Assessment of Students in Online Industrial Practice Activities Using Machine Learning Based on Mobile Application

Sunarti Sunarti, Hie Ling Ting, Tiksno Widyatmoko, Herri Akhmad Bukhori


All of the learning in the pandemic era uses online learning including practical in the industry that should do all of the students to apply their knowledge.  The practical industry online is very difficult to assement students that the assessment is given from both company and the university. Companies have many parameters to assessment and each company has different parameters. This study uses 14 parameters that are generally used in assessment for practical students and the university side using 10 parameters. The problem is that every parameter has a different weight than it makes it confusing to give marks with manual assessment. This research uses machine learning to fix this problem based on mobile applications for the user interfaces. The result of testing this application had an average accuracy for assessment students based on parameters of companies and universities that is 83,3%.


Assessment; mobile application; machine learning

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