TY - GEN
T1 - A Hybrid Evaluation Index Approach in Optimizing Single Tuition Fee Cluster Validity
AU - Yustanti, Wiyli
AU - Iriawan, Nur
AU - Irhamah,
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The grouping of the socio-economic level of new students at the time of registration at public universities is a problem faced by all state universities. Identifying the right group will have an impact on the students and the university. The quality of the results of a valid grouping will give a sense of fairness to the parents of students in paying tuition fees. On the other hand, the university also expects that the results of a valid grouping will contribute to optimal revenue. This study aims to evaluate the cluster structure of a single tuition fee at the State University of Surabaya. The existing cluster structure is compared with the results of grouping using nine clustering methods, namely K-Mean, Hierarchical, BIRCH, DBSCAN, Mini Batch K-Mean, Mean Shift, OPTICS, Spectral Clustering, and Mixture Gaussian. The proposed evaluation method is a combination of three evaluation concepts, namely internal validity (Silhouette-Index), external validity (Rand Index), and the percentage conformity value to the expected income factor (Revenue-Index). These three indicators are then calculated as the average value for each clustering method as Hybrid-Index. The highest Hybrid-Index is shown by the Mini Batch K-Mean algorithm, with an average value of 0.6420, so the Mini Batch K-Mean algorithm can be recommended as a method for grouping single tuition fees.
AB - The grouping of the socio-economic level of new students at the time of registration at public universities is a problem faced by all state universities. Identifying the right group will have an impact on the students and the university. The quality of the results of a valid grouping will give a sense of fairness to the parents of students in paying tuition fees. On the other hand, the university also expects that the results of a valid grouping will contribute to optimal revenue. This study aims to evaluate the cluster structure of a single tuition fee at the State University of Surabaya. The existing cluster structure is compared with the results of grouping using nine clustering methods, namely K-Mean, Hierarchical, BIRCH, DBSCAN, Mini Batch K-Mean, Mean Shift, OPTICS, Spectral Clustering, and Mixture Gaussian. The proposed evaluation method is a combination of three evaluation concepts, namely internal validity (Silhouette-Index), external validity (Rand Index), and the percentage conformity value to the expected income factor (Revenue-Index). These three indicators are then calculated as the average value for each clustering method as Hybrid-Index. The highest Hybrid-Index is shown by the Mini Batch K-Mean algorithm, with an average value of 0.6420, so the Mini Batch K-Mean algorithm can be recommended as a method for grouping single tuition fees.
KW - clustering
KW - clustering validity
KW - hybrid evaluation
KW - rand index
KW - silhouette index
UR - http://www.scopus.com/inward/record.url?scp=85150427043&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE57756.2022.10057653
DO - 10.1109/ICITISEE57756.2022.10057653
M3 - Conference contribution
AN - SCOPUS:85150427043
T3 - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
SP - 154
EP - 159
BT - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Y2 - 13 December 2022 through 14 December 2022
ER -
