APPLICATION OF BIG DATA CONCEPTS TO OPTIMIZE ASSET MANAGEMENT AT PT POS INDONESIA

Authors

  • Muhammad Ruslan Maulani Universitas Logistik dan Bisnis Internasional
  • Iwan Setiawan Universitas Logistik dan Bisnis Internasional
  • Marwanto Rahmatuloh Universitas Logistik dan Bisnis Internasional

DOI:

https://doi.org/10.33197/jitter.vol10.iss2.2024.1528

Keywords:

Big Data, Asset management, PT Pos Indonesia, Scrum, Data analysis

Abstract

This research aims to apply the Big Data concept in order to optimize asset management at PT Pos Indonesia. PT Pos Indonesia needs to adopt Big Data technology to collect, manage, and analyze data generated by various sources. The research method used in this study is Scrum, a software development framework that focuses on flexibility and team collaboration. This research involves several stages. First, a needs analysis and identification of assets that need to be optimized were conducted. Next, data was collected from various sources such as asset management systems. The collected data is then processed and stored in a suitable Big Data infrastructure. Next, using the Scrum method, the research team and stakeholders were involved in the process of developing a Big Data solution. Short sprints were conducted to implement and test various components of the system, such as the data collection platform, analysis algorithms, and visualization of results. Regular iterations allowed for continuous adjustments and improvements according to feedback provided by the team and stakeholders. The result of this research is a prototype application and the application of the Big Data concept implemented in the asset management application at PT Pos Indonesia. By optimizing the use of data, PT Pos Indonesia can increase operational efficiency, improve decision making based on more accurate data analysis.

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Published

2024-04-15

How to Cite

[1]
M. R. Maulani, I. Setiawan, and Marwanto Rahmatuloh, “APPLICATION OF BIG DATA CONCEPTS TO OPTIMIZE ASSET MANAGEMENT AT PT POS INDONESIA”, JITTER, vol. 10, no. 2, Apr. 2024.

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Articles