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All Journal Diklat Review : Jurnal manajemen pendidikan dan pelatihan
Mustika, Rony
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Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

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SYSTEMATIC LITERATURE REVIEW : PEMANFAATAN BIG DATA DALAM PENGAMBILAN KEPUTUSAN SEKTORAL DI INDONESIA Mustika, Rony; Veri, Jonh
Diklat Review : Jurnal manajemen pendidikan dan pelatihan Vol. 9 No. 2 (2025)
Publisher : Komunitas Manajemen Kompetitif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35446/diklatreview.v9i2.2402

Abstract

This study aims to analyze the utilization of Big Data in sectoral decision-making in Indonesia through a Systematic Literature Review (SLR) approach. The review is based on 200 scientific articles from various databases, refined using the PRISMA protocol, resulting in 7 eligible studies. The analysis reveals that Big Data has been applied in multiple sectors including health, public policy, logistics, smart cities, information systems, corporations, and organizations. The findings highlight that Big Data plays a vital role in supporting evidence-based decision-making, enhancing operational efficiency, and strengthening strategic and competitive capabilities. The study also identifies gaps in the literature, especially in less-explored sectors such as education and agriculture. Hence, Big Data is positioned not only as an information source but also as a strategic instrument in Indonesia’s sectoral digital transformation. Keywords: Big Data, decision making, public sector, systematic literature review, Indonesia
SYSTEMATIC LITERATURE REVIEW MENGENAI PERAMALAN PERMINTAAN UNTUK PENGAMBILAN KEPUTUSAN MANAJERIAL Mustika, Rony; Lestari, Ariya Nyepi; Zefriyenni, Zefriyenni
Diklat Review : Jurnal manajemen pendidikan dan pelatihan Vol. 9 No. 2 (2025)
Publisher : Komunitas Manajemen Kompetitif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35446/diklatreview.v9i2.2400

Abstract

This study presents a Systematic Literature Review (SLR) on demand forecasting and its role in managerial decision-making. Articles published between 2015 and 2025 were collected from Scopus, Web of Science, Dimensions, and Google Scholar using Publish or Perish software. Following PRISMA guidelines, eleven relevant studies were selected. Findings reveal that traditional time series methods such as ARIMA and exponential smoothing are still widely applied, while machine learning and hybrid models are increasingly used for higher accuracy in complex demand patterns. Bibliometric analysis with VOSviewer identified four main research clusters: model development, forecasting methods, managerial approaches, and sectoral applications. The study concludes that demand forecasting is not only a technical tool but also a strategic instrument to enhance managerial decision-making. Keywords: decision-making, demand forecasting, machine learning, managerial economics, systematic literature review.
Co-Authors Lestari, Ariya Nyepi Veri, Jonh Zefriyenni Zefriyenni, Zefriyenni