Systems Analyst

Systems analyst is a good profession to begin a career in IT. Job as a systems analyst offering a dynamic and challenging work varied. Systems analyst is a key individual in the process of system development. Systems analysts study the problems and needs of the organization to determine how people, data, processes, communication, and information technology can improve business results. A systems analyst is also the person most responsible for the process analysis and design of information systems. The main task and a systems analyst is to determine the form of systems to be built later. This decision is not easy. Determine the format of the error system to be built will result in failure of the project. Therefore, a systems analyst must have a successful and provided with some specific skills, such as:

1. Analysis Expertise
Analytical skills needed to understand the organization's information systems need to be built. Analytical skills used to map the problems faced by the client company that could be solved with information systems and what does not. Analytical skills are also required to solve problems that have been found again using a computer-based technology. Overall this activity will be very helpful to view the organization as a system. By analyzing the components of a systems analyst will be easier to understand the whole process of running a business and find where the indicated subsystem having problems.

2. Technical Expertise
The main task of an analyst is to determine the form of a computerized system such as what happened to solve the problem of the client company or organization. Because of the problems found Hams solved by the computer technology necessary technical expertise is the mastery of technology software and hardware. A systems analyst is required to know and master the software and the latest hardware, and to know the advantages and limitations of these technologies and technologies. This will greatly assist the analyst in choosing the right technology for a very specific client needs. Technical expertise can be acquired and formal education, specialized training, and hours of flying time in developing the information system project.

3. Managerial skills
One task of the systems analyst is a right-handed manager of information systems in managing the resources of small-scale projects. Systems analyst responsible for managing the resources under its control, such as programmers and technicians. Appropriate allocation of tasks is very influential on how quickly the settlement
project. Systems analyst also should be able to predict risk and changes in external factors such as hardware prices, changes in client needs, the emergence of a competing product, and others.

4. Interpersonal Skills
The systems analyst is actively communicating with clients and come out into the other stakeholders. Communication skills is necessary to capture accurate information about the problems that exist on the client. Sometimes there are multiple types of clients who are covered or not understand its own business processes. This is where the ability to communicate and systems analysts will determine the success of the identification problem. Communication is also required to present their work as well as other stakeholders and analysts who need to be known by the user. Communication is also necessary for coordination and instruction with other stakeholders. By communicating effectively with other stakeholders, the development project can always be known, the recent changes can be monitored and responded to.

As for matters responsibility of a systems analyst include:

1. Effective data capture and business sources.
2. Data stream to the computer.
3. Processing and storage of data by computer.
4. And the flow of useful information back to the business processes and users.

Information System (IS) Manager

In a formidable team of talented leaders must be found. For project development team of information systems, information systems manager is the leader of this team. Manager in the information systems department has a direct role in the development process of the system if they handle small-scale organizations. IS manager role in allocating and monitoring systems development projects rather than directly involved in the process of system development.

In the large-scale IT departments, IT managers are usually divided into manager manager with more specific responsibilities, for example:

1. Manager for the department's overall IS commonly referred to as Chief Information Officer and is under the president or director of a company.
2. Other managers who led the divisions on IT departments, such as IS development manager, operations manager, manager of IS programmers, and others.

As a leader, manager Hams not directly involved in the process of making information systems. To monitor the work and other stakeholders, managers effectively communicate with other stakeholders through the key players, namely the systems analyst.

Stakeholder

Stakeholder are people who have a particular interest in a business activity. In the development of an information system, Whitten et. al. divide the stakeholders in the development of information systems to:

1. Information System (IS) Manager
2. Systems Analyst
3. Programmer
4. The end user
5. Supporting end users
6. Business Manager
7. Other Information System (IS) Technicians

The division is based on differences in the characteristics of the work they do to complete a project information system. This difference does not mean one has a more important role, but together to support each other the completion of a project information system.

Expert System (ES) and Artificial Intelligence (AI)

Expert System (ES) is a knowledge representation to describe the way an expert in approaching a problem. ES is more centered on how to manipulate the coding and knowledge of information (rules eg if...then). As for how the ES as follows:

1. Users communicate with the system using an interactive dialog.
2. ES asks questions (which will ask an expert) and the user provides an answer.
3. The answer used to determine which rules are used and the ES system provides recommendations based on rules that have been saved.
4. A knowledge engineer responsible for the acquisition of knowledge on how to do, as an analyst but are trained to use different techniques.

Artificial Intelligence (AI) is defined as the intelligence of scientific entities. Such systems are generally considered to be a computer. Intelligence was created and put into a machine (computer) in order to do the job as do humans. Several kinds of fields that use artificial intelligence expert systems, among others, computer games (games), fuzzy logic, neural networks and robotics.

Broadly speaking, the AI ​​is divided into two schools of thought namely Conventional AI and Computational Intelligence (CI, Computational Intelligence). Conventional AI mostly involves methods now diklasifiksikan as machine learning, characterized by formalism and statistical analysis. Also known as symbolic AI, logical AI, AI and AI pure old fashioned way (GOFAI, Good Old Fashioned Artificial Intelligence). Method-the method include:

1. Expert systems: the capability to apply judgment to reach conclusions. An expert system can process large amounts of known information and provide conclusions based on such information.
2. Considerations based on case
3. Bayesian networks
4. Behavior-based AI: a modular method to the formation of AI systems manually

Computational intelligence involves iterative development or learning (eg parameter tuning as in connectionist systems. Learning is based on empirical data and are associated with non-symbolic AI, AI irregular and soft computing. Basic methods include:

1. Neural networks: systems with pattern recognition capabilities are very strong
2. Fuzzy systems: techniques for consideration under uncertainty, has been used extensively in modern industrial and consumer product control systems.
3. Evolutionary Computation: applying concepts such as biologically inspired population, mutation and the "survival of the fittest" to produce a better solution.

These methods are mainly divided into evolutionary algorithms (eg genetic algorithms) and swarm intelligence (eg ant algorithms)

With hybrid intelligent systems, experiments designed to combine these two groups. Expert inference rules can be generated through a neural network or production rules from statistical learning such as the ACT-R. A promising new approach is mentioned that the strengthening of intelligence to try to achieve artificial intelligence in the process of evolutionary development as a side effect of the strengthening of human intelligence through technology.

Decision Support System (DSS)

Decision Support System (DSS) is a management information system at the level of an organization that combines data and sophisticated analytical models or data analysis tools to support the retrieval of semi-structured and unstructured. DSS is designed to help organizational decision-making. DSS is usually composed of:

1. Database (can be extracted from the Transaction Processing System / Management Information System).
2. Graphical or mathematical models, which are used for business processes.
3. User interface, which is used by the user to communicate with the DSS.