Abstract
Consideration of scholarships is a common occurrence in educational institutions such as in a university. The scholarship selection committees play an essential role in judgment, which must pay attention to considering issues efficiently. However, they may make mistakes because an applicant’s information is complicated. This research proposes a scholarship analytic for the award of a student scholarship at university by using Data Mining techniques. The study was designed with seven variables on 468 samples, which were only selected with complete attributes from 2,549 student documents by a decision tree, J48 and J48graft algorithm with percentage split method at 20%, 30%, and 60%, k-fold cross validation both 5-folds and 10-folds. The development model’s results found that the model created by a decision tree with the J48 algorithm and percentage split method at 66% is most effective, with the precision value at 77.35%. Therefore, we choose to model with the J48 algorithm by percentage split method at 66% to develop the web application, which is useful for students to assess themselves before applying and will decrease the committee’s workload for the assessment of student’s scholarship applications.
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References
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Netherlands (2011)
Hand, D.J.J.D.s.: Principles of data mining. 30(7), 621–622 (2007)
Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Education India, London (2016)
Ge, Z., Song, Z., Ding, S.X., Huang, B.: Data mining and analytics in the process industry: the role of machine learning. IEEE Access 5, 20590–20616 (2017)
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., Chouvarda, I.: Machine learning and data mining methods in diabetes research. Comput. Struct. Biotechnol. J. 15, 104–116 (2017)
Noor, N.L.M., et al.: Predictive analytics: the application of J48 algorithm on grocery data to predict obesity. In: 2018 IEEE Conference on Big Data and Analytics (ICBDA). IEEE (2018)
Harth, N., Anagnostopoulos, C.: Quality-aware aggregation & predictive analytics at the edge. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 17–26. IEEE (2017)
Gunasekaran, A., et al.: Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res. 70, 308–317 (2017)
Hanasaki, N., et al.: A quasi-real-time hydrological simulation of the Chao Phraya River using meteorological data from the Thai Meteorological Department Automatic Weather Stations. Hydrol. Res. Lett. 8(1), 9–14 (2014)
Roiger, R.J.: Data Mining: A Tutorial-based Primer. CRC Press, United States (2017)
Ivezić, Ž., Connolly, A.J., VanderPlas, J.T., Gray, A.: Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data. Princeton University Press, New Jersey (2019)
Fayyad, U.: Knowledge discovery in databases: an overview. In: Lavrač, N., Džeroski, S. (eds.) ILP 1997. LNCS, vol. 1297, pp. 1–16. Springer, Heidelberg (1997). https://doi.org/10.1007/3540635149_30
Thomas, J., Princy, R.T.: Human heart disease prediction system using data mining techniques. In: 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE (2016)
Agaoglu, M.: Predicting instructor performance using data mining techniques in higher education. IEEE Access 4, 2379–2387 (2016)
Baepler, P., Murdoch, C.J.: Academic analytics and data mining in higher education. Int. J. Sch. Teach. Learn. 4(2), 1–9 (2010)
Bhadani, S., Verma, I., Dey, L.: Mining financial risk events from news and assessing their impact on stocks. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds.) MIDAS 2019. LNCS (LNAI), vol. 11985, pp. 85–100. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37720-5_7
Priyam, A., Guptaa, R., Ratheeb, A., Srivastavab, S.: Comparative analysis of decision tree classification algorithms. Int. J. Curr. Eng. Technol. ISSN 3(2), 334–337 (2013)
Saritas, M.M., Yasar, A.: Performance analysis of ANN and Naive Bayes classification algorithm for data classification. Int. J. Intell. Syst. Appl. Eng. 7(2), 88–91 (2019)
Sugiyarti, E., Jasmi, K.A., Basiron, B., Huda, M., Shankar, K., Maseleno, A.: Decision support system of scholarship grantee selection using data mining. Int. J. Pure Appl. Math. 119(15), 2239–2249 (2018)
Wang, X., Zhou, C., Xu, X.: Application of C4. 5 decision tree for scholarship evaluations. Procedia Comput. Sci. 151, 179–184 (2019)
Saravanan, N., Gayathri, V.: Classification of dengue dataset using J48 algorithm and ant colony based AJ48 algorithm. In: 2017 International Conference on Inventive Computing and Informatics (ICICI). IEEE (2017)
Perveen, S., Shahbaz, M., Guergachi, A., Keshavjee, K.: Performance analysis of data mining classification techniques to predict diabetes. Procedia Comput. Sci. 82, 115–121 (2016)
Pham, B.T., Bui, D.T., Prakash, I.: Landslide susceptibility assessment using bagging ensemble based alternating decision trees, logistic regression and J48 decision trees methods: a comparative study. Geotech. Geol. Eng. 35(6), 2597–2611 (2017)
Meena, G., Choudhary, R.R.: A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA. In: 2017 International Conference on Computer, Communications and Electronics (Comptelix). IEEE (2017)
Russell, I., Markov, Z.: An introduction to the Weka data mining system. In: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (2017)
Sultana, M., Haider, A., Uddin, M.S.: Analysis of data mining techniques for heart disease prediction. In: 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT). IEEE (2016)
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Khruahong, S., Tadkerd, P. (2020). Analysis of Scholarship Consideration Using J48 Decision Tree Algorithm for Data Mining. In: Luo, Y. (eds) Cooperative Design, Visualization, and Engineering. CDVE 2020. Lecture Notes in Computer Science(), vol 12341. Springer, Cham. https://doi.org/10.1007/978-3-030-60816-3_26
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