Pendeteksi Kesesuaian Format Laporan Skripsi dengan Panduan Penulisan menggunakan Machine Learning

Eko Travada Suprapto Putro Eko Travada

Abstract


Machine Learning is a method in artificial intelligence system that is able to model the data entered for future needs. Many applications are applied such as classifying data, predicting relationships between data, ranking data, reading data patterns, making movie thrillers and many other implementations. In this paper, we discuss the use of machine learning analysis result to detect the student's final project format whether it is in accordance with the final project guidelines or not. Based on research, it shows that the structural modeling of machine learning can be used to detect the suitability of the format both modeling separately and modeling in combination.

Key Word : Text Structure, Machine Learning, Final Project Documentation

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References


• Hoos, H. H. (2017) ‘Machine Learning – Opportunities and Limitations’.

• Hu, {yuening et al. (2014) ‘Interactive Topic Modeling’, pp. 0–56. Available at: http://cs.colorado.edu/.

• Noesgaard, S. S. and Ørngreen, R. (2015) ‘The effectiveness of e-learning: An explorative and integrative review of the definitions, methodologies and factors that promote e-Learning effectiveness’, Electronic Journal of e-Learning, 13(4), pp. 278–290.

• Pojon, M. (2017) ‘Using Machine Learning to Predict Student Performance’, India, (June).

• Rossi, L. A. and Gnawali, O. (2014) ‘Language independent analysis and classification of discussion threads in Coursera MOOC forums’, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, IEEE IRI 2014, pp. 654–661. doi: 10.1109/IRI.2014.7051952.

• Sebastiani, F. (2002) ‘Machine Learning in Automated Text Categorization’, 34(1), pp. 1–47.

• Wallach, H. M. (2008) ‘Structured Topic Models for Language’, Doctor, (2001), p. 136. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.149.2537&rep=rep1&type=pdf.

• Wuillemin, P.-H. and Torti, L. (2012) ‘Structured probabilistic inference’, International Journal of Approximate Reasoning. Elsevier, 53(7), pp. 946–968. doi: 10.1016/J.IJAR.2012.04.004.

• Zinman, A. et al. (2006) ‘Probabilistic Topic Models’, MIS Quarterly, 3(3), pp. 993–1022. doi: 10.1016/s0364-0213(01)00040-4.




DOI: https://doi.org/10.25134/nuansa.v13i1.1642

NUANSA INFORMATIKA : JURNAL TEKNOLOGY DAN INFORMASI
p-ISSN :1858-3911 , e-ISSN : 2614-5405
DOI : https://doi.org/10.25134/nuansa
Accreditation : SINTA 5

Organized by Faculty of Computer Science, Universitas Kuningan, Indonesia.
Website : https://journal.uniku.ac.id/index.php/ilkom
Email : [email protected]
Address : Jalan Cut Nyak Dhien No.36A Kuningan, Jawa Barat, Indonesia.

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