Multimedia authoring is the process of assembling various types of media content such as audio, video, text, images, and animation into a multimedia presentation using tools. Multimedia Authoring Tool is a useful tool that helps authors to create multimedia presentations. Multimedia presentations are very widely used in various fields, such as broadcast digital information delivery, digital visual communication in smart cars, and others. The Multimedia Authoring tool attributes are the factors that determine the quality of a multimedia authoring tool. A multimedia authoring tool needs to have several attributes so that these tools can be used properly. The purpose of this literature review study is to find the advantages of the multimedia authoring tool attribute in each of the existing studies to produce knowledge on how to create a good quality multimedia authoring tool. These attributes are Editing, Services, Performance, and the Formal Verification Model. Editing attribute is an attribute for interfacing with the author. Followed by Service attribute and performance attribute to check and achieve proper multimedia documents. Since 1998, a multimedia modeling tool has been studied, and up to now, there have been many studies that have focused on one or more of these attributes. This article discusses the existing studies to examine the attributes generated from the studies. Multimedia authoring attributes are very important to study because they are the benchmarks of the software requirement specifications of Multimedia Authoring tools. The use of the Petri net model, the Hoare Logic, and the Simple Interactive Multimedia Model as a formal verification model can improve the performance of the Multimedia Authoring Tool. In the questionnaire that was submitted to the users, it was assessed positively by the users with the improvements in the Multimedia Authoring Tool.

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A system capable of automatically grading short answers is a very useful tool. The system can be created using machine learning algorithms. In this study, a machine system using BERT is proposed. BERT is an open-source system that is set to English by default. The use of languages other than English Language is a challenge to be implemented in BERT. This study proposes a novel system to implement Indonesian Language in the BERT system for automatic grading of short answers. The experimental results were measured using two measuring instruments: Cohen's Kappa coefficient and the Confusion Matrix. The result of measuring the BERT output of the implemented system has a Cohen Kappa coefficient of 0.75, a precision of 0.94, a recall of 0.96, a Specificity of 0.76 and an F1 Score of 0.95. Based on the measurement results, it can be seen that the implementation of the automatic short answer grading system in Indonesian Language using BERT machine learning has been successful.