Phonteuka Vivaldi Fikry, - (2023) SISTEM PREDIKSI PENENTUAN KARYAWAN TETAP DI PT. OASE GLOBAL TEKNOLOGI MENGGUNAKAN METODE NAÏVE BAYES DAN LAPLACIAN CORRECTION. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Dalam era globalisasi dan persaingan industri yang semakin kompetitif, fenomena kesulitan mendapatkan pekerjaan merupakan tantangan yang nyata. Badan Pusat Statistik (BPS) mencatat bahwa tingkat pengangguran di Indonesia telah mengalami fluktuasi yang mencolok selama pandemi. Pada Februari 2021, angka pengangguran di kelompok usia 20-24 tahun mencapai 17,66%, mengalami kenaikan sebesar 3,36% dibandingkan periode sebelumnya. Dalam upaya mendapatkan pekerjaan, peran proses perekrutan dan seleksi karyawan di dalam perusahaan tidak dapat diabaikan. Setiap perusahaan memiliki kriteria dan proses seleksi yang berbeda dalam memilih karyawan yang paling cocok untuk posisi yang tersedia. Penelitian ini bertujuan merancang dan menerapkan Sistem Pendukung Keputusan (SPK/DSS) menggunakan algoritma klasifikasi Naïve Bayes dan metode Laplacian Correction dalam proses penetapan status karyawan tetap di PT. OASE GLOBAL TEKNOLOGI. Dengan fokus pada respons terhadap kompleksitas dunia kerja yang berubah dan kebutuhan akan seleksi karyawan yang akurat, penelitian ini menghasilkan sebuah DSS yang mampu memberikan rekomendasi objektif dan efisien. Algoritma Naïve Bayes digunakan untuk mengklasifikasikan calon karyawan, sedangkan metode Laplacian Correction digunakan untuk mengatasi nilai probabilitas nol. Implementasi DSS ini memiliki tingkat akurasi yang tinggi (90.382%) dan dinyatakan efektif oleh pengguna berdasarkan System Usability Scale (SUS), memberikan kontribusi praktis dalam pengembangan manajemen sumber daya manusia yang lebih adaptif dan efisien. In an era of globalization and increasingly competitive industries, the phenomenon of job difficulty is a significant real-world challenge. The Central Statistics Agency (BPS) of Indonesia has recorded notable fluctuations in the unemployment rate during the pandemic. In February 2021, the unemployment rate among the 20-24 age group reached 17.66%, marking an increase of 3.36% compared to the previous period. In the pursuit of employment opportunities, the role of the employee recruitment and selection process within companies cannot be understated. Each company possesses distinct criteria and selection processes in choosing the most suitable candidates for available positions. This study aims to design and implement a Decision Support System (DSS) using the Naïve Bayes classification algorithm and the Laplacian Correction method in the process of determining permanent employee status at PT. OASE GLOBAL TEKNOLOGI. With a focus on responding to the evolving complexity of the job market and the need for accurate employee selection, this research yields a DSS capable of providing objective and efficient recommendations. The Naïve Bayes algorithm is employed to classify prospective employees, while the Laplacian Correction method is used to address zero probability values. The implementation of this DSS demonstrates a high level of accuracy (90.382%) and is deemed effective by users based on the System Usability Scale (SUS), making a practical contribution to the development of more adaptive and efficient human resource management.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | https://scholar.google.com/citations?view_op=new_profile&hl=id&authuser=1 SINTA ID : 5974496 SINTA ID : 5991982 |
| Uncontrolled Keywords: | Classification, Decision Support System, Employee, Laplacian Correction, Naïve Bayes Classification, Prediction, RapidMiner, System Usability Scale (SUS), Waterfall, Website. |
| Subjects: | L Education > L Education (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Ilmu Komputer |
| Depositing User: | Phonteuka Vivaldi Fikry |
| Date Deposited: | 11 Sep 2023 09:23 |
| Last Modified: | 11 Sep 2023 09:23 |
| URI: | http://repository.upi.edu/id/eprint/103513 |
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