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All Journal Journal International Journal of Teaching and Learning (INJOTEL) Management Studies and Business Journal
Erlangga, Irwan Syah
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Other

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Journal : Management Studies and Business Journal

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Enhancing Quality Management through Advanced Statistical Techniques Nugroho, Cahyo Adi; Perdana, Janatika Putra; Tirtoadisuryo, Dendy; Rachmat, Asep Ferry; Erlangga, Irwan Syah
Management Studies and Business Journal (PRODUCTIVITY) Vol. 1 No. 9 (2024): Management Studies and Business Journal (PRODUCTIVITY)
Publisher : Penelitian dan Pengembangan Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62207/vbrcgz16

Abstract

Production environments with high variability present challenges in maintaining quality consistency, which are difficult to address using traditional approaches. This research aims to evaluate the impact of machine learning (ML)-based optimization on long-term quality management in industrial sectors that experience high production fluctuations. Using a systematic literature review approach with the PRISMA method, this research analyzes 18 studies related to the implementation of ML in quality process optimization. Results show that ML significantly supports product stability, defect reduction, and sustainable operational efficiency. The implications of this research strengthen the application of ML as a relevant and effective method for improving long-term quality in dynamic production environments.
The Role of AI in Enhancing HRM Practices A Comparative Study Across Industries Wibowo, Eko Putro; Avian, Zakhi Bailatul Nur; Tarigan, Frandy Putra Perdamen; Erlangga, Irwan Syah; Soenanta, Andy
Management Studies and Business Journal (PRODUCTIVITY) Vol. 1 No. 9 (2024): Management Studies and Business Journal (PRODUCTIVITY)
Publisher : Penelitian dan Pengembangan Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62207/yqwrzp72

Abstract

Application of artificial intelligence (AI) in Human Resource Management (HRM) is increasingly important in various industries to increase employee engagement and productivity. However, the impact of AI in HRM varies based on the specific characteristics of each industry, such as technology, manufacturing, services, and healthcare, demanding a targeted approach. This research aims to provide a comprehensive analysis of the role of AI in increasing employee engagement and productivity through a systematic literature review that examines research methods, industry distribution, and contextual factors that influence the effectiveness of AI in HRM. Using PRISMA methodology, a number of studies that met the inclusion and exclusion criteria were selected for analysis. The research results show that AI has a positive impact on employee engagement and productivity, but the impact varies across industries, influenced by organizational culture, skills requirements, and ethical and legal regulations. These findings provide guidance for HRM practitioners in effectively adopting AI according to the unique needs of each sector.
Co-Authors Avian, Zakhi Bailatul Nur Dede Umar Frandy Putra Perdamen Tarigan Lantri Ternasih Nugroho, Cahyo Adi Perdana, Janatika Putra Rachmat, Asep Ferry Soenanta, Andy Tarigan, Frandy Putra Perdamen Tirtoadisuryo, Dendy Wibowo, Eko Putro