Analisis Pengukuran Tingkat Kepuasan Pengguna Aplikasi Helpdesk Menggunakan Metode End User Computing Satisfaction (EUCS) pada UIN Raden Fatah Palembang - Aswadi, M.; Sutabri, Tata (Penerbit Goodwood, 2023)
Purpose: This research aims to assess the satisfaction level of students with the online helpdesk application at UIN Raden Fatah Palembang, employing the End User Computing Satisfaction (EUCS) approach. The study involves the collection of data through questionnaires distributed to 100 users of the Helpdesk UIN Raden Fatah Palembang website. Research Methodology: Data analysis is conducted using the IBM software SPSS Statistica 26, involving initial stages of validity testing, reliability testing, and multiple linear regression. Five variables are explored in the research: content, accuracy, format, ease of use, and timeliness. A total of five hypotheses are tested, each associated with a specific variable. Results: The analysis reveals that out of the five hypotheses tested, two are considered insignificant as the t-values are smaller than the critical t-values. The t-values for content (X1) and timeliness (X5) are 1.105 and 1.888, respectively, leading to the conclusion that these two variables are not significant. From the multiple linear regression results, the independent variables (content, accuracy, format, ease of use, and timeliness) collectively influence the dependent variable, attachment, by 91.7%, while the remaining 8.3% is influenced by other unexplored factors in this study. Limitation: It is important to acknowledge the limitations of this study. The research is confined to the specific context of UIN Raden Fatah Palembang and may not be universally applicable. Additionally, the study does not delve into certain factors that could contribute to user satisfaction. Contribution: The findings contribute insights into factors influencing user satisfaction with the helpdesk application. In conclusion, the research employs rigorous testing procedures, including validity and reliability tests, and utilizes multiple linear regression for hypothesis testing.