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The Analysis of Data Preparation to Validate Model Values of Information Technology

Author

Listed:
  • Taufik Hidayat

    (Department of Electrical Engineering, Universitas Indonesia, Depok, Indonesia)

  • Rahutomo Mahardiko

    (Department of Data Management, PT.BFI Finance Indonesia Tbk, Indonesia)

  • Ali Miftakhu Rosyad

    (Department of Islamic Education, Universitas Wiralodra, Indramayu, Indonesia)

Abstract

Currently, there are some methods of preparing data for validating an IT value model correctly. One challenge in applying data mining to validate model values is to convert data into an appropriate form for this activity. Data mining algorithms can then be applied using the prepared data. The adequacy of data preparation often determines whether this data mining is successful or not. This study aims at creating a method for preparing the data during validation. The basic method used for data preparation is the Returns to Scale (RTS) method because it is easy to use and can be combined with further validation results. This method was applied by employing two models: two-factor and three-factor models. Both models are then compared to see the difference between them. The developed model is then tested on Branchless Banking (BB) and Downstream Petroleum (DP) industries. The results show that the method is applicable to prepare the data for validation. In addition, the results also demonstrate that both industries, DP and BB, have different result on data preparation, meaning that DP and BB have different ITs. This research contributes not only to a technique of preparing data for validating an IT value model by the RTS method but also can be a basis to work for data validation because it can give a result with the behaviour of the industry.

Suggested Citation

  • Taufik Hidayat & Rahutomo Mahardiko & Ali Miftakhu Rosyad, 2023. "The Analysis of Data Preparation to Validate Model Values of Information Technology," Virtual Economics, The London Academy of Science and Business, vol. 6(2), pages 23-34, June.
    • Handle: RePEc:aid:journl:v:6:y:2023:i:2:p:23-34
    DOI: 10.34021/ve.2023.06.02(2)
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    References listed on IDEAS

    as
    1. Taufik Hidayat & Rahutomo Mahardiko, 2021. "A mathematical model to forecast future banking income," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 19(4), pages 515-529.
    2. Lin, Winston T. & Chen, Yueh H. & Hung, TingShu, 2019. "A partial adjustment valuation approach with stochastic and dynamic speeds of partial adjustment to measuring and evaluating the business value of information technology," European Journal of Operational Research, Elsevier, vol. 272(2), pages 766-779.
    3. Kumbhakar, Subal C. & Wang, Hung-Jen, 2005. "Estimation of growth convergence using a stochastic production frontier approach," Economics Letters, Elsevier, vol. 88(3), pages 300-305, September.
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    1. Henryk Dzwigol & Aleksy Kwilinski & Oleksii Lyulyov & Tetyana Pimonenko, 2024. "Digitalization and Energy in Attaining Sustainable Development: Impact on Energy Consumption, Energy Structure, and Energy Intensity," Energies, MDPI, vol. 17(5), pages 1-17, March.

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