Analisis Metode Decision Tree dan Regresi Logistik Sebagai Sistem Rekomendasi Kenaikan Golongan Berdasarkan Kinerja Pegawai pada Universitas Lamappapoleonro

Authors

  • Andi Nurul Aksa Sistem Komputer, Universitas Handayani Makassar
  • Andani Achmad Teknik Elektro, Universitas Hasanuddin
  • Abdul Latief Arda Sistem Komputer, Universitas Handayani Makassar

DOI:

https://doi.org/10.33020/saintekom.v15i1.782

Keywords:

employee performance, decision tree, logistic regression, recommendation system, job promotion

Abstract

This research focuses on the importance of employee performance in supporting organizational success, especially in the promotion process at Lamappapoleonro University which is still done manually. Therefore, this research aims to develop a recommendation system for promotion using the Decision Tree and Logistic Regression methods, which is expected to speed up and simplify the decision-making process regarding employee promotions. The Decision Tree algorithm is used to classify employee performance in the form of sufficient, good, and excellent variables, while the Logistic Regression algorithm is used to predict the feasibility of employee promotion with the variable feasible or inappropriate. The data used in this study includes 12 independent variables, such as attendance, discipline, responsibility, and innovative ability. The analysis results show that the Decision Tree and Logistic Regression methods are able to produce accurate predictions, with an accuracy rate of 91.67% and 100% respectively. The main factors that influence promotion are honesty, discipline, and innovation ability. With this recommendation system, the employee promotion process becomes more efficient and accurate, providing significant benefits for human resource management at Lamappapoleonro University.

Downloads

Download data is not yet available.

References

Alatas, A., Mumpuni, R., & Lina Nurlaili, A. (2021). SPK Penilaian Kinerja Untuk Kenaikan Jabatan Pegawai Menggunakan Metode Moora. Jurnal Informatika Dan Sistem Informasi, 2(2), 171–180. https://doi.org/10.33005/jifosi.v2i2.358

Angioni, S. A., Giansante, C., Ferri, N., Ballarin, L., Pampanin, D. M., Marin, M. G., Bargione, G., Vasapollo, C., Donato, F., Virgili, M., Petetta, A., Lucchetti, A., Cabuga Jr, C. C., Masendo, C. B. ., Hernando, B. J. ., Joseph, C. C. ., Velasco, J. P. ., Angco, M. K. ., Ayaton, M. A., … Barile, N. B. (2021). Logistic Regression Model –A Review. International Journal of Innovative Science and Research Technology, 6(5), 1276–1280. http://dspace.ucuenca.edu.ec/bitstream/123456789/35612/1/Trabajo de

Ardiza, F., Nawangsari, L. C., & Sutawijaya, A. H. (2021). The Influence of Green Performance Appraisal and Green Compensation to Improve Employee Performance through OCBE. International Review of Management and Marketing, 11(4), 13–22. https://doi.org/10.32479/irmm.11632

Harahap, A. Y. N. (2020). Sistem Penunjang Keputusan Kenaikan Jabatan Karyawan Pada Pt.Ayn Dengan Menerapkan Bahasa Pemrograman Java. Jurnal Sistem Informasi Kaputama (JSIK), Vol 4 No 2, Juli 2020, 4(2), 111–117. http://repo.iain-tulungagung.ac.id/5510/5/BAB 2.pdf

Hasnining, A. (2023). Text Mining Untuk Klasifikasi Emosi Pengguna Media Sosial Dengan Algoritma Naïve Bayes Menurut laporan terbaru dari We Are judul “ Text Mining Untuk Klasifikasi Emosi A . Algoritma Naïve Bayes Algoritma Naive Bayes merupakan sebuah metoda klasifikasi mengg. Patria Artha Tecnological Journal, 7(1), 57–67.

Hoffman, D. W. (2022). SISTEM PENDUKUNG KEPUTUSAN KENAIKAN PANGKAT. SIBerPro, 7(2), 1–4.

Khair, F. El, Defit, S., & Yuhandri, Y. (2021). Sistem Keputusan dengan Metode Multi Attribute Utility Theory dalam Penilaian Kinerja Pegawai. Jurnal Informasi Dan Teknologi, 3, 215–220. https://doi.org/10.37034/jidt.v3i4.155

Križani?, S. (2020). Educational data mining using cluster analysis and decision tree technique: A case study. International Journal of Engineering Business Management, 12, 1–9. https://doi.org/10.1177/1847979020908675

Muriuki, M. N., & Wanyoike, R. (2021). Performance Appraisal and Employee Performance. International Academic Journal of Human Resource and Business Administration, 3(10), 265–272. https://iajournals.org/articles/iajhrba_v3_i10_265_272.pdf

Purwaningsih, Y., & Supriyanto, R. (2020). Sistem Pendukung Keputusan Promosi Pejabat Struktural Melalui Diklat Kepemimpinan Iv Menggunakan Metode Profile Matching Studi Kasus Di Pppptk Bahasa Jakarta. Jurnal Aplikasi Bisnis Dan Manajemen, 6(1), 74–85. https://doi.org/10.17358/jabm.6.1.74

Putri, S. R. (2024). Implementation of the decision tree method and C4.5 algorithm for teacher classification in obtaining web-based position promotion at SMK Al-Ihya Selajambe Kuningan-Implementation of the decision tree method and C4.5 algorithm for teacher classification i. Informatika Dan Sains, 14(01), 783–79. https://doi.org/10.54209/infosains.v14i01

Samudra, J. T., Hayadi, B. H., & Ramadhan, P. S. (2022). Komparasi 3 Metode Algoritma Klasifikasi Data Mining Pada Prediksi Kenaikan Jabatan. J-SISKO TECH (Jurnal Teknologi Sistem Informasi Dan Sistem Komputer TGD), 5(2), 127. https://doi.org/10.53513/jsk.v5i2.5642

Shabrilianti, S. S., Triayudi, A., & Lantana, D. A. (2023). Analisis Klasifikasi Perfomance KPI Salesman Menggunakan Metode Decision Tree Dan Naïve Bayes. Jurnal Riset Komputer (JURIKOM), 10(1), 182–191. https://doi.org/10.30865/jurikom.v10i1.5628

Toyib, R., & Saputera, S. A. (2020). Aplikasi Sistem Penilaian Kinerja Guru Dengan Metode Decision Tree Menggunakan Algoritma ID3 (Studi Kasus SLTP Negeri 3 Marga Sakti Bengkulu Utara). Journal of Technopreneurship and Information System (JTIS), 2(1), 1–7. https://doi.org/10.36085/jtis.v2i1.88

Yunita, A., Santoso, H. B., & Hasibuan, Z. A. (2021). Research Review on Big Data Usage for Learning Analytics and Educational Data Mining: A Way Forward to Develop an Intelligent Automation System. Journal of Physics: Conference Series, 1898(1). https://doi.org/10.1088/1742-6596/1898/1/012044

Downloads

PlumX Metrics

Published

31-03-2025

How to Cite

Aksa, Andi Nurul, Andani Achmad, and Abdul Latief Arda. 2025. “Analisis Metode Decision Tree Dan Regresi Logistik Sebagai Sistem Rekomendasi Kenaikan Golongan Berdasarkan Kinerja Pegawai Pada Universitas Lamappapoleonro”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 15 (1):42-55. https://doi.org/10.33020/saintekom.v15i1.782.

Issue

Vol. 15 No. 1 (2025): Maret 2025

Section

Articles

License

Copyright (c) 2025 Andi Nurul Aksa, Andani Achmad, Abdul Latief Arda

Creative Commons License

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

Copyright :
By submitting manuscripts to Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen, the author agrees with this policy. No specific document approval is required.

  1. The copyright in each article belongs to the author.
  2. Authors retain all their rights to the published work, not limited to the rights set forth in this page.
  3. Authors acknowledge that Saintekom Journal: Science, Technology, Computers and Management as the first to publish under the Creative Commons Attribution 4.0 International license (CC BY-SA).
  4. The author may submit the paper separately, arrange for non-exclusive distribution of the manuscript that has been published in this journal into other versions (e.g. sent to the author's institutional respository, publication into a book, etc.), by acknowledging that the manuscript has been first published Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen;
  5. The author warrants that the article is original, written by the named author, has not been previously published, contains no unlawful statements, does not infringe the rights of others, is subject to copyright exclusively held by the author.
  6. If the article is jointly prepared by more than one author, each author submitting the manuscript warrants that he or she has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to inform co-authors of the terms of this policy. Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen will not be held liable for anything that may arise due to internal author disputes.

Lisensi :
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen  is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY-SA). This license permits anyone to:.

  1. Share - copy and redistribute this material in any form or format;
  2. Adaptation - modify, alter, and create derivatives of this material for any purpose.
  3. Attribution - you must give appropriate credit, include a link to the license, and state that changes have been made. You may do this in any appropriate manner, but it does not imply that the licensor endorses you or your use.
  4. Similar Sharing - If you modify, alter, or create a derivative of this material, you must distribute your contribution under the same license as the original material.