Open AccessANALISIS PENERIMAAN PENGGUNA APLIKASI SIAKAD DESKTOP UNIVERSITAS BRAWIJAYA MENGGUNAKAN UTAUT
Author(s) -
Yusi Tyroni Mursityo,
Mochamad Chandra Saputra,
Admaja Dwi Herlambang
Publication year - 2019
Publication title -
joutica
Language(s) - English
Resource type - Journals
eISSN - 2621-511X
pISSN - 2503-071X
DOI - 10.30736/jti.v4i1.280
Subject(s) - construct (python library) , computer science , service (business) , variance (accounting) , heuristic , artificial intelligence , marketing , business , programming language , accounting
Education services from tertiary institutions have applied a lot of the Academic Information System (SIAKAD). In the research on heuristic evaluation in the Academic Information System of Brawijaya University, there were several problems related to users, including problems in the ease of users to interact or use applications, SIAKAD Desktop which did not provide information to the user about the condition of a process within a certain time period or time. On the other hand, universities must guarantee the best service. Therefore, it is necessary to evaluate the acceptance of SIAKAD Desktop users to find out what factors can affect the use of Desktop SIAKAD. UTAUT as a theoretical model will be used to explore the acceptance and use of the technological environment, especially for SIAKAD users in the academic section of the Faculty of Computer Science, Universitas Brawijaya. The data that has been collected will then be analyzed using the Partial Least Square Sequence Equation Modeling (PLS SEM) method. The results of the analysis show that only the conditions of the facilities significantly influence the behavior of using SIAKAD Desktop. The UTAUT model can only explain 44.8% of the factor variants using SIAKAD Desktop which is included in the moderate category. Therefore, it is necessary to develop a research model construct construct so that the prediction of the variance accuracy of the research model increases. In addition, it is expected that further research can cover a wider population target so that data is more reliable.