Skripsi

CLUSTERING DATA GAJI KARYAWAN DI INDONESIA MENGGUNAKAN ALGORITMA KNN

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Salary is an important aspect in the employment sector as it reflects job value and employee welfare. Along with the rapid growth of salary data availability, effective analytical methods are required to transform raw data into meaningful information. However, salary data are often complex and unlabeled, making direct analysis difficult. This study aims to group salary data using the K-Means algorithm in order to identify patterns and distribution of average salary levels. The research employs a data mining approach with a clustering technique, which belongs to unsupervised learning. Prior to clustering, the salary data undergo preprocessing, including logarithmic transformation, to improve data stability. The results indicate that the K-Means algorithm is able to classify salary data into three distinct clusters, namely low, medium, and high salary groups. The resulting clusters provide a clearer representation of salary distribution and reveal noticeable differences between salary levels. Therefore, the clustering results can serve as a foundation for salary trend analysis and support decision-making processes in the employment sector.

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Detail Information

Series Title

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Call Number

T1932292026

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xvi, 83 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.312 07

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

-

Subject(s)

Prodi Teknik Informatika
Algoritma K-Nearest Neighbors (K-NN)

Specific Detail Info

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Statement of Responsibility

KA

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