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Multi-label classification is a technique used for mapping data from single labels to multiple labels. These multiple labels stand part of the same label set comprising inconsistent labels. The objective of multi-label classification is to create a classification model for previously unidentified samples. The accuracy of multi-label classification based on machine learning algorithms has been a particular study and discussion topic for researchers. This research aims to present a systematic literature review on multi-label classification based on machine learning algorithms. This study also discusses machine learning algorithm techniques and methods for multi-label classification. The findings would help researchers to explore and find the best accuracy of multi-label classification. The review result considered the Support Vector Machine (SVM) as the most accurate and appropriate machine learning algorithm in multi-label classification. [ABSTRACT FROM AUTHOR]

Students' motivation is an important factor in ensuring the success of e-learning implementation. In order to ensure students is motivated to use e-learning, motivational design has been used during the development process of e-learning applications. The use of gamification in learning context can help to increase student motivation. The ARCS+G model of motivational design is used as a guide for the gamification of learning. This study focuses on the influence of gamification on students' motivation in using elearning applications based on the ARCS+G model. Data from the Instructional Materials Motivation Scale (IMMS) questionnaire, were gathered and analyzed for comparison of two groups (one control and one experimental) in attention, relevance, confidence, and satisfaction categories. Based on the result of analysis, students from the experimental group are more motivated to use e-learning applications compared with the controlled group. This proves that gamification affect students' motivation when used in elearning applications. [ABSTRACT FROM AUTHOR]