Implementation of Generate and Test Algorithm for Junior High School Zoning System in Malang

Ratnasar, Novia; Mawarni, Inggar Tri Agustin; Handayani, Anik Nur; Wibawa, Aji Prasetya; Hammad, Jehad; Wibowotomo, Budi; Sugandi, Machmud

doi:10.1109/icovet50258.2020.9230207

Cited by 4 publications

(1 citation statement)

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“…Research that has relevance in the school zoning system, among others, proposes a generate and test algorithm to provide accommodation for the closest, best, and most accurate suitability with the design of a system performance model without involving the help of floor plan software. The test results from this study use the haversine formula calculations and utilize Google Maps to predict distance measurements [22]. Subsequent research as a reference is to design a new student admissions system that is guided by the parameter of the distance of the domicile of prospective new students to the nearest school by modeling using the Haversine distance algorithm to analyze the distance between school locations and the location of domicile of prospective new students.…”

Section: Related Workmentioning

confidence: 99%

Balanced clustering for student admission school zoning by parameter tuning of constrained k-means

Zainuddin,

Nur Risal

2024

IJ-AI

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The Indonesian government issued a regulation through the Ministry of Education and Culture, number 51 of 2018, which contains zoning rules to improve the quality of education in school educational institutions. This research aims to compare the performance of the k-means algorithm with the constrained k-means algorithm to model the zoning of each school area based on the shortest distance parameter between the school location and the domicile of prospective students. The study used data from 2248 prospective students and 22 public school locations. The results of testing the k-means algorithm in grouping showed the formation of non-circular patterns in the cluster membership with different numbers of centroid cluster members. In contrast, testing the constrained k-means algorithm showed balanced outcomes in cluster membership with a membership value of 103 for each school as the cluster center. The research findings state that the developed constrained k-means algorithm solves the problem of unbalanced data clustering and overlapping issues in the process of new student admissions. In other words, the constrained k-means algorithm can be a reference for the government in making decisions on new student admissions

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Section: Related Workmentioning

confidence: 99%