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The effect of occupational safety and health on employee productivity in furniture industry Etruly, Niki; Nafi'ah, Roikhanatun
MBR (Management and Business Review) Vol 7 No 2 (2023): Desember
Publisher : Fakultas Ekonomika dan Bisnis, Universitas Kanjuruhan Malang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/mbr.v7i2.9254

Abstract

The risk of work accidents is a major concern in companies.  This is caused by the consequences that occur in work accidents to the deadly consequences of these accidents, so risk analysis even more important. The business process of the furniture industry from upstream to downstream has risk of occupational accidents. This study aims to determine the effect of occupational safety and health on employee productivity. Based on the results of data analysis, it can be concluded that the occupational safety does not have a significant effect on work productivity and occupational health variable  has a significant effect on work productivity. Simultaneously the independent variables have a significant relationship with work productivity. For future research, other variables that influence productivity can be added, such as leadership style, work discipline, and work motivation to expand the research area, so that it can produce new thoughts or studies.
Forensic-Based Investigation Optimization to Solving Traveling Salesman Problem Pamungkas, Prayoga Yudha; Latifah, Umi; Nafi'ah, Roikhanatun
Jurnal Tekno Vol. 22 No. 1 (2025): JURNAL TEKNO
Publisher : Direktorat Riset dan Pengabdian pada Masyarakat Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/33050y35

Abstract

The FBI Optimization (FBIO) represents one of the most novel metaheuristics which has been experimented with to solve Traveling Salesman Problem (TSP), demonstrating superior performance compared to tradition-al algorithms. Unlike conventional approaches that start with exploration and gradually shift to exploitation, FBIO maintains a dominant exploration phase throughout its iterations. Beginning with 100% exploration and tapering to approximately 90% dominance in exploration by the end of the process, FBIO effectively navigates the solution space, uncovering more promising routes. This exploration-centric approach enables FBIO to achieve solutions that are 8.39% closer to the near-optimal result compared to its counterparts. The algorithm’s enhanced performance in TSP highlights its potential applicability to other combinatorial optimization challenges. By prioritizing exploration, FBIO offers a robust framework for addressing complex prob-lems and ensures a comprehensive search of the solution space. Its ability to deliver high quality near-optimal solutions makes FBIO a valuable tool for future research, presenting new opportunities for solving various optimization problems and advancing practical problem-solving methodologies