[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117139-en":3,"doc-seo-117139-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117139,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","A Dedication of Machine Learning for Trend of Digital HRM - Dedication and conceptual literature review","The digital transformation reshapes multiple aspects of human life and work, including human resource management. Machine learning enables systems to learn and adapt from data without explicit programming, offering strong potential to raise organizational human-resource efficiency. This study presents a literature review of recent research using machine learning, organized across machine-learning categories and linked to the dimensions of HRM, from human resource planning to broader HR operations.","|  | JPPIPA 9(Special Issue) (2023)\u003Cbr>Jurnal Penelitian Pendidikan IPA\u003Cbr>Journal of Research in Science Education\u003Cbr>[http://jppipa.unram.ac.id/index.php/jppipa/index](http://jppipa.unram.ac.id/index.php/jppipa/index) |  |\n| --- | --- | --- |\n\nA Dedication of Machine Learning for Trend of Digital HRM  \nDasmadi1  \n1 Slamet Riyadi University Surakarta, Indonesia.  \nReceived: October 24, 2023  \nRevised: December 10, 2023  \nAccepted: December 25, 2023  \nPublished: December 31, 2023  \nCorresponding Author: Dasmadi  \n[dasmadi@unisri.ac.id](dasmadi@unisri.ac.id)  \nDOI: 10.29303/jppipa.v9iSpecialIssue.5804  \n© 2023 The Authors. This open access article is distributed under a (CC-BY License)  \nAbstract: The digital world has inevitably entered various fields of human life in carrying out their duties as world leaders. Technology is an important tool to ease the human workload, including in this discourse is human resource management. Machine Learning is a technology that allows machines to learn and adapt quickly from given data without having to be explicitly programmed. Machine Learning has found its place in many industries and has great potential to improve the efficiency of human resources within organizations. This research is a literature review of several articles related to machine learning. The review was conducted from some of the recent research efforts that utilize machine learning. Furthermore, this review is derived from multiple literacies and includes an attempt at problem solving efforts that are divided into section areas from the perspective of each machine learning category. Machine learning can change the way the human resource management domain functions in an organization. It is making changes in all aspects of human resource management starting from human resource planning. Enormous data is available in human resource information systems (HRIS) available in organizations.  \nKeywords: Human Capital; Machine; Learning; Management  \nIntroduction  \nMachine learning is a subfield from the broad field of artificial intelligence, this aims to make machines able to learn like human. Learning here means understanding, observing and representing information about some statistical phenomenon (Milano, 2018) . Feeding ML models with big data can provide asset managers with recommendations that influence decision-making around portfolio allocation and/or stock selection, depending on the type of AI technique used (Mirete-Ferrer et al., 2022) . Big data has replaced traditional datasets, which are now considered a commodity easily available to all investors, and is being used by asset managers to gain insights in their investment process (OECD, 2021) .  \nFor the investment community, information has always been key and data has been the cornerstone of many investment strategies, from fundamental analysis to systematic trading and quantitative strategies alike (Schinckus, 2018) . While structured data was at the core  \nof such‘traditional’ strategies, vast amounts of raw or unstructured/semi-structured data are now promising to provide a new informational edge to investors deploying AI in the implementation of their strategies. AI allows asset managers to digest vast amounts of data from multiple sources and unlock insights from the data to inform their strategies at very short timeframes (Bose et al., 2023; OECD, 2021) .  \nHowever, in the research area of human resource management, there is still a lack of an overall ML application framework, combined with the specific dimensions of human resource management, to analyze its specific application. Therefore, based on the six dimensions of human resource management and the main technical applications of ML, this paper proposes a conceptual AI application to HRM model to guide enterprises how to use AI technology to assist human resource management (Jia et al., 2018; Vrontis et al., 2022) .  \nEmployee turnover can be defined as “The proportion of the employees who leave an orga","cbCaipAa6aR5TpPk","https://ap.wps.com/l/cbCaipAa6aR5TpPk","pdf",449289,1,6,"English","en",105,"# Introduction\n## Machine learning fundamentals and big data\n## Need for an ML application framework in HRM\n# Method\n## Descriptive qualitative research process\n## Data analysis sequence\n# Literature review and HRM implications","[{\"question\":\"What role does machine learning play in digital HRM?\",\"answer\":\"Machine learning can change how HRM functions in organizations by enabling faster learning from HR data and supporting decision-making across HR activities, including planning.\"},{\"question\":\"How was the research conducted?\",\"answer\":\"The study used descriptive research with a qualitative approach, following steps of data collection, sorting, analysis, and conclusion making, supported by an ordered data analysis sequence.\"},{\"question\":\"Why is an overall machine learning application framework for HRM needed?\",\"answer\":\"The paper notes a lack of a comprehensive ML application framework that integrates HRM dimensions with specific ML technical applications, prompting a proposed conceptual AI application to HRM.\"}]","A Dedication of Machine Learning for Trend of Digital HRM - 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