Since March 2020, due to the COVID-19 pandemic and in line with Merdeka Belajar - Kampus Merdeka, higher education institutions have conducted distance learning in asynchronous and synchronous modes, such as video meetings using Microsoft Teams and provide e-learning. In order to reach the goals and strategies of the higher education institutions, universities implement several control objectives within the COBIT 5 framework, so they can use and manage resources efficiently, provide the best education for students. This study aims to analyze the acceptance level of the COBIT implementation in higher education institutions by using the UTAUT model in E-Learning management, the use of Microsoft Teams and distance learning. This study uses a quantitative approach with a causal explanatory research design. Dissemination of the survey was conducted by simple random sampling at 6 (six) universities in Batam City. This study reveals that E-Learning management, the use of Microsoft Teams, and the application of distance learning together have a significant influence on the implementation of COBIT with an acceptance index of 85.5%, which refers to the satisfying category.
In social media, it is found that hate speech is conveyed in the form of text, images and videos, as a result it can provoke certain people to do things that are against the law and harm other person. Therefore, it is necessary to make early detection of hate speech by utilizing machine learning algorithms. This study is to analyze the level of accuracy, precision, recall and F1-Score of 3 kinds of algorithms (SVM, XGBoost, and Neural Network) in the classification of hate speech, using datasets sourced from public hate speech on Twitter in Indonesian. The results of the analysis show that the SVM algorithm has a level of accuracy (83.2%), precision (83%), recall (83%) and F1-score (83%), SVM occupies the highest level compared to XGBoost and Neural Network, so the SVM algorithm can be considered for use in hate speech classification
The culinary business is a promising business today because food and beverages are basic needs. This can be seen from the number of cafes, restaurants or restaurants in Batam City. Along with the rapid growth of the culinary business, the variety of food is becoming increasingly diverse, thus making it increasingly difficult for consumers to choose and determine the right food, both based on taste and ease of access. Therefore, it is necessary to need a system that can provide the user with the best and most suitable food or restaurant recommendations. This study designs and builds a recommendation system using K-Nearest Neighbors which functions to recommend restaurants with the closest distance, while Content-Based Filtering functions to recommend foods according to user preferences. The system is built in the form of a mobile-based application so that users can access and get more relevant recommendations
A bug is a system error that causes a mismatch between user expectations to actualization. Bugs often arise from programmatical mistakes, and a prompt resolution is mandatory so that the users’ business processes are assured. However, the author has noted a low efficiency of developers’ bug solving. After research, the cause is the lack of knowledge management, especially in bug tracking. This has resulted in the repetition of the error. The developers often made the same mistake that the others had encountered. The above has moved the author to build a bug tracking system (BTS) that can enhance the efficiency of bug solving. The author has developed the application using Django, MySQL, and Scrum. Scrum’s agile approaches enable dynamic and rapid development. In addition, BTS utilized a content-based filtering algorithm known as Cosine Similarity. This study results in the implementation of BTS with comprehensive capabilities. Users can manage bugs and compare them to others to find potential duplication into minimizing data redundancy. The author has implemented the BTS towards groups of developers. Through a user acceptance survey, the author finds 78.72% of system objectives achieved. The users find improvement in the quality and efficiency of their bug solving
Preloved goods are used goods that are still of good quality. When cleaning the house sometimes found a number of Preloved items, where these items are only used once or not used at all and stored for quite a long time at home. Used goods that accumulate in the house make the room in the house even narrower. Therefore, Preloved items are more profitable if they are sold rather that being thrown away or stored. Besides that, selling Preloved items can get extra money and help in environmental preservation. So the writer got an idea how to use a website as a marketplace to sell Preloved goods in Batam city, namely the Batam Preloved Shop. Batam residents get information on Preloved items they want to buy through this marketplace. The method used in designing and developing the Batam Preloved Shop is Agile. Next, test the system by testing the Black Box and System Usability Scale (SUS). The results of the Black Box test show that the Batam Preloved Shop system is running as expected and an analysis of user satisfaction using the System Usability Scale (SUS) obtains an average value of 81, so it can be concluded that the Batam Preloved Shop system has been running well according to its functions and uses
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