Information technology governance is an integrated part of the management of organizations that govern leadership, the organization of process structure arrangements that govern information technology is used as optimally as possible. Work processes and relationships between the office community, researchers and various levels of interests and stakeholders. COBIT 5 is a company that provides services to companies, both companies, organizations, and management in managing and managing assets or IT resources to achieve company goals. It human resource in West Lombok is currently not well managed, Which hes not yet fulfilled the need for IT human resources in the office of West Lombok To solve this problem the authors carry out ITgovernance using the framework COBIT 5 On DISKOMINFO west Lombok. On DISKOMINFO West Lombok. This study will provide recommendation for improvement of IT problems the exist in diskominfo West Lombok and cand be used as a reference, especially in the managed of IT human resources and can improve the performance of DISKOMINFO west Lombok. On DSIKOMINFO West Lombok using to IT processes APO01 (management framework IT) and APO07 (human resouces). The result of this analysis are the capability level of IT governace that reflects the condition of IT governance in DISKOMINFO West Lombok. With refenace to the capability level provided by the cobit 5 framework, namely from level 1 (performed) to 5 (optimising). Key words: information technology, DSIKOMINFO COBIT 5
In order to prepare students to face the rapid development of technology, changes in work life and skills, students must be better prepared to face the progress of the times. Universities must be able to carry out innovative learning processes so that students achieve optimal learning outcomes which include aspects of knowledge, skills and attitudes. So the MBKM program was launched to answer these demands. However, MBKM has pros and cons in its implementation, so it is necessary to analyze and evaluate policies to improve performance through feedback from the public by conducting sentiment analysis of MBKM policies on twitter users from 2019 to 2022 with the hashtag #kampusmerdeka. This study used the Naïve Bayes and SVM algorithms to determine accuracy based on sentiment classification. The data used 1118 data with positive sentiment 618 data and negative sentiment 500 data. This study resulted in an accuracy of 86%, precision of 87% and recall of 80% with testing data using the Naïve Bayes algorithm. Then using the linear kernel SVM algorithm with the same testing data resulted in accuracy of 93%, precision of 100% and recall of 84%. Therefore, it is important to conduct studies to improve the MBKM program so that its implementation is clearly in accordance with existing procedures.
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