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

Abstract


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%.


Keywords


Assessment; mobile application; machine learning

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References


Darmawan, M. S., Daeni, F., & Listiaji, P. (2020). The Use of Quizizz As An Online Assessment Application for Science Learning in The Pandemic Era. Unnes Science Education Journal, 9(3), 144-150.

Elzainy, A., El Sadik, A., & Al Abdulmonem, W. (2020). Experience of e-learning and online assessment during the COVID-19 pandemic at the College of Medicine, Qassim University. Journal of Taibah University Medical Sciences, 15(6), 456-462.

Divayana, D. G. H., Sappaile, B. I., Pujawan, I., Dibia, I. K., Artaningsih, L., Sundayana, I., & Sugiharni, G. A. D. (2017). An Evaluation of Instructional Process of Expert System Course Program by Using Mobile Technology-based CSE-UCLA Model. International Journal of Interactive Mobile Technologies, 11(6).

Hering, D., Moog, O., Sandin, L., & Verdonschot, P. F. (2004). Overview and application of the AQEM assessment system. Hydrobiologia, 516(1), 1-20.

Fee, K. (2013). Delivering e-learning. A complete strategy for design, application and assessment. Development and Learning in Organizations: An International Journal.

Alghamdi, M. I. (2020). Survey on Applications of Deep Learning and Machine Learning Techniques for Cyber Security. International Journal of Interactive Mobile Technologies, 14(16).

Jackson, C., Østerlund, C., Crowston, K., Harandi, M., Allen, S., Bahaadini, S., ... & Zevin, M. (2020). Teaching citizen scientists to categorize glitches using machine learning guided training. Computers in Human Behavior, 105, 106198.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine, 375(13), 1216.

Shakarami, A., Ghobaei-Arani, M., Masdari, M., & Hosseinzadeh, M. (2020). A survey on the computation offloading approaches in mobile edge/cloud computing environment: a stochastic-based perspective. Journal of Grid Computing, 18(4), 639-671.

Ristè, D., Da Silva, M. P., Ryan, C. A., Cross, A. W., Córcoles, A. D., Smolin, J. A., ... & Johnson, B. R. (2017). Demonstration of quantum advantage in machine learning. npj Quantum Information, 3(1), 1-5.

Wang, X. S., Ryoo, J. H. J., Bendle, N., & Kopalle, P. K. (2020). The role of machine learning analytics and metrics in retailing research. Journal of Retailing.

Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361.

Kim, H. C., Park, J. H., Kim, D. W., & Lee, J. (2020). Multilabel naïve Bayes classification considering label dependence. Pattern Recognition Letters, 136, 279-285.

Wirawan, I. M. A., & Diarsa, I. W. B. (2018). Mobile-based Recommendation System for the Tour Package Using the Hybrid Method. iJIM, 12(8).




DOI: http://dx.doi.org/10.28926/briliant.v7i2.926

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