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Journal : JEMPER (Jurnal Ekonomi Manajemen Perbankan)

Pengaruh Kompensasi terhadap Kinerja Karyawan di PT Bukit Raya Sekawan Pratama, Yudhistira Anugerah; Yuliaty, Farida; Kosasih, Kosasih
Jurnal Ekonomi Manajemen Perbankan Vol 7 No 1 (2025): JEMPER Januari - Juni
Publisher : Prodi Manajemen S1 dan D3 Keuangan & Perbankan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32897/jemper.v7i1.4210

Abstract

This study analyzes the effect of compensation on employee performance at Bukit Raya Sekawan Mining Company using an associative quantitative approach. The study population consists of all employees of the company, totaling 45 individuals. Data were collected through observations, interviews, literature studies, documentation, observation guidelines, and questionnaires. The results indicate that compensation has a positive influence on employee performance. Therefore, the company needs to monitor and evaluate both compensation and employee performance to optimize the achievement of corporate goals and employee welfare. Additionally, the company should provide clear information about the compensation system, particularly regarding health benefits. Thus, it is expected that the company and employees can jointly fulfill their objectives, rights, and obligations as a unified entity in the future
Objektivitas AI dalam Rekrutmen dan Seleksi: Solusi atau Tantangan? Pratama, Yudhistira Anugerah; Isnajati, Louisiani Mansoni; Djabbar, Husnawati
Jurnal Ekonomi Manajemen Perbankan Vol 7 No 1 (2025): JEMPER Januari - Juni
Publisher : Prodi Manajemen S1 dan D3 Keuangan & Perbankan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32897/jemper.v7i1.4239

Abstract

This study investigates the use of artificial intelligence (AI) in employee recruitment and selection, focusing on enhancing objectivity and fairness. Employing both quantitative and qualitative methods, the research reveals that AI can improve efficiency, transparency, and consistency by evaluating candidates based on skills, experience, and job fit. However, the study also highlights that AI is not entirely free from bias, especially when trained on data reflecting historical discrimination. Therefore, organizations must recognize AI's limitations and ensure ongoing evaluation of these systems. While AI offers great potential to reduce human bias, it requires careful implementation with human oversight. The study concludes by recommending the development of ethical algorithms and promoting digital literacy among HR decision-makers to ensure fair and inclusive recruitment practice.