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Submitted
16 September 2021
Accepted
30 November 2021
Published
30 November 2021
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Abstract

The Smart Indonesia Card (KIP) scholarship program is a government scholarship program through the Ministry of Religion of the Republic of Indonesia which is given to students who have a good academic level but have a weak economic level. Sultan Syarif Kasim State Islamic University, Riau accepts new students every year, but the quota for the KIP scholarship program is limited. With the limited quota for the KIP program, a system is needed that is able to classify submission data from students who register for the KIP program, so that the selection process can be carried out, quickly, precisely, and in accordance with the required quota. In this study, the K-Modes and K-Nearest Neighbor (KNN) Algorithms were used by using the achievement variables, report cards, and national exam scores when high school, father's income, parental status, and homeownership status. Reprocessing is carried out before the testing stage, testing is carried out by performing the initial stages, namely clustering using the K-Modes algorithm, then validating or testing data by applying the Grid Search Cross-Validation (GSCV) method, and finally predicting using the KNN algorithm. The test resulted in a performance value of 66.79%

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How to Cite
maspur, P., & Dadang, D. S. S. S. (2021). Using KNN Algorithms for Determining the Recipient of Smart Indonesia Scholarship Program. Jurnal Komputer Terapan, 7(2), 163–173. https://doi.org/10.35143/jkt.v7i2.4962
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References

  1. K. A. R. I. Direktorat Jenderal Pendidikan Islam, Keputusan Menteri Agama Republik Indonesia Nomor 361 Tahun 2020 tentang Pedoman Program Kartu Indonesia Pintar Kuliah pada Perguruan Tinggi Keagamaan. Indonesia, [Online]. Available: https://kemenag.go.id/archive/keputusan-menteri-agama-nomor-361- tahun-2020-tentang-pedoman-program-kartu-indonesia-pintar-kuliahpada-perguruan-tinggi-keagamaan, 2020.
  2. H. Parvin, H. Alizadeh, and B. Minaei-bidgoli, “MKNN : Modified KNearest Neighbor,†Proc. World Congr. Eng. Comput. Sci. WCECS, E-ISSN : 2798 – 4664 89 pp. 22–25, 2008.
  3. M. Govindarajan and R. Chandrasekaran, “Evaluation of k-Nearest Neighbor classifier performance for direct marketing,†Expert Syst. Appl., vol. 37, no. 1, pp. 253–258, 2010, doi: 10.1016/j.eswa.2009.04.055.
  4. K. T. Tun and A. M. Aye, “Selection of Appropriate Candidates for Scholarship Application Form using KNN Algorithm,†Int. J. Sci. Eng. Technol. Res., vol. 03, no. 06, pp. 1019–1026, 2014.
  5. B. Surarso and R. Gernowo, “Implementation of the K-Nearest Neighbor Method to determine the Classification of the Study Program Operational Budget in Higher Education,†Proceeding of ICOHETECH, pp. 201–204, 2019, [Online]. Available: http://ojs.udb.ac.id/index.php/icohetech/article/view/803.
  6. D. Kurniadi, E. Abdurachman, H. L. H. S. Warnars, and W. Suparta, “The prediction of scholarship recipients in higher education using kNearest neighbor algorithm,†IOP Conf. Ser. Mater. Sci. Eng., vol. 434, no. 1, 2018, doi: 10.1088/1757-899X/434/1/012039.
  7. H. Zhou, Y. Zhang, and Y. Liu, “A Global-Relationship Dissimilarity Measure for the k -Modes Clustering Algorithm,†Comput. Intell. Neurosci., vol. 2017, 2017, doi: 10.1155/2017/3691316.
  8. A. Chaturvedi, K. Foods, P. E. Green, and J. D. Carroll, “K-modes clustering,†Journal of Classification, vol. 18, no. 1. pp. 35–55, 2001, doi: 10.1007/s00357-001-0004-3.
  9. Y. K. JAIN and S. K. BHANDARE, “Min Max Normalization Based Data Perturbation Method for Privacy Protection,†Int. J. Comput. Commun. Technol., vol. 4, no. 4, pp. 233–238, 2013, doi: 10.47893/ijcct.2013.1201.
  10. K. Schliep, K. Hechenbichler, and A. Lizee, “Weighted k-Nearest Neighbors,†2016, vol. 399, p. 15, 2016, [Online]. Available: https://cran.r-project.org/web/packages/kknn/kknn.pdf. [11] Z. Huang, “Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values,†Data Min. Knowl. Discov., vol. 2, pp. 283–304, 1998, doi: 10.1023/A:1009769707641.