Implementasi Data Mining Metode K-Means Menggunakan Framework Python Dalam Mengelompokkan Pegawai Berdasarkan Data Presensi

 (*)Mira Mira Mail (Institut Shanti Bhuana, Bengkayang, Indonesia)
 Azriel Christian Nurcahyo (Institut Shanti Bhuana, Bengkayang, Indonesia)
 Candra Gudiato (Institut Shanti Bhuana, Bengkayang, Indonesia)
 Kusnanto Kusnanto (Institut Shanti Bhuana, Bengkayang, Indonesia)

(*) Corresponding Author

Submitted: May 16, 2024; Published: July 26, 2024

Abstract

Computerized employee attendance data collection provides convenience in terms of real-time monitoring. The computerized attendance data collection process has been widely implemented by government and private agencies. With a computerized system, the data collection process is easier to carry out. The data that has been collected increases as time passes. To improve discipline in employee attendance patterns. Perumdam Tirta Bengkayang implements an attendance application with features consisting of working hours, shifts, attendance location and is equipped with documentation of incoming and outgoing absences in the form of selfies attached to the application. The data that has been collected is then analyzed to assess employee discipline levels using data mining techniques, namely the K-Means method. Data mining methods are used to group employee attendance data patterns. Data mining is the process of collecting data, and finding patterns or relationships between data. The K-Means method works by dividing data into k closest clusters. The calculation begins by determining the value of k, centroid, and closest point. Meanwhile, the analysis uses the Python library by importing the necessary libraries such as numpy, pandas, matplotlib, sklearn. Based on the results of the analysis and grouping of employees, 26.76% of employees fall into cluster 0, namely the low level of discipline, 71.83% of employees fall into cluster 1, namely the medium level of discipline, and 1.41% of employees fall into cluster 2, namely the high level of discipline.

Keywords


Attendance; Data Mining; K-Means Clustering; Library; Python

Full Text:

PDF


Article Metrics

Abstract view : 161 times
PDF - 142 times

References

A. Presensi, K. Kegiatan, and J. Menggunakan, “Congregational Activity on Android-based Platform,” vol. 18, no. 03, pp. 113–120, 2023.

D. M. Pratama, S. A. Nulhaqim, and G. G. Kamil Basar, “Sistem Informasi Manajemen Dan Pemanfaatannya Pada Organisasi Pelayanan Kemanusiaan Aksi Cepat Tanggap Kabupaten Bandung Barat,” Share Soc. Work J., vol. 12, no. 1, p. 23, 2022, doi: 10.24198/share.v12i1.34699.

U. Aryanti and S. Karmila, “Sistem Informasi Absensi Pegawai Berbasis Web di Kantor Desa Nagreg,” Intern. (Information Syst. Journal), vol. 5, no. 1, pp. 90–101, 2022, doi: 10.32627/internal.v5i1.532.

P. R. Fhonna and A. Marzuki, “Sistem Informasi Absensi Pegawai Pada Biro Kominfo KantorBupati Kabupaten Aceh Utara Berbasis Web,” J. Ilmu Komput. dan Sist. Inf., vol. 3, no. 3, pp. 333–340, 2021.

B. Pane, J. R. Coyanda, and K. Ghazali, “Sistem Informasi Pegawai Pada Balai Diklat Keagamaan Palembang,” Digit. Transform. Technol., vol. 3, no. 2, pp. 557–568, 2023, doi: 10.47709/digitech.v3i2.2986.

M. O. Odja, F. J. Likadja, W. T. Ina, and S. I. Pella, “Penggunaan Microsoft Excel untuk Kemudahan Pengolahan Data Nilai Hasil Belajar Siswa,” ABDIMAS J. LPPM Undana, vol. 15, no. 2, pp. 22–29, 2021.

G. Tika and Adiwijaya, “Klasifikasi Topik Berita Berbahasa Indonesia Menggunakan Multilayer Perceptron,” e-Proceeding Eng., vol. 6, no. 2, p. 2137, 2019.

S. K. M. K. Mira, S. K. M. K. Candra Gudiato, and B. C. S. Bryan Chan, Computer Vision dan YOLO Menggali Potensi Computer Vision dan Implementasi YOLO untuk Pertanian Pintar. Uwais Inspirasi Indonesia , 2023. [Online]. Available: https://books.google.co.id/books?id=TVXpEAAAQBAJ

E. T. Naldy and A. Andri, “Penerapan Data Mining Untuk Analisis Daftar Pembelian Konsumen Dengan Menggunakan Algoritma Apriori Pada Transaksi Penjualan Toko Bangunan MDN,” J. Nas. Ilmu Komput., vol. 2, no. 2, pp. 89–101, 2021, doi: 10.47747/jurnalnik.v2i2.525.

N. Rakhmawaty, N. Y. Nasution, and F. D. T. Amijaya, “Perbandingan Metode K-Means Dan Metode Fuzzy C-Means (FCM) Pada Analisis Kinerja Pegawai PT. Cemara Khatulistiwa Persada Bontang,” J. EKSPONENSIAL, vol. 13, no. 1, pp. 63–71, 2022.

M. Fadel, A. Wibowo, T. Faculty, and U. Budi, “APPLICATION OF MACHINE LEARNING IN PREDICTING EMPLOYEE PENERAPAN MACHINE LEARNING DALAM PREDIKSI PELANGGARAN,” vol. 5, no. 1, pp. 171–178, 2024.

D. Murni, B. Efendi, N. Rahmadani, S. Informasi, and S. Tinggi Manajemen Informatika dan Komputer Royal Kisaran, “Implementation of Employee Discipline Clustering At Gotting Sidodadi Village Office Bandar Pasir Mandoge Using K-Means Algorithm,” J. Tek. Inform., vol. 3, no. 2, pp. 295–304, 2022, [Online]. Available: https://doi.org/10.20884/1.jutif.2022.3.2.236

M. A. K-means, T. Kristianda, and F. Putrawansyah, “Klasterisasi Pola Kehadiran Pegawai Institut Teknologi Pagar Alam,” vol. 1, no. 1, 2023.

M. Mira, I. Sembiring, and H. D. Purnomo, “Implementasi Transfer Learning Pada Algoritma Convolutional Neural Network untuk Mengklasifikasikan Image Objek Wisata,” Build. Informatics, Technol. Sci., vol. 4, no. 1, pp. 209–216, 2022, doi: 10.47065/bits.v4i1.1764.

D. Cahyanti, A. Rahmayani, and S. A. Husniar, “Analisis performa metode Knn pada Dataset pasien pengidap Kanker Payudara,” Indones. J. Data Sci., vol. 1, no. 2, pp. 39–43, 2020, doi: 10.33096/ijodas.v1i2.13.

A. Yudhistira and R. Andika, “Pengelompokan Data Nilai Siswa Menggunakan Metode K-Means Clustering,” J. Artif. Intell. Technol. Inf., vol. 1, no. 1, pp. 20–28, 2023, doi: 10.58602/jaiti.v1i1.22.

A. Y. Qur’ani and R. S. Widiastuti, “Implementasi Fuzzy C-Means (FCM) dan Generalized Fuzzy C-Means (GFCM) dalam Clustering Produk Domestik Regional Bruto (PDRB) Menurut Pengeluaran,” ULIL ALBAB J. Ilm. …, vol. 2, no. 9, pp. 4203–4214, 2023, [Online]. Available: https://journal-nusantara.com/index.php/JIM/article/view/2258%0Ahttps://journal-nusantara.com/index.php/JIM/article/download/2258/1880

R. Gelar Guntara, “Deteksi Atap Bangunan Berbasis Citra Udara Menggunakan Google Colab dan Algoritma Deep Learning YOLOv7,” J. Manaj. Sist. Inf., vol. 2, no. 1, pp. 9–18, 2023, doi: 10.59431/jmasif.v2i1.156.

D. S. R. Sukhdeve and S. S. Sukhdeve, “Google Colaboratory BT - Google Cloud Platform for Data Science: A Crash Course on Big Data, Machine Learning, and Data Analytics Services,” D. S. R. Sukhdeve and S. S. Sukhdeve, Eds. Berkeley, CA: Apress, 2023, pp. 11–34. doi: 10.1007/978-1-4842-9688-2_2.

V. A. Permadi, S. P. Tahalea, and R. P. Agusdin, “K-Means and Elbow Method for Cluster Analysis of Elementary School Data,” Prog. Pendidik., vol. 4, no. 1, pp. 50–57, 2023, doi: 10.29303/prospek.v4i1.328.

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.