K-Means Clustering Data NIK Mahasiswa Baru Menggunakan Altair Ai-Studio

Yogi Bachtiar; Rayung Wulan; Aswin Fitriansyah

doi:10.30998/string.v10i1.27398

K-Means Clustering Data NIK Mahasiswa Baru Menggunakan Altair Ai-Studio

Yogi Bachtiar^(1*), Rayung Wulan⁽²⁾, Aswin Fitriansyah⁽³⁾

(1)
(2) Universitas Indraprasta PGRI
(3) Universitas Indraprasta PGRI
(*) Corresponding Author

Abstract

In this connected digital era, the need to have a secure and trusted digital identity is becoming increasingly important. One of the major steps taken by Indonesia in achieving this is by utilizing the NIK (Population Identification Number) as a single citizen identity data. NIK is a unique number given to every Indonesian citizen since their birth. Not only that, the utilization of NIK as single data also allows the government to conduct data analysis more effectively. With centralized data, the government can make decisions and make policies that are more targeted and in accordance with the needs of the community. In this research, the author applies data mining techniques using the K-Means algorithm. The approach is used to group data based on the distance between data, which organizes data into a predetermined number of clusters. The research stages start from data preparation, data transformation, data modeling and data evaluation. The results of the K-mean algorithm testing research using Altair AI-Studio can be concluded that the distribution of new Indraprasta PGRI University students for the 2023/2024 academic year based on NIK is spread across 32 of the 37 provinces in Indonesia with the largest population from DKI Jakarta province, namely 4,630 students and West Java as many as 4,219 students who are included in the Cluster0 category.

Keywords

Clustering; K-Means; Altair Ai-Studio

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DOI: http://dx.doi.org/10.30998/string.v10i1.27398