De-i-fuzzification is a process of converting the intuitionistic fuzzy set into a fuzzy set. It becomes one of the core procedures in fuzzy time series forecasting model based on the intuitionistic fuzzy set. In this paper, we propose a fuzzy time series forecasting model based on intuitionistic fuzzy set via de-i-fuzzification. The de-i-fuzzification approach used is assigning the hesitancy degree to the major grade. The data are partitioned into a few intervals using the frequency density-based method. The data in the fuzzy set form is then transformed into an intuitionistic fuzzy set using the definition of intuitionistic fuzzy set. The arithmetic rules based on centroid defuzzification is used to obtain the forecasted output. The model is implemented on the data of student enrolment at the University of Alabama. The results are then compared to forecasting method using classical fuzzy set and similar de-i-fuzzification approach using max-min operation. The proposed method outperforms the other two methods, thus supports the fact that intuitionistic fuzzy set is a generalization of a classical fuzzy set and gives better performance in forecasting.
Due to the COVID-19 pandemic, the enforcement of the Movement Control Order (MCO) by theMalaysian government since March 2020 significantly impacted many sectors such as the economy, society, and others. MCO enforcement has made Malaysians spend most of their time staying at home, and even some have lost their income source. Another sector that has been greatly affected is the educational sector. Today’s landscape of education has changed dramatically with the phenomenal rise of virtual classes from home. Learning and teaching processes are undertaken remotely and on digital platforms to curb the spreading of the virus. This situation has affected the lesson and learning process from home to many of the several students in Malaysia. Therefore, this study investigates the challenges of home learning during MCO among students in the Universiti Teknologi MARA (UiTM) Pahang Branch. A simple random sampling technique was used to distribute the online survey questionnaires, involving a sample of 213 students. Besides, a descriptive statistic was used to study the students’ demographic characteristics according to the challenges. In contrast, logistic regression analysis was used to determine the factors associated with home learning challenges during MCO. Based on the findings, most male and female students were not well prepared for home learning during MCO, with a percentage of 71.60% and 69.70%, respectively. As a result, 79.81% agreed that home learning is more stressful than the physical classes on the campus. In comparison, 79.63% of Social Science and 83.02% of Science and Technology students claimed that the workload given is way more significant during online classes. Furthermore, this study concludes that the most associated challenges of home learning faced by the students during MCO are the abundance of workload and loss of interest in the subject.
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