Network formed between users in a social media can be used to encourage information spreading among them. This research applied Social Network Analysis which further can be used to social media marketing to improve the marketing process effectively. Based on previous research, information spreading speed among the social media is affected by the users' activity connection which can be represented in centrality value. The centrality value itself is very affected by the graph structure and weights. This research applied degree and eigenvector centrality to observe the effect of centrality value for twitter data. The result shows that there is significant difference among 10 most influential users. This result will be used for the future research that will be focused in small and medium enterprise (SME) twitter data.

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, departments, etc. This problem becomes complicated for universities which have immense number of students and lecturers. Therefore, a scalable and reliable timetabling solver is needed. However, current solvers and generic solution failed to meet several specific UCTP. Moreover, some universities implement student sectioning problem with individual student specific constraints. This research introduces the Reinforced Asynchronous Island Model Genetic Algorithm (RIMGA) to optimize the resource usage of the computer. RIMGA will configure the slave that has completed its process to helping other machines that have yet to complete theirs. This research shows that RIMGA not only improves time performance in the computational execution process, it also offers greater opportunity to escape the local optimum trap than previous model.

In this paper we study how to determine the nodes that most influential to a node in the network. Social Network Analysis (SNA) can measure the centrality of a node in order to obtain an influential nodes in the dissemination of information. One of the centrality measurement that can be applied is degree centrality. In this research, the method used is Opsahl method, combines two indicators, the number of neighborhood (degree) and the amount of weight relations (strength) of a node and uses tuning parameters. The weight relations are obtained from the number of relations as following/follower, mention and reply. Tuning parameters are parameters which are used to set the influence of both degree and strength to the degree centrality measurement results. Based on test results, the node who has a high strength value is derived from weight relations which are obtained from mentions and replies.

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning a teaching event in a certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. This problem becomes complicated for universities with a large number of students and lecturers. Moreover, several universities are implementing student sectioning, which is a problem of assigning students to classes of a subject while respecting individual student requests, along with additional constraints. Such implementation also implies the complexity of constraints, which is larger accordingly. However, current and generic solvers have failed to meet the scalability and reliability requirements for student sectioning UCTP. In this paper, we introduce the localized island model genetic algorithm with dual dynamic migration policy (DM-LIMGA) to solve student sectioning UCTP. Our research shows that DM-LIMGA can produce a feasible timetable for the student sectioning problem and get better results than previous works and the current UCTP solver. Our proposed solution also consistently yield lower violation number than other algorithms, as evidenced by UCTP benchmark experiment results.

Social Network Analysis (SNA) has been applied in several case studies. SNA is applied to enhance the company's marketing strategy as well as small and medium businesses. This research proposes a collaborative model using content-based and userbased approach, with the centrality measurement methods. Content-based approach tends to focus on tweet content analysis of the existing nodes in a network, while the user-based approach focuses on the connections between users in the network twitter.The model will combine the advantages of collaborative content-based and user-based approach, to find the most influential people in a twitter network to make the dissemination of information more effectively and efficiently.