ANALISIS PERBANDINGAN ALGORITMA CLUSTERING K-MEANS DAN DBSCAN ( DENSITY - BASED CLUSTERING) UNTUK PENILAIAN KINERJA KARYAWAN DI PT HOTEL INDONESIA NATOUR

ADHIE, MUHAMMAD RIFQI AKHDAN (2024) ANALISIS PERBANDINGAN ALGORITMA CLUSTERING K-MEANS DAN DBSCAN ( DENSITY - BASED CLUSTERING) UNTUK PENILAIAN KINERJA KARYAWAN DI PT HOTEL INDONESIA NATOUR. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Assessment of an employee's performance is an important stage of employee performance evaluation for the Company. Monitoring and assessment of employee performance assessments must be carried out in order to improve the quality of human resources. This research aims to apply data mining for clustering employee performance assessments at PT Hotel Indonesia Natour using the K-Means algorithm. The method used to cluster employee performance assessments at PT Hotel Indonesia Natour is the k-means and DBSCAN algorithm methods. Calculation of the K-Means and DBSCAN algorithms for clustering employee performance reviews at PT HIN. The value of the K-Means algorithm is the best in cluster k=3 with the highest employee score of 153.4, the average employee score is 96.3, the lowest employee score is 72.3, but the The optimal DBI of the DBSCAN algorithm is an Eps value of 0.3, MinPts 5, the highest employee value is 108.1, the mean value is 104.899999999, the lowest value is found to be 104.100000001. Therefore, based on the results of calculations when testing the effectiveness of clusters in evaluating the performance of PT Hin employees, the K-Means algorithm is more optimal than the DBSCAN algorithm. Keywords: Employee assessment, k-means algorithm, Algoritma DBSCAN Clustering Penilaian terhadap suatu kinerja karyawan merupakan suatu tahap evaluasi kinerja karyawan yang penting bagi Perusahaan. Pemantauan dan penilaian terhadap suatu penilaian kinerja karyawan harus dilakukan agar dapat meningkatkan kualitas sumber daya manusia. Penelitian ini bertujuan untuk menerapkan data mining untuk clustering penilaian kinerja karyawan di PT Hotel Indonesia Natour menggunakan algoritma K-Means. Metode yang digunakan untuk meng-clustering penilaian kinerja karyawan di PT Hotel Indonesia Natour yaitu metode algoritma k-means dan DBSCAN. Perhitungan algoritma K-Means dan DBSCAN pada clustering review kinerja karyawan di PT HIN Nilai dari Algoritma K-Means yang terbaik pada cluster k=3 dengan nilai karyawan tertinggi yaitu 153.4, nilai rata-rata karyawan 96.3, rendah nilai karyawan sebesar 72.3, namun nilai DBI optimal algoritma DBSCAN adalah nilai Eps 0.3, MinPts 5, nilai karyawan tertinggi 108.1, nilai mean 104.899999999, ditemukan nilai terendah 104.100000001. Oleh karena itu, berdasarkan hasil perhitungan pada saat pengujian efektivitas cluster dalam evaluasi kinerja karyawan PT Hin, algoritma K-Means lebih optimal dibandingkan dengan algoritma DBSCAN. Kata Kunci : Penilaian karyawan, Algoritma k-means, Algoritma DBSCAN Clustering

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