Objectivity based Self-Evolving Agent Model Validation for Social Issue Simulation

Jung, Joon-Young; Bae, Jang Won; Lee, Chunhee; Kang, Dong‐oh; Paik, Euihyun

doi:10.1109/ictc.2018.8539712

Cited by 1 publication

(1 citation statement)

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“…The parts of IT spending are the hardware and software purchased by the institution a year. The parts of the IT labour share are all investments in the permanent staff of an IT office, investments in personnel during an IT project and investment in temporary staff of an IT office [22,23].…”

Section: Datamentioning

confidence: 99%

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

Hidayat,

Mahardiko,

Rosyad

2023

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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.

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Section: Datamentioning

confidence: 99%