Analyzing Universities Service Quality to Student Satisfaction; Academic and Non-Academic Analyses
Bibliographic record
Abstract
Indonesia has many higher learning institutions both public and private sectors such as colleges, polytechnics, institutes and universities and they are competing among them to get students to enroll in their institutions. It has become competitive among them to get students than before. The growing competition among higher learning institutions had forced them to strive to improve their service quality provided to students. The student satisfaction of service quality can be divided into two parts, namely satisfaction in academic and non-academic. The purpose of this research is to determine whether the academic and non-academic service quality affect student satisfaction of Economics Faculty Universitas Negeri Semarang. This research used an exploratory method that explaining the relationship between hypothesis testing, making prediction and getting the implicit meaning of problems that want to be solved. This study was conducted at the Economics Faculty of Universitas Negeri Semarang. The data analysis used SEM PLS. The population in this study were students from the Economics Faculty of Universitas Negeri Semarang who registered in 2015 and graduated in 2018. The total number of population in this study were 3,596 students majoring in Economics Education, Accounting, Management, and Economic Development. This study used a stratified sampling technique where students from all disciplines and levels were determined using the Slovin formula. Questionnaires were distributed to a sample of 360 students and were administered by trained enumerators. Data were collected using self-administered assessment questionnaires of a five Likert scale and analyzed using SEM PLS 6.0 Warp PLS. The results of this research were, first, academic service quality did not influence student satisfaction. Second, the non-academic service quality has a positive and significant influence on student satisfaction. This is because the supported learning infrastructure was found to be a factor that satisfied the students compared to teaching methods that was carried out by faculty members. It was also found that attitude and behavior in academic aspect were not significant in improving the students’ satisfaction. Therefore, it is suggested that Faculty of Economics of UNNES should focus on maintaining and improving the service quality of non-academic aspects in order to compete with other higher learning institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".