In this paper, an application based on Apache Hadoop is deployed to gather, store and analyze the data from Internet, especially online and social media. Nowadays, this application is a common tool for media analysis. In our case, it is used to assist in the modelling of system dynamics. Basically, There are several tools that will be used, such as for file system, data crawling from the Internet, data indexing, data storage, and data analytics. The selection of technology is as the industrial trend. Surely, this is not the best approach, but as another perspective for modelling of system dynamics. A system dynamics model is developed to study the profitability of the telecommunication company and how the complaint or negative sentiment will impact to their profits. The clustering analytics is used to identify the components of the system. In continuation of the improvement process, the clustering analytics will be used not only as one time effort. It runs periodically to develop a better model of the system. Sentiment analysis tool is used as the input for one of the component, which is the complaint component. The sentiments are sourced from online and social media. Manual investigation and analytics of Internet data is required in developing the relation between the components.

In the process of public policymaking, it is recommended to use system dynamics (SD) and Big Data in e-government. SD could assist in viewing problems in complex system holistically, while Big Data in e-government is for collecting data and information which are used for developing model of system dynamics and its equations. On this occasion, partial least square – structural equation modeling (PLS-SEM) is used to validate the relationship between public policymaking (PP), system dynamics (SD) and Big Data in the e-government. Brief explanation about survey and PLS-SEM are discussed. Some of the statistic parameters, such as path coefficients or beta coefficients, P values, R Square coefficients and effect size are shared and analyzed. These terms are parameters for us to see how strong the relationship between PP, SD and Big Data.

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In ICT capacity planning process, many organizations or institutions ignore unconsciously other components except statistical data of bandwidth utilization of ICT products. On this occasion, the ICT capacity planning process is analyzed by using system dynamics that considers some factors or components which are combinations between technical and non technical aspects such as: business, education, ICT infrastructure, ICT usage and cyber crime. Simulation of interrelationship between the components is conducted to understand the behavior of the system. System dynamics gives us input on correction of the statistical data by minimizing cyber crime effects. In this paper, it is also introduced the System Breakdown Structure (SBS), a technique to breakdown a big and complex system into smaller and manageable components. The objective of this SBS is to make system dynamics more expandable in hierarchy way in analyzing a system.

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