Wednesday, September 24, 2008

Transaction Processing System (TPS)

A Transaction Processing System To be considered a transaction processing system the computer must pass the ACID test.
From a technical perspective, a Transaction Processing System (or Transaction Processing Monitor) monitors transaction programs, a special kind of programs. The essence of a transaction program is that it manages data that must be left in a consistent state. E.g. if an electronic payment is made, the amount must be either both withdrawn from one account and added to the other, or none at all. In case of a failure preventing transaction completion, the partially executed transaction must be 'rolled back' by the TPS. While this type of integrity must be provided also for batch transaction processing, it is particularly important for online processing: if e.g. an airline seat reservation system is accessed by multiple operators, after an empty seat inquiry, the seat reservation data must be locked until the reservation is made, otherwise another user may get the impression a seat is still free while it is actually being booked at the time. Without proper transaction monitoring, double bookings may occur. Other transaction monitor functions include deadlock detection and resolution (deadlocks may be inevitable in certain cases of cross-dependence on data), and transaction logging (in 'journals') for 'forward recovery' in case of massive failures.
Transaction Processing is not limited to application programs. The 'journaled file system' provided with IBMs AIX Unix operating system employs similar techniques to maintain file system integrity, including a journal.

Types of Transaction Processing Systems
  • Contrasted with batch processing
Batch processing is not transaction processing. Batch processing involves processing several transactions at the same time, and the results of each transaction are not immediately available when the transaction is being entered.[1]it has a time delay
  • Real-time and batch processing
There are a number of differences between real-time and batch processing. These are outlined below:
Each transaction in real-time processing is unique. It is not part of a group of transactions, even though those transactions are processed in the same manner. Transactions in real-time processing are stand-alone both in the entry to the system and also in the handling of output.
Real-time processing requires the master file to be available more often for updating and reference than batch processing. The database is not accessible all of the time for batch processing.

Real-time processing has fewer errors than batch processing, as transaction data is validated and entered immediately. With batch processing, the data is organised and stored before the master file is updated. Errors can occur during these steps.

Infrequent errors may occur in real-time processing; however, they are often tolerated. It is not practical to shut down the system for infrequent errors.
More computer operators are required in real-time processing, as the operations are not centralised. It is more difficult to maintain a real-time processing system than a batch processing system.

Features of Transaction Processing Systems
Rapid Response
Fast performance with a rapid response time is critical. Businesses cannot afford to have customers waiting for a TPS to respond, the turnaround time from the input of the transaction to the production for the output must be a few seconds or less.


Many organizations rely heavily on their TPS; a breakdown will disrupt operations or even stop the business. For a TPS to be effective its failure rate must be very low. If a TPS does fail, then quick and accurate recovery must be possible. This makes well–designed backup and recovery procedures essential.


A TPS wants every transaction to be processed in the same way regardless of the user, the customer or the time for day. If a TPS were flexible, there would be too many opportunities for non-standard operations, for example, a commercial airline needs to consistently accept airline reservations from a range of travel agents, accepting different transactions data from different travel agents would be a problem.

Controlled processing

The processing in a TPS must support an organization's operations. For example if an organization allocates roles and responsibilities to particular employees, then the TPS should enforce and maintain this requirement.

A transaction’s changes to the state are atomic: either all happen or none happen. These changes include database changes, messages, and actions on transducers.
Atomicity ensures that all of the steps involved in a transaction are completed successfully as a group. This means that if one step fails then no other steps after the failed step will be completed.


A transaction is a correct transformation of the state. The actions taken as a group do not violate any of the integrity constraints associated with the state. This requires that the transaction be a correct program!


Even though transactions execute concurrently, it appears to each transaction T, that others executed either before T or after T, but not both.


Once a transaction completes successfully (commits), its changes to the state survive failures.

Ensures that two users cannot change the same data at the same time. That is, one user cannot change a piece of data before another user has finished with it. For example, if an airliine ticket agent starts to reserve the last seat on a flight, then another agent cannot tell another passenger that a seat is available.

Storing and Retrieving

Storing and retrieving information from a TPS must be efficient and effective. The data are stored in warehouses or other databases, the system must be well designed for its backup and recovery procedures.

Databases and files

The storage and retrieval of data must be accurate as it is used many times throughout the day. A database is a collection of data neatly organized, which stores the accounting and operational records in the database. Databases are always protective of their delicate data, so they usually have a restricted view of certain data. Databases are designed using hierarchical, network or relational structures; each structure is effective in its own sense.
Hierarchical structure: organizes data in a series of levels, hence why it is called hierarchal. Its top to bottom like structure consists of nodes and branches; each child node has branches and is only linked to one higher level parent node.
Network structure: Similar to hierarchical, network structures also organizes data using nodes and branches. But, unlike hierarchical, each child node can be linked to multiple, higher parent nodes.
Relational structure: Unlike network and hierarchical, a relational database organizes its data in a series of related tables. This gives flexibility as relationships between the tables are built.

Tuesday, September 23, 2008

Enterprise Resource Planning (ERP)

Enterprise resource planning (ERP) is the planning of how business resources (materials, employees, customers etc.) are acquired and moved from one state to another.
An ERP system supports most of the business system that maintains in a single database the data needed for a variety of business functions such as Manufacturing, Supply Chain Management, Financials, Projects, Human Resources and Customer Relationship Management.
An ERP system is based on a common database and a modular software design. The common database can allow every department of a business to store and retrieve information in real-time. The information should be reliable, accessible, and easily shared. The modular software design should mean a business can select the modules they need, mix and match modules from different vendors, and add new modules of their own to improve business performance.
Ideally, the data for the various business functions are integrated. In practice the ERP system may comprise a set of discrete applications, each maintaining a discrete data store within one physical database.

The initials ERP originated as an extension of MRP (material requirements planning, and then manufacturing resource planning) and CIM (computer-integrated manufacturing) and was introduced by research and analysis firm Gartner. ERP systems now attempt to cover all basic functions of an enterprise, regardless of the organization's business or charter. Non-manufacturing businesses, non-profit organizations and governments now all use ERP systems.
To be considered an ERP system, a software package must provide the function of at least two systems. For example, a software package that provides both payroll and accounting functions could technically be considered an ERP software package.
However, the term is typically reserved for larger, more broadly based applications. The introduction of an ERP system to replace two or more independent applications eliminates the need for external interfaces previously required between systems, and provides additional benefits ranging from standardization and lower maintenance (one system instead of two or more) to easier and/or greater reporting capabilities (as all data is typically kept in one database).
Examples of modules in an ERP which formerly would have been stand-alone applications include: Manufacturing, Supply Chain, Financials, Customer Relationship Management (CRM), Human Resources, Warehouse Management and Decision Support System.

Maintenance and Support Services
Maintenance and Support Services involves monitoring and managing an Operational ERP system. This function is often provided in-house using members of the IT department, but may also be provided by specialist external consulting and services companies.


In the absence of an ERP system, a large manufacturer may find itself with many software applications that do not talk to each other and do not effectively interface. Tasks that need to interface with one another may involve: design engineering (how to best make the product), order tracking from acceptance through fulfillment, the revenue cycle from invoice through cash receipt, managing interdependencies of complex Bill of Materials, tracking the 3-way match between Purchase orders (what was ordered), Inventory receipts (what arrived), and Costing (what the vendor invoiced), the Accounting for all of these tasks, tracking the Revenue, Cost and Profit on a granular level.
Change how a product is made, in the engineering details, and that is how it will now be made. Effective dates can be used to control when the switch over will occur from an old version to the next one, both the date that some ingredients go into effect, and date that some are discontinued. Part of the change can include labeling to identify version numbers.

Some security features are included within an ERP system to protect against both outsider crime, such as industrial espionage, and insider crime, such as embezzlement. A data tampering scenario might involve a disgruntled employee intentionally modifying prices to below the breakeven point in order to attempt to take down the company, or other sabotage. ERP systems typically provide functionality for implementing internal controls to prevent actions of this kind. ERP vendors are also moving toward better integration with other kinds of information security tools.

Problems with ERP systems are mainly due to inadequate investment in ongoing training for involved personnel, including those implementing and testing changes, as well as a lack of corporate policy protecting the integrity of the data in the ERP systems and how it is used.
  • Customization of the ERP software is limited.
  • Re-engineering of business processes to fit the "industry standard" prescribed by the ERP system may lead to a loss of competitive advantage.
  • ERP systems can be very expensive leading to a new category of "ERP light" solutions
  • ERPs are often seen as too rigid and too difficult to adapt to the specific workflow and business process of some companies—this is cited as one of the main causes of their failure.
  • Many of the integrated links need high accuracy in other applications to work effectively. A company can achieve minimum standards, then over time "dirty data" will reduce the reliability of some applications.
  • Once a system is established, switching costs are very high for any one of the partners (reducing flexibility and strategic control at the corporate level).
  • The blurring of company boundaries can cause problems in accountability, lines of responsibility, and employee morale.
  • Resistance in sharing sensitive internal information between departments can reduce the effectiveness of the software.
  • Some large organizations may have multiple departments with separate, independent resources, missions, chains-of-command, etc, and consolidation into a single enterprise may yield limited benefits.
  • The system may be too complex measured against the actual needs of the customer.

Artificial Intelegent (AI)

Artificial intelligence (AI) is the intelligence of machines and the branch of computer science which aims to create it.

History of Artificial Intelligence research
History of artificial intelligence and timeline of artificial intelligence.
In the middle of the 20th century, a handful of scientists began a new approach to building intelligent machines, based on recent discoveries in neurology, a new mathematical theory of information, an understanding of control and stability called cybernetics, and above all, by the invention of the digital computer, a machine based on the abstract essence of mathematical reasoning.
The field of modern AI research was founded at conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for many decades, especially John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, who founded AI laboratories at MIT, CMU and Stanford. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle 60s their research was heavily funded by the U.S. Department of Defense and they were optimistic about the future of the new field:
1965, H. A. Simon: "[M]achines will be capable, within twenty years, of doing any work a man can do"
1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
These predictions, and many like them, would not come true. They had failed to recognize the difficulty of some of the problems they faced. In 1974, in response to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI. This was the first AI Winter.
In the early 80s, AI research was revived by the commercial success of expert systems (a form of AI program that simulated the knowledge and analytical skills of one or more human experts). By 1985 the market for AI had reached more than a billion dollars and governments around the world poured money back into the field. However, just a few years later, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, more lasting AI Winter began.
In the 90s and early 21st century AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence was adopted throughout the technology industry, providing the heavy lifting for logistics, data mining, medical diagnosis and many other areas. The success was due to several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.

Approaches to Artificial Intelligence
Artificial intelligence is a young science and there is still no established unifying theory. The field is fragmented and research communities have grown around different approaches.

The human brain provides inspiration for artificial intelligence researchers, however there is no consensus on how closely it should be simulated.
In the 40s and 50s, a number of researchers explored the connection between neurology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton and the Ratio Club in England.

Traditional symbolic
Artificial Intelligence
When access to digital computers became possible in the middle 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: CMU, Stanford and MIT, and each one developed its own style of research. John Haugeland named these approaches to AI "good old fashioned AI" or "GOFAI".

Cognitive simulation
Economist Herbert Simon and Alan Newell studied human problem solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team performed psychological experiments to demonstrate the similarities between human problem solving and the programs (such as their "General Problem Solver") they were developing. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 80s.

Logical AI

Unlike Newell and Simon, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithms. His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning. Logic was also focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.

"Scruffy" symbolic AI

Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult problems in vision and natural language processing required ad-hoc solutions – they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford), and this still forms the basis of research into commonsense knowledge bases (such as Doug Lenat's Cyc) which must be built one complicated concept at a time.

Knowledge based
Artificial Intelligence
When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications. This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software. The knowledge revolution was also driven by the realization that truly enormous amounts of knowledge would be required by many simple AI applications.

Sub-symbolic AI

During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in small demonstration programs. Approaches based on cybernetics or neural networks were abandoned or pushed into the background. By the 1980s, however, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.
Bottom-up, situated, behavior based or nouvelle AI
Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focussed on the basic engineering problems that would allow robots to move and survive. Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 50s and reintroduced the use of control theory in AI. These approaches are also conceptually related to the embodied mind thesis.

Computational Intelligence
Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle 1980s. These and other sub-symbolic approaches, such as fuzzy systems and evolutionary computation, are now studied collectively by the emerging discipline of computational intelligence.

In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly scientific, in the sense that their results are both measurable and verifiable, and they have been responsible for many of AI's recent successes. The shared mathematical language has also permitted a high level of collaboration with more established fields (like mathematics, economics or operations research). Russell & Norvig (2003) describe this movement as nothing less than a "revolution" and "the victory of the neats."

Intelligent agent paradigm

The "intelligent agent" paradigm became widely accepted during the 1990s. An intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success. The simplest intelligent agents are programs that solve specific problems. The most complicated intelligent agents are rational, thinking human beings. The paradigm gives researchers license to study isolated problems and find solutions that are both verifiable and useful, without agreeing on one single approach. An agent that solves a specific problem can use any approach that works — some agents are symbolic and logical, some are sub-symbolic neural networks and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields—such as decision theory and economics—that also use concepts of abstract agents.

Integrating the approaches

An agent architecture or cognitive architecture allows researchers to build more versatile and intelligent systems out of interacting intelligent agents in a multi-agent system. A system with both symbolic and sub-symbolic components is a hybrid intelligent system, and the study of such systems is artificial intelligence systems integration. A hierarchical control system provides a bridge between sub-symbolic AI at its lowest, reactive levels and traditional symbolic AI at its highest levels, where relaxed time constraints permit planning and world modelling. Rodney Brooks' subsumption architecture was an early proposal for such a hierarchical system.

Decision Support System (DSS)

Decision support systems constitute a class of computer-based information systems including knowledge-based systems that support decision-making activities.

Decision Support Systems (DSS) are a specific class of computerized information system that supports business and organizational decision-making activities. A properly-designed DSS is an interactive software-based system intended to help decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions.

Typical information that a decision support application might gather and present would be:

an inventory of all of your current information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
comparative sales figures between one week and the next,
projected revenue figures based on new product sales assumptions;
the consequences of different decision alternatives, given past experience in a context that is described.

Classifying DSS
There are several ways to classify DSS applications. Not every DSS fits neatly into one category, but a mix of two or more architecture in one.
Holsapple and Whinston classify DSS into the following six frameworks: Text-oriented DSS, Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS, and Compound DSS.
A compound DSS is the most popular classification for a DSS. It is a hybrid system that includes two or more of the five basic structures described by Holsapple and Whinston.
The support given by DSS can be separated into three distinct, interrelated categories: Personal Support, Group Support, and Organizational Support.
Additionally, the build up of a DSS is also classified into a few characteristics. 1) inputs: this is used so the DSS can have factors, numbers, and characteristics to analyze. 2) user knowledge and expertise: This allows the system to decide how much it is relied on, and exactly what inputs must be analyzed with or without the user. 3) outputs: This is used so the user of the system can analyze the decisions that may be made and then potentially 4) make a decision: This decision making is made by the DSS, however, it is ultimately made by the user in order to decide on which criteria it should use.
DSSs which perform selected cognitive decision-making functions and are based on artificial intelligence or intelligent agents technologies are called Intelligent Decision Support Systems (IDSS).

Benefits of DSS
Improves personal efficiency
Expedites problem solving
Facilitates interpersonal communication
Promotes learning or training
Increases organizational control
Generates new evidence in support of a decision
Creates a competitive advantage over competition
Encourages exploration and discovery on the part of the decision maker
Reveals new approaches to thinking about the problem space

Sistem Informasi Akuntansi

Sistem Informasi Akuntansi (SIA) adalah sebuah Sistem Informasi yang menangani segala sesuatu yang berkenaan dengan Akuntansi. Akuntansi sendiri sebenarnya adalah sebuah Sistem Informasi. Fungsi penting yang dibentuk SIA pada sebuah organisasi antara lain :
  • Mengumpulkan dan menyimpan data tentang aktivitas dan transaksi.
  • Memproses data menjadi into informasi yang dapat digunakan dalam proses pengambilan keputusan.
  • Melakukan kontrol secara tepat terhadap aset organisasi.

Untuk memahami bagaimana SIA bekerja, perlu untuk menjawab beberapa pertanyaan sebagai berikut :
  • Bagaimana mengoleksi data yang berkaitan dengan aktivitas dan transaksi organisasi?
  • Bagaimana mentransformasi data kedalam informasi sehingga manajemen dapat menggunakan untuk menjalankan organisasi?
  • Bagaimana menjamin ketersediaan, keandalan, keakuratan informasi ?
  • Bagaimana menjamin ketersediaan, keandalan, keakuratan informasi ?

Sebuah SIA menambah nilai dengan cara:
  • Menyediakan informasi yang akurat dan tepat waktu sehingga dapat melakukan aktivitas utama pada value chain secara efektif dan efisien.
  • Meningkatkan kualitas dan mengurangi biaya produk dan jasa yang dihasilkan.
  • Meningkatkan efisiensi.
  • Meningkatkan kemampuan dalam pengambilan keputusan.
  • Meningkatkan sharing knowledge.
  • Menambah efisiensi kerja pada bagian keuangan

Sistem Informasi Manajemen

Sistem Informasi Manajemen merupakan sistem informasi yang menghasilkan hasil keluaran (output) dengan menggunakan masukan (input) dan berbagai proses yang diperlukan untuk memenuhi tujuan tertentu dalam suatu kegiatan manajemen.

Tujuan Umum
  • Menyediakan informasi yang dipergunakan di dalam perhitungan harga pokok jasa, produk, dan tujuan lain yang diinginkan manajemen.
  • Menyediakan informasi yang dipergunakan dalam perencanaan, pengendalian, pengevaluasian, dan perbaikan berkelanjutan.
  • Menyediakan informasi untuk pengambilan keputusan.
Ketiga tujuan tersebut menunjukkan bahwa manajer dan pengguna lainnya perlu memiliki akses ke informasi akuntansi manajemen dan mengetahui bagaimana cara menggunakannya. Informasi akuntansi manajemen dapat membantu mereka mengidentifikasi suatu masalah, menyelesaikan masalah, dan mengevaluasi kinerja (informasi akuntansi dibutuhkan dam dipergunakan dalam semua tahap manajemen, termasuk perencanaan, pengendalian dan pengambilan keputusan).

Proses Manajemen
Proses manajemen didefinisikan sebagai aktivitas-aktivitas:
  • Perencanaan, formulasi terinci untuk mencapai suatu tujuan akhir tertentu adalah aktivitas manajemen yang disebut perencanaan. Oleh karenanya, perencanaan mensyaratkan penetapan tujuan dan identifikasi metode untuk mencapai tujuan tersebut.
  • Pengendalian, perencanaan hanyalah setengah dari peretempuran. Setelah suatu rencana dibuat, rencana tersebut harus diimplementasikan, dan manajer serta pekerja harus memonitor pelaksanaannya untuk memastikan rencana tersebut berjalan sebagaimana mestinya. Aktivitas manajerial untuk memonitor pelaksanaan rencana dan melakukan tindakan korektif sesuai kebutuhan, disebut kebutuhan.
  • Pengambilan Keputusan, proses pemilihan diantara berbagai alternative disebut dengan proses pengambilan keputusan. Fungsi manajerial ini merupakan jalinan antara perencanaan dan pengendalian. Manajer harus memilih diantara beberapa tujuan dan metode untuk melaksanakan tujuan yang dipilih. Hanya satu dari beberapa rencana yang dapat dipilih. Komentar serupa dapat dibuat berkenaan dengan fungsi pengendalian.

SIM merupakan kumpulan dari sistem informasi:
  • Sistem informasi akuntansi (accounting information systems), menyediakan informasi dan transaksi keuangan.
  • Sistem informasi pemasaran (marketing information systems), menyediakan informasi untuk penjualan, promosi penjualan, kegiatan-kegiatan pemasaran, kegiatan-kegiatan penelitian pasar dan lain sebagainya yang berhubungan dengan pemasaran.
  • Sistem informasi manajemen persediaan (inventory management information systems).
  • Sistem informasi personalia (personnel information systems).
  • Sistem informasi distribusi (distribution information systems).
  • Sistem informasi pembelian (purchasing information systems).
  • Sistem informasi kekayaan (treasury information systems).
  • Sistem informasi analisis kredit (credit analysis information systems).
  • Sistem informasi penelitian dan pengembangan (research and development information systems).
  • Sistem informasi analisis software.
  • Sistem informasi teknik (engineering information systems).

Sistem, Informasi, dan Data

Sistem berasal dari bahasa Latin (systēma) dan bahasa Yunani (sustēma) adalah suatu kesatuan yang terdiri komponen atau elemen yang dihubungkan bersama untuk memudahkan aliran informasi, materi atau energi. Istilah ini sering dipergunakan untuk menggambarkan suatu set entitas yang berinteraksi, di mana suatu model matematika seringkali bisa dibuat.
Sistem juga merupakan kesatuan bagian-bagian yang saling berhubungan yang berada dalam suatu wilayah serta memiliki item-item penggerak, contoh umum misalnya seperti negara. Negara merupakan suatu kumpulan dari beberapa elemen kesatuan lain seperti provinsi yang saling berhubungan sehingga membentuk suatu negara dimana yang berperan sebagai penggeraknya yaitu rakyat yang berada dinegara tersebut.
Kata "sistem" banyak sekali digunakan dalam percakapan sehari-hari, dalam forum diskusi maupun dokumen ilmiah. Kata ini digunakan untuk banyak hal, dan pada banyak bidang pula, sehingga maknanya menjadi beragam. Dalam pengertian yang paling umum, sebuah sistem adalah sekumpulan benda yang memiliki hubungan di antara mereka.

Elemen dalam sistem

Pada prinsipnya, setiap sistem selalu terdiri atas empat elemen:
Objek, yang dapat berupa bagian, elemen, ataupun variabel. Ia dapat benda fisik, abstrak, ataupun keduanya sekaligus; tergantung kepada sifat sistem tersebut.
Atribut, yang menentukan kualitas atau sifat kepemilikan sistem dan objeknya.
Hubungan internal, di antara objek-objek di dalamnya.
Lingkungan, tempat di mana sistem berada.

Jenis sistem

Ada berbagai tipe sistem berdasarkan kategori:
  • Atas dasar keterbukaan:
Sistem terbuka, dimana pihak luar dapat mempengaruhinya.
Sistem tertutup.
  • Atas dasar komponen:
Sistem fisik, dengan komponen materi dan energi.
Sistem non-fisik atau konsep, berisikan ide-ide.

Data adalah bentuk jamak dari datum, berasal dari bahasa Latin yang berarti "sesuatu yang diberikan". Dalam penggunaan sehari-hari data berarti suatu pernyataan yang diterima secara apa adanya. Pernyataan ini adalah hasil pengukuran atau pengamatan suatu variabel yang bentuknya dapat berupa angka, kata-kata, atau citra.

Informasi adalah hasil pemrosesan, manipulasi dan pengorganisasian/penataan dari sekelompok data yang mempunyai nilai pengetahuan (knowledge) bagi penggunanya. Namun demikian istilah ini memiliki banyak arti bergantung pada konteksnya, dan secara umum berhubungan erat dengan konsep seperti arti, pengetahuan, negentropy, komunikasi, kebenaran, representasi, dan rangsangan mental.
Banyak orang meggunakan istilah "era informasi", "masyarakat informasi," dan teknologi informasi, dalam bidang ilmu informasi dan ilmu komputer yang sering disorot, namun kata "informasi" sering dipakai tanpa pertimbangan yang cermat mengenai berbagai arti yang dimilikinya.