 0.6 would be considered sufficient. Conversely, if the value of the loading factor is less than 0.5, then it is removed from the model [3] .
Table 11. Outer Loading of Student Respondents Indicator X 1 X 2 X 3 X 4 Y 1 Y 2
cont1 0.665
cont2 0.748
cont3 0.686
cont4 0.563
cont5 0.734
cont6 0.704
cont7 0.688
cont8 0.679
cont9 0.626
cont10 0.568
hub1 0.822
hub2 0.883
hub3 0.664
hub4 0.788
kteks1 0.818
kteks2 0.783
kteks3 0.802
kteks4 0.830
kteks5 0.788
kteks6 0.654
kteks7 0.670
int1 0.687
int2 0.813
int3 0.831
int4 0.863
int5 0.776
int6 0.871
int7 0.865
int8 0.851
int9 0.844
KPM1 0.905
KPM2 0.912
KPM3 0.938
KPM4 0.933
KPM5 0.911
TPSI1 0.876
TPSI2 0.865
TPSI3 0.808
Based on Outer Loadingin Table 1 , it can be concluded that the value of the loading factor is greater than 0 , 5 . This means that it meets the criteria ofconvergent validity and can be declared valid.
Table 12. Outer loading Testing stage 3 Respondent Lecturer
Indicator X 1 X 2 X 3 X 4 Y 1 Y 2
cont1 0.899
cont2 0.802
cont3 0.769
cont4 0.699
hub1 0.847
hub2 0.876
hub4 0.770
kteks1 0.878
kteks2 0.878
kteks3 0.921
kteks4 0.911
kteks5 0.713
int1 0.640
int2 0.882
int3 0.875
int4 0.810
int5 0.769
int6 0.776
int7 0.915
int8 0.796
int9 0.798
KPD1 0.834
KPD2 0.875
KPD3 0.933
KPD4 0.949
KPD5 0.929
TPSI1 0.897
TPSI2 0.947
TPSI3 0.925
Based on Outer Loading in Table 2 , testing has been carried out three times, namely:
- The first stage of testing, there are several indicators whose values are invalid or below 0 , 5, namely cont5, cont6, cont7, cont8, cont9, cont10, hub3, and kteks7. Then the indicator must be removed from the model.
- The second stage of testing , there are indicators whose values are invalid or below 0 , 5, namely kteks6. Then the indicator must be removed from the model.
- The third stage of testing, the value of the loading factor is greater than 0.5. This means that it meets the criteria of convergent validity and can be declared valid.
The test of convergent validity is to look at the value of Average Variance Extracted (AVE). The indicator is considered to have good convergent validity if it has an AVE value of more than 0,5 . AVE accrual values can be seen in Table 5 and Table 6.
ICMST 2019 August, 1 2019 Table 13. Student Respondent Convergent Validity
Proxy variable
Average Variance Extracted (AVE)
X 1 0.447
X 2 0.630
X 3 0.587
X 4 0.679
Y 1 0.846
Y 2 0.723
Based on Table 3 indicates that the value of Average Variance Extracted (AVE) variable X 1 is0.447. This means that convergent validity on X1 otherwise not well / not valid.
Table 14. Convergent Validity Responden Dosen Variabel
Proksi
Average Variance Extracted
(AVE)
X1 0,633
X2 0,692
X3 0,746
X4 0,657
Y1 0,819
Y2 0,853
Based on Table 4 shows that the value of Average Variance Extracted (AVE) is more than 0.5. This means that the convergent validity of all variables is declared valid.
3.2.3 Discriminant Validity
Discriminant validity of the measurement model with reflexive indicators is assessed based on cross-loading measurements in the construct. The criterion in cross-loading is that each indicator that measures its construct must be correlated higher with its extract compared to other constructs [7]. The following table shows the Cross-loading value that each indicator has.
From the results of the analysis all proxy variables already have gooddiscriminant validity. Then it can be concluded that the test of discriminant validity validity has been fulfilled, and can be declared valid.
3.2.3. Composite Reliability
The construct reliability test is done by measuring two criteria, namely composite reliability and cronbachalpha. Construct declared reliable if the value of composite reliability and Cronbach alpha above 0.7. The reliability composite test results and cronbach alpha are found in Table 7 and 8. From the tables that are presented, all variables can have compositereliability and cronbach alpha abo ve 0,7. So that everything is said to be reliable.
Table 15. Test the Reability of the Student Respondent construct
Proxy variable
Composite
Reliability CronbachsAlpha
X 1 0.889 0.862
X 2 0.871 0.801
X 3 0.908 0.881
X 4 0.950 0.940
Y 1 0.965 0.954
Y 2 0.886 0.808
Table 16. Test the Resability of the Respondent's Construct Lecturer
Proxy variable
Composite Reliability
Cronbach's Alpha
X 1 0.873 0.808
X 2 0.871 0.778
X 3 0.936 0.912
X 4 0.945 0.933
Y 1 0.958 0.944
Y 2 0.945 0.915
From Tables 5 and 6 presented, it can be seen that all variables have composite reliability and cronbach alpha above 0.7. Then all variables are said to be reliable.
3.3. Evaluation of Structural Models (Inner Model)
After testing the measurement model (outer
model) the next step is testing the structural model (inner model) where to find out whether the hypothesis can be accepted or rejected through the Bootstrapping process [7] .
Bootstrapping is intended to minimize abnormal problems in the research data. This study will use a significant value (α) of 0.1 or 10%.
Table 17. Path Coefficient Estimation in Student RespodentStructural Model
Path
diagram Estimation P-
Value Conclusion X 1 → Y 1 0.145 0.005 Significant X 2 → Y 1 0.178 0,000 Significant X 3 → Y 1 0.085 0.065 Significant X 4 → Y 1 0.551 0,000 Significant Y 1 → Y 2 0.628 0,000 Significant
Based on Table 7, that constructs at X 1 , X 2 , X 3 , and X 4 with User Satisfaction (Y1) as well as constructs of User Satisfaction (Y1) with the Purpose of Using Information Systems (Y2) are stated <0.1, so that these constructs have a significant effect.
From the original sample value or estimated coefficient that all variables have a positive effect.
Only X 3 to Y 1 has a weak influence weighing 0.065.
Table 18. Path Coefficient Estimation in the Structural Model of Respondents of Lecturers Path
diagram Estimation P-
Value Conclusion X 1 → Y 1 0.043 0.754 Not significant X 2 → Y 1 0.327 0.028 Significant X 3 → Y 1 0.155 0.420 Not significant X 4 → Y 1 0.449 0,000 Significant Y 1 → Y 2 0.427 0.004 Significant
Based on Table 12, that constructs at X 2 and X 4 with User Satisfaction (Y1) as well as constructs of User Satisfaction (Y1) with the Purpose of Using Information Systems (Y2) are stated <0.1, so that constructs have an effect significant. Constructions X
1 and X 3 with satisfaction User (Y1) has no significant effect .
From the original sample value or the estimated coefficient only X2 to Y1 , X4 to Y1 and Y1 to Y2 which have a positive influence. Only X1 to Y1 and X3 to Y1 has a weak influence with a weight of 0.754 and 0.420.
3.4. Overall Summary Results
Figure 10. Research Results Model Information:
X : Information Quality SI SKEM X1 : "Content" Proxy Variable X2 : Proxy "Connection" variable X3 : "Contextual" proxy variable X4 : "Interaction" proxy variable
Y1 : Student / Lecturer User Satisfaction Y2 : Purpose of Using Information Systems
€ : Error ( Error )
Table 19. Summary of Hypothesis Testing Premise Hypothesis Results
Premise 1 H 1
There is a positive influence between the quality of information
(Content, Connection,
Context and Interaction) on the SKEM with student user satisfaction.
Be accepted
Premise 2 H 1
Student user satisfaction
influences the purpose of using SI SKEM.
Be accepted Premise H 1 There is a positive Be
ICMST 2019 August, 1 2019 Premise Hypothesis Results
3 influence between the quality of information on the SI SKEM and student user satisfaction.
accepted
Premise 4 H 1
Lecturer user satisfaction
influences the purpose of using SK SK.
Be accepted
4. CONCLUSION
From the results of the evaluation study of the satisfaction of SI SKEM at the University of XYZ, we can conclude the following: The quality of information has an effect on student user satisfaction of 0.740. As well as the quality of information has an effect on lecturer user satisfaction of 0.759. And the recommendation given is that there is a need to socialize the use of XYZ University SKEM SI to students and lecturers. And the development of systems that can facilitate users.
Based on the results of the study, suggestions that can be submitted for further research include:
a. The need for adding question indicators to the questionnaire so that the assessment and user needs can be explored in more detail.
b. The need for deeper evaluation to find out the causes of the not significant relationship between variables.
c. Need further research on other factors and the addition of indicators that can affect user satisfaction to continue using SI SKEM.
d. Addition of sample numbers of student respondents. If the greater the amount of data taken, the higher the accuracy of the analysis produced.
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