GRACE SELVI MONICA ZEBUA, NPM 2109100034 (2025) METODE K-MEANS CLUSTERING UNTUK MENGUKUR TINGKAT KEDISIPLINAN PEGAWAI MENGGUNAKAN RAPIDMINER (STUDI KASUS : BADAN PENGELOLA KEUANGAN DAN ASET DAERAH LABUHANBATU). Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Kedisiplinan pegawai sangat penting untuk produktivitas organisasi, namun pemantauan manual sering kali tidak akurat dan memakan waktu. Penelitian ini memanfaatkan K-Means Clustering untuk mengelompokkan pegawai berdasarkan data kehadiran, keterlambatan, dan pelanggaran aturan. Analisis ini melibatkan pengumpulan data, penerapan algoritma K-Means, dan evaluasi menggunakan Davies-Bouldin Index (DBI) untuk mengukur kualitas pengelompokan. Hasil penelitian menunjukkan tiga kategori pegawai: disiplin, kurang disiplin, dan tidak disiplin. RapidMiner memungkinkan analisis yang lebih cepat dan akurat dibandingkan dengan metode manual. Temuan menunjukkan bahwa pegawai disiplin memiliki tingkat kehadiran yang tinggi dan sedikit pelanggaran, sementara pegawai kurang disiplin menunjukkan beberapa keterlambatan. Penelitian ini memberikan pendekatan berbasis data untuk meningkatkan kedisiplinan pegawai, serta memberikan wawasan berharga untuk pengambilan keputusan manajerial dan perumusan kebijakan di BPKAD Labuhanbatu. Kata Kunci : Kedisiplinan Pegawai, K-Means Clustering, RapidMiner, Manajemen Sumber Daya Manusia, Davies-Bouldin Index. ========================================= Employee discipline is vital for organizational productivity, yet manual monitoring can be inaccurate and time-consuming. This research utilizes K-Means Clustering to categorize employees based on attendance, tardiness, and rule violations. The analysis involves data collection, application of the K-Means algorithm, and evaluation using the Davies-Bouldin Index (DBI) to measure clustering quality. The results reveal three categories of employees: disciplined, less disciplined, and undisciplined. RapidMiner facilitates a quicker and more accurate analysis compared to manual methods. The findings indicate that disciplined employees have high attendance and few violations, while less disciplined employees show some tardiness. This research provides a data-driven approach to improving employee discipline, offering valuable insights for managerial decision-making and policy formulation at BPKAD Labuhanbatu. Keywords : Employee Discipline, K-Means Clustering, RapidMiner, Human Resource Management, Davies-Bouldin Index.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Kedisiplinan Pegawai, K-Means Clustering, RapidMiner, Manajemen Sumber Daya Manusia, Davies-Bouldin Index. ============================================ Employee Discipline, K-Means Clustering, RapidMiner, Human Resource Management, Davies-Bouldin Index |
| Subjects: | Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 14 Apr 2025 09:24 |
| Last Modified: | 14 Apr 2025 09:24 |
| URI: | http://repository.ulb.ac.id/id/eprint/1266 |
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