Learning activities during the Covid-19 pandemic were carried out with an online system even though in reality many institutions had not prepared their systems and infrastructure properly. Some e-learning media that are generally used based on survey results include 53.81% google classrooms combined with other applications that are not integrated with the institution's Learning Management System. This condition provides research opportunities to evaluate the effectiveness of online learning, especially how students are motivated to learn the method, where the results can be used as a reference in developing and refining the method. Based on many studies, that the gamification model can increase individual motivation in carrying out activities, this study uses a gamification octalysis framework to analyze the extent of the role of gamification in the learning process and measure the amount of student motivation in online learning activities. The evaluation results show that the conclusion of the Likert scale results in a "High" level, while the highest score is "Very High". As for the octalysis test scale, the average score of 6.5 on a scale of 1 to 10. The conclusion from the results of this evaluation is that the motivation to learn e-learning during the Covid-19 period is quite high and has the potential to be developed. While the results of the Octalysis framework with 8 core drives are still average, for that we need innovation in E-learning which aims to increase student motivation based on Octalysis's 8 core drives. The results of this study recommend that gamification is needed to increase student learning motivation in order to improve learning outcomes.

Challenge in developing a collaborative filtering (CF)-based recommendation system is the problem of coldstarting of items that causes the data to sparse and reduces the accuracy of the recommendations. Therefore, to produce high accuracy a match is needed between the types of data and the approach used. Two approaches in CF include user-based and item-based CFs, both of which can process two types of data; implicit and explicit data. This work aims to find a combination of approaches and data types that produce high accuracy. Cosine-similarity is used to measure the similarity between users and also between items. Mean Absolute Error is also measured to discover the accuracy of a recommendation. Testing of three groups of data based on sparseness results in the best accuracy in an explicit data-based approach that has the smallest MAE value. The result is that the average MAE value for user based (implicit data) is 0.1032, user based (explicit data) is 0.2320, item based (implicit data) is 0.3495, and item based (explicit data) is 0.0926. The best accuracy is in the item-based (explicit-data) approach which is the smallest average MAE value.

This In this paper, we introduce a concept of granular fuzzy rule-based system, offer a motivation behind its emergence and elaborate on ensuing algorithm developments. It is shown that the granularity of the fuzzy rules is directly associated with a reduction (compression) process in which the number of rules becomes reduced in order to enhance the readability (transparency) of the resulting rule base. The retained rules are made more abstract (general) by admitting a granular form of the fuzzy sets forming their antecedents. In other words, while the original rules read as "if A i then B i " their reduced subset comes in the form "if G(A i ) the B i " with G(.) denoting a certain granular extension of the original fuzzy set (which can be realized e.g. in the form of interval fuzzy sets, fuzzy sets of type-2 or rough fuzzy-sets). It is shown that the optimization of the reduced set of rules is realized through an optimal distribution of information granularity among fuzzy sets forming the conditions of the reduced rules. In particular, it is shown that the distribution of information granularity, being regarded as an important design asset, is realized through a minimization of a certain objective function quantifying how well the granular fuzzy set formed by reduced rules set represents (covers) all rules. In this study, we use a technique of particle swarm optimization (PSO) as a vehicle of forming a subset of rules and the optimal allocation of information granulation to construct a granular fuzzy rule-based system. In the sequel, we introduce and idea of a granular representation of results of inferences realized in fuzzy rule-based system.

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