Implementation of the K-Nearest Neighbor Method to determine the Classification of the Study Program Operational Budget in Higher Education


Operational Finance
K-Nearest Neighbor
Study Program.


Sultan Agung Islamic University annually designs operational costs for work programs in the study program and of course determines the amount of financial budget for the study program work program. K-Nearest Neighbor algorithm is needed to determine the required operational budget classification based on the number of active students, the number of financial admissions, the number of employees and the percentage of work program realization programs. The results of this study are to facilitate the leadership of higher education in the budget field to classify the amount of the budget required by study programs in the classification of up, or fixed. The purpose of this study is expected to facilitate the leadership of the financial budget department to classify the budget needed by the study program and as an awareness system in the work program of the study program with a classification value of 79.96% for the operational budget of the college study program.



I. G. P. Pundarika and A. A. N. . Dwirandra, “The Effect of Budget Participation on Budgetary Slack with Organizational Commitments and Love of Money as Moderation,” Int. J. Sci. Res., vol. 8, no. 2, pp. 491–496, 2019.

X. Peng, Y. Cai, Q. Li, and K. Wang, “Control rod position reconstruction based on K-Nearest Neighbor Method,” Ann. Nucl. Energy, vol. 102, pp. 231–235, 2017.

T. Sathish, S. Rangarajan, A. Muthuram, and R. P. Kumar, “Analysis and modelling of dissimilar materials welding based on K-nearest neighbour predictor,” Mater. Today Proc., no. xxxx, 2019.

A. Swetapadma and A. Yadav, “A novel single-ended fault location scheme for parallel transmission lines using k-nearest neighbor algorithm,” Comput. Electr. Eng., vol. 69, no. April 2017, pp. 41–53, 2018.

Z. Zhang, T. Jiang, S. Li, and Y. Yang, “Automated feature learning for nonlinear process monitoring – An approach using stacked denoising autoencoder and k-nearest neighbor rule,” J. Process Control, vol. 64, pp. 49–61, 2018.

E. Turban, J. E. Aronson, and T. Liang, “Decision Support Systems and.”

S. M. Ayyad, A. I. Saleh, and L. M. Labib, “Gene expression cancer classification using modified K-Nearest Neighbors technique,” BioSystems, vol. 176, no. December 2018, pp. 41–51, 2019.

J. Han, M. Kamber, and Jian Pei, Data Mining Concepts and Techniques. 2012.

C. di M. Vercellis, Business Intelligence: Data Mining and Optimization for Decision Making, First. Italy, 2009.

A. Muhammad, M. S. Mazliham, P. Boursier, and M. Shahrulniza, “K-Nearest Neighbor Algorithm for Improving Accuracy in Clutter Based Location Estimation of Wireless Nodes,” Malaysian J. Comput. Sci., vol. 24, no. 3, pp. 146–159, 2011.

M. of C. Bramer, Principles of Data Mining, Second. London, 2013.

C. H. Cheng, C. P. Chan, and Y. J. Sheu, “A novel purity-based k nearest neighbors imputation method and its application in financial distress prediction,” Eng. Appl. Artif. Intell., vol. 81, no. March, pp. 283–299, 2019.