PENERAPAN ALGORITMA NAÏVE BAYES UNTUK PREDIKSI PENERIMAAN KARYAWAN

  • Intan Murni Pratiwi Universitas Buana Perjuangan Karawang
  • Ahmad Fauzi Universitas Buana Perjuangan Karawang
  • Santi Arum Puspita Lestari Universitas Buana Perjuangan Karawang
  • Yana Cahyana Universitas Buana Perjuangan Karawang

Abstract

The number of job seekers keeps growing, as does the quantity of companies that open job vacancies and offer opportunities to prospective employees. In terms of recruiting new employees, companies are very selective.. Companies are very selective in accepting prospective workers, where prospective workers must have qualifications that are in accordance with the positions needed in the company, because employees are an important asset in the growth and development of the company. because employees are an important factor in the growth and development of the company. Quality companies need good employees. This research uses employee recruitment data from PT Atma Darma Apta. The data has 372 rows and 8 attributes. The Naïve Bayes algorithm and the assessment techniques Mean Squared Error, Root Mean Squared Error, and R2 Score are used in this study. The results showed that the algorithm obtained good results by using a 90 to 10 data division resulting in a large accuracy value of 97.14%. In addition, the MSE, RMSE, and R2 Score values have quite good results, which are 2.86, 16.90, and 1.00. The 70 to 30 data division produces poor values with error values of 152.80 and 123.60, but the accuracy and R2 Score values are quite large at 96.15% and 0.95. With these results, this research can be continued into an application that can predict employee selection results.

References

[1] I. Dwi Yuliyanti and E. Zuraidah, “Penerapan Sistem Pendukung Keputusan Penerimaan Karyawan Baru Di PT. Patra Jasa Dengan Metode K-Means Clustering,” RESOLUSI: Rekayasa Teknik Informatika dan Informasi , vol. 2, no. 1, 2021, [Online]. Available: https://djournals.com/resolusi
[2] R. Saputra, D. Wijaya, T. Wijaya, and A. Sani, “Penerapan Data Mining dalam Proses Penerimaan Karyawan Baru pada Perusahaan Outsourcing (SDM) Dengan Metode C4.5 Menggunakan Aplikasi Weka,” 2023. [Online]. Available: https://ejournal.kreatifcemerlang.id/index.php/jbpi
[3] Y. Yuningsih and P. S. Eka, “Hasil Keberhasilan Penerimaan Karyawan Baru Menggunakan Metode Naïve Bayes,” Jurnal Teknlogi Informatika dan Komputer MH. Thamrin, vol. 8, no. 1, 2022.
[4] A. C. Sitorus, I. Kelana Jaya, and D. Hasibuhan, “Implementasi Data Mining Data Pencari Kerja Menggunakan Metode K-Means,” 2023. [Online]. Available: http://ojs.fikom-methodist.net/index.php/methosisfo
[5] A. Hewage, “Exploring the Applicability of Artificial Intelligence in Recruitment and Selection Processes: A Focus on the Recruitment Phase,” Journal of Human Resource and Sustainability Studies, vol. 11, no. 03, pp. 603–634, 2023, doi: 10.4236/jhrss.2023.113034.
[6] J. Miharja and Suhendri, “Penerapan Data Mining Penerimaan Karyawan Menggunakan Metode Naive Bayes Classifier,” Proceeding Sendiu, 2021.
[7] A. M. Husein and M. Brutu, “Prediksi Penerimaan Calon Karyawan Dengan Menggunakan Algoritma C4.5 Pada Biro Kesejahteraan Rakyat Provinsi Sumatera Utara,” Digital Transformation Technology, vol. 2, no. 1, pp. 16–20, Sep. 2022, doi: 10.47709/digitech.v2i1.1769.
[8] N. Khasanah and A. Salim, “Prediksi Kelulusan Mahasiswa Dengan Metode Naive Bayes,” Technologia, vol. 13, no. 3, 2022.
[9] L. W. Kusuma, “Prediksi Kemampuan Lulusan SMK untukk Dapat Bersaing Di Dunia Kerja dengan Menggunakan Naïve Bayes: Studi Kasus SMK Buddhi Tangerang,” Jurnal Algor, vol. 1, no. 1, 2019, [Online]. Available: https://jurnal.buddhidharma.ac.id/index.php/algor/index
[10] B. Umaternate, I. T. Umagapi, Yuyun, and Hazriani, “Prediksi Kelulusan Pegawai Pemerintah Dengan Perjanjian Kerja Guru Menggunakan Metode Naive Bayes,” Prosiding Seminar Nasional SISFOTEK, 2023.
[11] S. Royan, A. Yulian, and Syaechurodji, “Implementasi Data Mining Menggunakan Metode Naive Bayes Dengan Feature Selection Untukk Prediksi Kelulusan Mahasiswa Tepat Waktu,” Jurnal Sains dan Teknologi, vol. 5, no. 2, 2021.
[12] D. Fitrianah, S. Dwiasnati, H. H. H, and K. A. Baihaqi, “Penerapan Metode Machine Learning untukk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes,” Faktor Exacta, vol. 14, no. 2, p. 92, Aug. 2021, doi: 10.30998/faktorexacta.v14i2.9297.
[13] E. Widodo and A. Jaelani, “Penerapan Data Mining Untukk Prediksi Penerima Bantuan Pangan Non Tunai (BPNT) Di Desa Wanacala Menggunakan Metode Naïve Bayes,” vol. 13, 2022.
[14] I. Widhi Saputro and B. Wulan Sari, “Uji Performa Algoritma Naïve Bayes untukk Prediksi Masa Studi Mahasiswa Naïve Bayes Algorithm Performance Test for Student Study Prediction,” Citec Journal, vol. 6, no. 1, 2019.
[15] N. Basuni and Amril Mutoi Siregar, “Comparison of the Accuracy of Drug User Classification Models Using Machine Learning Methods,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 6, pp. 1348–1353, Dec. 2023, doi: 10.29207/resti.v7i6.5401.
[16] R. Difitria and I. Cholissodin, “Penerapan Support Vector Regression dan Particle Swarm Optimization untukk Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Daerah Istimewa Yogyakarta,” 2020. [Online]. Available: http://j-ptiik.ub.ac.id
[17] Arya Gupta, “Mean Squared Error : Ikhtisar, Contoh, Konsep, dan Lainnya,” Simplilearn.
[18] N. Nyoman, P. Pinata, M. Sukarsa, N. Kadek, and D. Rusjayanthi, “Prediksi Kecelakaan Lalu Lintas di Bali dengan XGBoost pada Python.”
[19] R. Paninggalih, B. Nugroho, and M. I. Takaendengan, “Prediksi Saldo Produksi Hasil Ternak Kabupaten Blitar Menggunakan Regresi Linier Berganda.” [Online]. Available: www.data.go.id.
[20] A. F. Watratan, A. P. B and D. Moeis, "Implementasi Algoritma Naive Bayes Untukk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia," Journal Of Applied Computer Science And Technology (JACOST), vol. 1, 2022.
Published
2024-06-30
How to Cite
PRATIWI, Intan Murni et al. PENERAPAN ALGORITMA NAÏVE BAYES UNTUK PREDIKSI PENERIMAAN KARYAWAN. Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 1, p. 236-243, june 2024. ISSN 2621-3079. Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1282>. Date accessed: 19 july 2024. doi: https://doi.org/10.37600/tekinkom.v7i1.1282.
Section
Articles