Klasifikasi Pegawai Terbaik Triwulan pada BPS Provinsi Gorontalo Menggunakan Algoritma Naïve Bayes

Hanna Fidri Mardiny; Frangky Tupamahu; Hilmansyah Gani; Khairul Fathan Habie

doi:10.30606/rjti.v5i2.4714

Authors

Hanna Fidri Mardiny Universitas Muhammadiyah Gorontalo
Frangky Tupamahu Universitas Muhammadiyah Gorontalo
Hilmansyah Gani Universitas Muhammadiyah Gorontalo
Khairul Fathan Habie Universitas Muhammadiyah Gorontalo

DOI:

https://doi.org/10.30606/rjti.v5i2.4714

Keywords:

Naïve Bayes, Klasifikasi, Data Mining, Evaluasi Kinerja, Pegawai Terbaik

Abstract

The selection of the best employee is an important performance evaluation process aimed at improving employee motivation and productivity. At the Statistics Indonesia (BPS) of Gorontalo Province, the selection process still involves subjective considerations, which may affect the consistency and objectivity of decision-making. Therefore, a data-driven approach is needed to support the evaluation process. This study aimed to implement the Naïve Bayes algorithm to classify the best quarterly employee based on employee performance assessment data. The dataset consisted of performance records from 54 employees of BPS Gorontalo Province collected from the first quarter of 2023 to the fourth quarter of 2024. The classification process utilized BerAKHLAK behavioral indicators, discipline indicators, and Employee Performance Achievement (CKP) as predictor variables, while the target variable was employee status, namely best employee and non-best employee. The research stages included data preparation, data transformation, training and testing data partitioning, model development using the Naïve Bayes algorithm, and model evaluation using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The evaluation results showed that the proposed model achieved an accuracy, precision, recall, and F1-score of 100%, indicating high classification performance on the dataset used in this study. These findings demonstrate that the Naïve Bayes algorithm is effective in classifying employee performance and can be utilized as a decision-support tool for determining the best quarterly employee. The implementation of this method is expected to enhance the objectivity, consistency, and transparency of employee performance evaluation at Statistics Indonesia (BPS) of Gorontalo Province.

Downloads

Download data is not yet available.

References

“Peraturan Pemerintah Republik Indonesia Nomor 30 Tahun 2019 tentang Penilaian Kinerja Pegawai Negeri Sipil,” Pemerintah Republik Indonesia, Jakarta, 2019.

“Surat Edaran Menteri PANRB Nomor 20 Tahun 2021 tentang Implementasi Core Values ASN BerAKHLAK dan Employer Branding ASN ‘Bangga Melayani Bangsa,’” Kementerian PANRB, Jakarta, 2021.

A. C. Rahmat Kurniawan, D. Lituhayu, and D. Rostyaningsih, “Efektivitas Core Value BerAKHLAK dalam Meningkatkan Kinerja Pegawai Pada Badan Kepegawaian Daerah Kabupaten Rembang,” J. Public Policy Manag. Rev., vol. 13, no. 4, pp. 59–79, 2024.

F. Tupamahu and S. Suleman, “Klasifikasi Citra Digital Motif Karawo Berbasis Tekstur Menggunakan Jaringan Syaraf Tiruan,” J. Teknol. Inf. Indones., vol. 6, no. 1, 2021.

S. Liputo, F. Tupamahu, W. Hasyim, S. A. Sabiku, R. Parman, and A. Hanapi, “Prediction of Elementary School Students’ Mental Health using Decision Tree Algorithm with K-Fold Cross-Validation in Bone Bolango Regency, Gorontalo Province,” J. La Multiapp, vol. 4, no. 6, pp. 253–259, 2023, doi: 10.37899/journallamultiapp.v4i6.1005.

I. Ibrahim, H. Gani, R. Lamusu, and Y. Humolungo, “Perbandingan Algoritma Naïve Bayes dan C4.5 untuk Klasifikasi Bantuan Rumah Sehat,” J. Ilmu Komput., vol. 2, no. 1, pp. 72–80, 2022, doi: 10.31314/juik.v2i1.1477.

Y. Yunus, O. Maliki, and H. Gani, “Implementasi Metode AHP dalam Sistem Pendukung Keputusan untuk Penerimaan Karyawan di Sashop Kota Gorontalo,” J. Ilmu Komput., vol. 5, no. 1, pp. 70–79, 2025, doi: 10.31314/juik.v5i1.4036.

K. Kenny, D. Arisandi, and T. Sutrisno, “Evaluasi Penilaian Kinerja Karyawan dengan Metode Naïve Bayes,” Comput. J. Comput. Sci. Inf. Syst., vol. 8, no. 1, pp. 110–118, 2024, doi: 10.24912/computatio.v8i1.20287.

E. Poerwandono and F. J. Perwitosari, “Application of Data Mining for Employee Performance Assessment Using the Naïve Bayes Classification Method,” Saintek, vol. 5, no. 1, pp. 120–122, 2023, doi: 10.55338/saintek.v5i1.1416.

N. N. Wardah, A. M. Yunita, A. Sugiarto, and R. Rizky, “Implementasi Metode Naïve Bayes Untuk Menentukan Pegawai Terbaik Di Universitas Mathla’ul Anwar Banten,” J. Tek. Inform. Unis, vol. 9, no. 1, pp. 31–40, 2023, doi: 10.33592/jutis.v9i1.1291.

A. Algiffary and T. Sutabri, “Implementasi Machine Learning dengan Algoritma Naive Bayes Terhadap Sistem Informasi Pelayanan Pemberkasan Kepegawaian di BKPSDM Kota Palembang,” Indones. J. Comput. Sci., vol. 12, no. 2, pp. 284–301, 2023.

M. R. Khoirudin, M. Hasbi, B. Widada, K. Akhyar, and K. Sandradewi, “Klasifikasi Kelayakan Pegawai Kontrak Menjadi Pegawai Tetap Menggunakan Metode Naive Bayes,” J. Teknol. Inf. dan Komun., vol. 12, no. 2, pp. 7–15, 2024, doi: 10.30646/tikomsin.v12i2.863.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4th ed. Morgan Kaufmann, 2017.

A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd ed. O’Reilly Media, 2022.

E. Wahyudi, R. F. Wijaya, and Khairul, “K-Means and Naive Bayes Algorithms for Evaluation of Education Personnel Performance Based on SPMI Standards,” Sink. J. dan Penelit. Tek. Inform., vol. 8, no. 3, pp. 1872–1883, 2024, doi: 10.33395/sinkron.v8i3.13890.

J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 4th ed. Morgan Kaufmann, 2023.

D. N. S. S. Sangaji, “Penerapan Algoritma Naïve Bayes untuk Klasifikasi Penilaian Kinerja Pegawai di Kedai XYZ,” Kohesi J. Sains dan Teknol., vol. 7, no. 1, pp. 61–70, 2025.

R. Rudianto, T. Wahyudi, and P. Handayani, “Perbandingan Akurasi C4.5 dan Naive Bayes Untuk Evaluasi Kinerja Karyawan PT Catur Sentosa Adiprana,” JUSIKOM PRIMA, vol. 5, no. 2, pp. 61–66, 2022.