's spread has altered the learning process at all educational levels, resulting in the proliferation of virtual classrooms throughout the world. The purpose of this study is to determine lecturer satisfaction with Microsoft Teams' use in online learning during the COVID-19 pandemic. This way, we can implement the necessary improvements to ensure that students and lecturers are satisfied with their use of Microsoft Teams. Thirty-four lecturers from UiTM Cawangan Negeri Sembilan (UiTMNCS) participated in the study. Five criteria were used consisting of: Basic Microsoft Teams Functions, Discussion, Assessments, Features, and Attendance Form. Data were collected via questionnaires and then distributed to respondents via Google forms. All calculations were performed using the SPSS Statistics 26 software. The findings indicated that Basic Microsoft Teams Functions is the most effective criterion, while Assessments and Attendance Form is the least effective. The sub-criteria rating with the highest score is Creating a TEAM is simple and straightforward, and the lowest score indicates that Microsoft Teams works well even with slow internet. Chi-square test for independent variable shows there is no relationship between gender, residential area, faculties and teaching experience of UiTMCNS lecturers with sub criteria functions in Basic Functions of Microsoft Teams, Discussion, Assessments, Features and Attendance Form. Further research will be proposed to improve the attendance system by integrating it with the Microsoft team's attendance sheet.

Dual hesitant fuzzy set (DHFS) consists of two parts: membership hesitant function and non-membership hesitant function. This set supports more exemplary and flexible access to set degrees for each element in the domain and can address two types of hesitant in this situation. It can be considered a powerful tool for expressing uncertain information in the decision-making process. The function of z-score, namely z-arithmetic mean, z-geometric mean, and z-harmonic mean, has been proposed with five important bases, these bases are hesitant degree for dual hesitant fuzzy element (DHFE), DHFE deviation degree, parameter , (the importance of the hesitant degree), parameter  , (the importance of the deviation degree) and parameter , (the importance of membership (positive view) or non-membership (negative view). A comparison of the z-score with the existing score function was made to show some of their drawbacks. Next, the z-score function is then applied to solve multi-criteria decision making (MCDM) problems. To illustrate the proposed method's effectiveness, an example of MCDM specifically in pattern recognition has been shown.