Penerapan Algoritma K-Means Clustering untuk Klasterisasi Pola Kehadiran Pegawai

Anom Danang Sumantri, Sattriedi Wahyu Binabar, Nurul Amalia

Abstract

Abstrak : 

Analisis pola kehadiran pegawai merupakan langkah penting dalam memahami karakteristik perilaku waktu kehadiran di lingkungan kerja. Penelitian ini bertujuan untuk menganalisis pola kehadiran pegawai berdasarkan data absensi pegawai menggunakan algoritma K-Means clustering. Tahapan penelitian meliputi pengumpulan data, pra-pemrosesan data, klasterisasi, evaluasi dan visualisasi. Data absensi selama tiga bulan diolah melalui tahap pembersihan, diskretisasi, pembentukan, pembuatan atribut baru dan normalisasi untuk memastikan kualitas data. Proses klasterisasi dilakukan dengan jumlah 3 klaster bedasarkan hasil metode elbow. Evaluasi model menggunakan metode Davies–Bouldin Index (DBI) menghasilkan nilai sebesar 0,743. Hasil klasterisasi kemudian divisualisasikan menggunakan Power BI untuk mempermudah interpretasi pola kehadiran. Berdasarkan hasil tersebut, diperoleh 3 pola waktu kehadiran utama yaitu waktu kehadiran baik, cukup, dan kurang yang menggambarkan perbedaan karakteristik waktu kehadiran pegawai secara lebih jelas. Hasil penelitian ini memberikan pemahaman menyeluruh mengenai pola waktu kehadiran pegawai yang dapat dijadikan dasar untuk analisis lebih lanjut, seperti evaluasi kinerja dan kedisplinan.

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Abstract :

Analysis of employee attendance patterns is an important step in understanding the characteristics of attendance behavior in the work environment. This study aims to analyze employee attendance patterns based on attendance data using the K-Means clustering algorithm. The research stages include data collection, data pre-processing, clustering, evaluation and visualization. Attendance data for 3 months is processed through the stages of cleaning, discretion, creation of new attributes and normalization to ensure data quality. The clustering process was carried out with a total of 3 clusters based on the results of the elbow method. The model evaluation using the Davies–Bouldin Index (DBI) method yielded a value of 0.743. The clustering results are then visualized using Power BI to make it easier to interpret attendance patterns. Based on these results, 3 main attendance time patterns were obtained, namely good, sufficient, and poor attendance time which depicts the differences in the characteristics of employee attendance time more clearly. The results of this study provide a comprehensive understanding of the pattern of employee attendance time which can be used as a basis for further analysis, such as performance and discipline evaluation.

Keywords

pola kehadiran pegawai; data mining; k-means clustering; visualisasi data

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References

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DOI: https://doi.org/10.20961/ijai.v10i1.110323

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