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Sri Yayu Ninglasari
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International Journal of Business and Management Technology in Society (IJBMTS)
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 30254256     DOI : -
Core Subject : Economy, Social,
Economics, Econometrics & Finance Social Sciences
The International Journal of Business and Management Technology in Society (IJBMTS) is a peer-reviewed, open-access journal that publishes original research articles, review articles, and case studies in the fields of business, management, and management of technology. The journal aims to provide a platform for scholars, practitioners, and policymakers to exchange ideas and share knowledge on how business and management of technology practices can contribute to the advancement of the society. The journal welcomes submissions from authors around the world and encourages interdisciplinary perspectives. Focus and Scope -Human Resource Management -Operations & Supply Chain Management -Accounting & Governance -Financial Management -International Business -Information Management -Management of Technology -Enterprise System -Innovation & Entrepreneurship -Social Entrepreneurship -Knowledge Management -Business Model & Development -Economics & Econometric -Strategic Management -Small Medium Enterprises -Marketing & Branding -Corporate social responsibility
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2024)" : 2 Documents clear
Artificial Intelligence (AI) Technology Trends in Human Resource Productivity: A Bibliometric and Content Analysis Zahril Maulana Jilham Al'ula; Astra Savero Qomara; Tyassatrio Kuncorowibowo; Syarifa Hanoum
International Journal of Business and Management Technology in Society Vol. 2 No. 2 (2024)
Publisher : Direktorat Riset dan Pengabdian Kepada Masyarakat, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j30254256.v2i2.1023

Abstract

Purpose – This research aims to show research trends in the field of AI implementation in the human resources realm and its relationship with human resource productivity Methodology – This research combines bibliometric analysis with content analysis methods. Bibliometric analysis is carried out by quantitative and statistical analysis of a set of data that is linked using bibliometric indicators that represent a set of topics that are the research area in this study. Then the findings from the bibliometric method are supported by content analysis from various studies in this research area, so that it can produce output with a clearer perspective. Findings – This research show that Artificial Intelligence (AI) can have significant effect on productivity in some case, but it must also be acknowledged that companies must also be wise in ensuring that the work to be adapted with the help of AI is appropriate, because the implementation of AI has not yet reached the point where all human work can be assisted or replaced by AI. Research Limitation – This research was conducted only through the findings on several previous research, articles, and the data obtained from Scopus only. Practical Implications – Based on the bibliometric analysis of recent trends in AI technology and its impact on human resource productivity, it is recommended that organizations invest in AI-based HR tools and systems to improve their productivity and efficiency. The study highlights the need for HR professionals to stay up to date with the latest AI trends and technologies to remain competitive in the job market.
Optimizing Product Delivery through Two-Dimensional Time Warping Demand Allocation under Uncertainty Meilanitasari, Prita; Vanany, Iwan; Ma'ady, Mochamad Nizar Palefi; Isrofi, Nisa
International Journal of Business and Management Technology in Society Vol. 2 No. 2 (2024)
Publisher : Direktorat Riset dan Pengabdian Kepada Masyarakat, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j30254256.v2i2.1341

Abstract

Purpose – This study aims to optimize delivery operations by implementing a flexible clustering method to handle demand uncertainty and improve logistics efficiency. Methodology – This study develops a clustering algorithm using a two-dimensional time-warping approach to group demand points based on spatial proximity and demand characteristics. The methodology consists of three stages: 1) processing data on point distances, 2) clustering using two-dimensional time warping, and 3) validating through silhouette analysis. Findings – This study resulted in optimal and efficient demand clustering through location clustering with a Silhouette coefficient value of 0.7 or an accuracy and feasibility level of 70%. The algorithm also shows improved computational efficiency compared to traditional approaches, making it suitable for practical applications in uncertain and dynamic environments. Practical implications – This study holds significant importance for businesses in the logistics and retail sectors. Through demand clustering, businesses can effectively group customer demands and utilize this information to optimize inventory management and delivery solutions.

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