Penerapan Algoritma K-Means Data Mining Pada Clustering Kelayakan Penerima UKT Dengan Normalisasi Data Model Z-Score

Tuition Assistance is money given specifically to students with the aim of alleviating the problem of paying educational costs for less fortunate students so they can continue their education. With the large number of scholarship applicants on a campus, especially Budidarma University, a computerized information system is needed so that the selection of students who receive tuition assistance can run well. One way that can be implemented is by applying data mining with the K-Means algorithm. From the results of applying the data mining method, it can be concluded that there were 10 students who received tuition assistance who were included in cluster 1 and likewise in cluster 2 there were 10 students who did not receive tuition assistance.

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Publication Details

Journal
Building of Informatics Technology and Science (BITS)
Published
2024-12-30
DOI
https://doi.org/10.47065/bits.v6i3.6475
Citations
1
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.78
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article

Penerapan Algoritma K-Means Data Mining Pada Clustering Kelayakan Penerima UKT Dengan Normalisasi Data Model Z-Score

Salmon Salmon, Yunita Yunita, Muhammad Fahmi
1 citations
Building of Informatics Technology and Science (BITS)
Data Mining and Machine Learning Applications
0.78
article

Penerapan Algoritma K-Means Data Mining Pada Clustering Kelayakan Penerima UKT Dengan Normalisasi Data Model Z-Score

Salmon Salmon, Yunita Yunita, Muhammad Fahmi
article en
1 citations

Abstract

Tuition Assistance is money given specifically to students with the aim of alleviating the problem of paying educational costs for less fortunate students so they can continue their education. With the large number of scholarship applicants on a campus, especially Budidarma University, a computerized information system is needed so that the selection of students who receive tuition assistance can run well. One way that can be implemented is by applying data mining with the K-Means algorithm. From the results of applying the data mining method, it can be concluded that there were 10 students who received tuition assistance who were included in cluster 1 and likewise in cluster 2 there were 10 students who did not receive tuition assistance.

Building of Informatics Technology and Science (BITS)Vol. 6(3)
Universitas Widya Dharma Klaten (ID)
Openalex Percentile: Top 17%
Data Mining and Machine Learning Applications
0.78
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Penerapan Algoritma K-Means Data Mining Pada Clustering Kelayakan Penerima UKT Dengan Normalisasi Data Model Z-Score — Salmon Salmon, Yunita Yunita, et al. · Building of Informatics Technology and Science (BITS) (2024) | TGRS Research Map | TGRS