Sistem Pendukung Keputusan Penentuan Golongan UKT Bagi Calon Mahasiswa Baru Menggunakan Algoritma K-Nearest Neighbor

Authors

  • Said Fadlan Anshari Universitas Malikussaleh
  • Syahriani Putri Ayu Universitas Malikussaleh
  • Fadlisyah Fadlisyah Universitas Malikussaleh
  • Rizki Suwanda Universitas Malikussaleh
  • Tri Ramdhany Universitas Ekuitas indonesia

DOI:

https://doi.org/10.70340/jirsi.v5i1.271

Keywords:

UKT, Decision Support System, K-Nearest Neighbor

Abstract

In continuing lectures, financial readiness is needed to finance education. Single Tuition Fee (Uang Kuliah Tunggal or UKT) is a tuition fee in one semester where there is only one type of fee collection based on the economic and social conditions of the student's parents/guardians so that each student's payment is not the same. The existence of these group differences plus the increase in the UKT group can trigger demonstrations at Malikussaleh University for new students of the class of 2023. Therefore, a decision support system is needed in grouping UKT groups. This study uses the K-Nearest Neighbor  algorithm with  a dataset of 1381 UKT data for new students class of 2023. Furthermore, a split dataset was carried out  by dividing 90% of training data and 10% of testing data. Then the attributes used consist of 13 attributes including father's income, mother's income, father's education, mother's education, father's job, mother's job, home status, house area, number of cars, number of motorcycles, number of brothers, number of working brothers, and number of younger siblings. The outputs produced in this study are classified into 7 classes, namely UKT 1, 2, 3, 4, 5, 6, and 7. The accuracy results obtained at K = 15 were 70.5% with an error value of  29.5% with the results of the number of data in UKT 1 as many as 16 people, UKT 2 as many as 38 people, UKT 3 as many as 27 people, UKT 4 as many as 32 people, UKT 5 as many as 26 people, and UKT 6, and UKT 7 as many as 0 people.

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Published

2026-01-31