Jenis-Jenis Penelitian

Monday, February 27, 2012

1. Menurut Penggunaannya
Jenis penelitian bila dilihat dari segi penggunaannya dapat digolongkan menjadi:
i) Penelitian dasar atau penelitian murni ( pure research ) LIPI memberi definisi sebagai berikut. Penelitian dasar adalah setiap penelitian yang bertujuan untuk meningkatkan pengetahuan ilmiah atau untuk menemukan bidang penelitian baru tanpa suatu tujuan praktis tertentu. Artinya kegunaan hasil penelitian itu tidak segera dipakai namun dalam waktu jangka panjang juga akan terpakai.
ii) Penelitian terapan ( applied research )
Batasan yang diberikan LIPI adalah:
Penelitian terapan ialah setiap penelitian yang bertujuan untuk meningkatkan pengetahuan ilmiah dengan suatu tujuan praktis. Berarti hasilnya diharapkan segera dapat dipakai untuk keperluan praktis. Misalnya penelitian untuk menunjang kegiatan pembangunan yang sedang berjalan, penelitian untuk melandasi kebijakan pengambilan keputusan atau administrator.
Dilihat dari segi tujuannya, penelitian terapan berkepentingan dengan penemuan-penemuan yang berkenan dengan aplikasi dan sesuatu konsep-konsep teoritis tertentu.

2. Menurut Metodenya
Jenis penelitian dilihat dari segi metodenya adalah sebagai berikut :
i) Penelitian Historis
ii) Penelitian Filosofis
iii) Penelitian Observasional
iv) Penelitian Ekspremental

3. Menurut Sifat Permasalahannya
Sesuai dengan tugas penelitian itu untuk mmemberikan, menerangkan, meramalkan dan mengatasi permasalahan atau persoalan-persoalan, maka penelitian dapat pula digolongkan dari sudut pandangan ini. Sehingga penggolongan ini bisa mencakup penggolongan yang disebut terdahulu. Berdasarkan penggolongan ini dapat dipilih rancangan penelitian yang sesuai. Ada delapan jenis penelitian itu yakni:
i) Penelitian Historis
ii) Penelitian Deskriptif
iii) Penelitian Perkembangan
iv) Penelitian kasus dan Penelitian lapangan
v) Penelitian Korelasional
vi) Penelitian Kausal-Komparatif
vii) Penelitian Ekspremental
viii) Penelitian Tindakan

i) Penelitian Historis
Penelitian ditujukan kepada rekonstruksi masa lampau sistematis dan objektif memahami peristiwa-peristiwa masa lampau itu.
Data yang dikumpulkan pada penelitian ini sukar dikendalikan. Maka tingkat kepastian pemecahan permasalahan dengan metode ini adalah paling rendah. Data yang dikumpulkan biasanya hasil pengamatan orang lain seperti surat-surat arsip atau dokumen-dokumen masa lalu. Penelitian seperti ini jika ditujukan kepada kehidupan pribadi seseorang, maka penelitian disebut penelitian biografis.

ii) Penelitian Deskriptif
Penelitian deskripsi berusaha memberikan dengan sistematis dan cermat fakta-fakta aktual dan sifat populasi tertentu.
Misalnya: penelitian yang dilakukan mahasiswa untuk menyusun tesis memperoleh gelar sarjana kependidikan di IKIP, biasanya adalah penelitian deskriptif, seperti penelitian mengenai kemunduran prestasi belajar siswa, kemunduran rasa tanggung jawab.

iii) Penelitian Perkembangan
Penelitian perkembangan menyelidiki pola dan proses pertumbuhan atau perubahan sebagai fungsi dari waktu.
Kekhususan:
(1) Memusatkan perhatian pada ubahan-ubahan dan perkembangannya selama jangka waktu tertentu. Meneliti pola-pola pertumbuhan, laju, arah, dan urutan perkembangan dalam beberapa fase.
(2) Penelitian ini umumnya memakai waktu yang panjang atau bersifat longitudinal. Dan biasa dilakukan oleh peneliti ahli dengan fasilitas cukup.

iv) Penelitian kasus dan Penelitian lapangan
Penelitian kasus memusatkan perhatian pada suatu kasus secara intensif dan terperinci mengenai latar belakang keadaan sekarang yang dipermasalahkan.
kekhususan
(1) Subjek yang diteliti terdiri dari suatu kesatuan ( unit ) secara mendalam, sehingga hasilnya merupakan gambaran lengkap atau kasus pada unit itu. Kasus bisa terbatas pada satu orang saja, satu keluarga, satu daerah, satu peristiwa atau suatu kelompok terbatas lain.
(2) Selain penelitian hanya pada suatu unit, ubahan-ubahan yang diteliti juga terbatas, dari ubahan-ubahan dan kondisi-kondisi yang lebih besar jumlahnya, yang terpusat pada spek yang menjadi kasus. Biasanya penelitian ini dengan cara longitudinal.

v) Penelitian Korelasional
Penelitian korelasional bertujuan melihat hubungan antara dua gejala atau lebih.misalnya, apakah ada hubungan antara status sosial orang tua siswa dengan prestasi anak mereka.

vi) Penelitian Kausal-Komparatif
Penelitian untuk menyelidiki kemungkinan hubungan sebab akibat antara faktor tertentu yang mungkin menjadi penyebab gejala yang diselidiki.
Misalnya : sikap santai siswa dalam kegiatan belajar mungkin disebabkan banyaknya lulusan pendidikan tertentu yang tidak mendapat lapangan kerja.
Kekhususan
(1) Pengumpulan data mengenai gejala yang diduga mempunyai hubungan sebab akibat itu dilakukan setelah peristiwa yang dipermasalahkan itu telah terjadi ( penelitian bersifat ex post facto ).
(2) Suatu gejala yang diamati, diusut kembali dari suatu faktor atau beberapa faktor pada masa lampau.

vii) Penelitian Ekspremental
Penelitian dengan melakuakn percobaan terhadap kelompok-kelompok ekspremen. Kepada tiap kelompok ekspremen dikenakan perlakuan-perlakuan tertentu dengan kondisi-kondisi yang dapat dikontrol.
Data sebagai hasil pengaruh perlakuan terhadap kelompok ekspremen diukur secara kuantitatif kemudian dibandingkan. Misalnya, hendak meneliti keefektifan metode-metode mengajar. Penerapan tiap metode dicobakan terhadap kelompok-kelompok coba. Pada akhir percobaan prestasi belajar tiap kelompok dievaluasi.

viii) Penelitian Tindakan
Penelitian yang bertujuan untuk mengembangkan keterampilan baru untuk mengatasi kebutuhan dalam dunia kerja atau kebutuhan praktis lain. Misalnya, meneliti keterampilan kerja yang sesuai bagi siswa putus sekolah di suatu daerah.
Penelitian pengembangan keterampilan mengisi program B kurikulum SMA 1984.
Kekhususan
(1) Dipersiapkan untuk kebutuhan praktis yang berkaitan dengan dunia kerja.
(2) Penelitian didasarkan pada pengamatan aktual dan data tingkah laku. Menyiapkan program kerja untuk pemecahan masalah.
(3) Bersifat fleksibel, dapat diadakan perubahan selama proses penelitian bila dianggap penting untuk pembaruan ( inovasi ).

4. Menurut Bidang Ilmu
Ragam penelitian ditinjau dari bidangnya adalah: penelitian pendidikan (lebih lanjut lagi pendidikan guru, pendidikan ekonomi, pendidikan kesenian), ketekhnikan, ruang angkasa, pertanian, perbankan, kedokteran, keolahragaan, dan sebagainya.

The Applications Of Expert Systems

Friday, February 24, 2012

The spectrum of applications of expert systems technology to industrial and commercial problems is so wide as to defy easy characterization. The applications find their way into most areas of knowledge work. They are as varied as helping salespersons sell modular factory-built homes to helping NASA plan the maintenance of a space shuttle in preparation for its next flight.

Applications tend to cluster into seven major classes.

Diagnosis and Troubleshooting of Devices and Systems of All Kinds

This class comprises systems that deduce faults and suggest corrective actions for a malfunctioning device or process. Medical diagnosis was one of the first knowledge areas to which ES technology was applied (for example, see Shortliffe 1976), but diagnosis of engineered systems quickly surpassed medical diagnosis. There are probably more diagnostic applications of ES than any other type. The diagnostic problem can be stated in the abstract as: given the evidence presenting itself, what is the underlying problem/reason/cause?

Planning and Scheduling

Systems that fall into this class analyze a set of one or more potentially complex and interacting goals in order to determine a set of actions to achieve those goals, and/or provide a detailed temporal ordering of those actions, taking into account personnel, materiel, and other constraints. This class has great commercial potential, which has been recognized. Examples involve airline scheduling of flights, personnel, and gates; manufacturing job-shop scheduling; and manufacturing process planning.

Configuration of Manufactured Objects from Subassemblies

Configuration, whereby a solution to a problem is synthesized from a given set of elements related by a set of constraints, is historically one of the most important of expert system applications. Configuration applications were pioneered by computer companies as a means of facilitating the manufacture of semi-custom minicomputers (McDermott 1981). The technique has found its way into use in many different industries, for example, modular home building, manufacturing, and other problems involving complex engineering design and manufacturing.

Financial Decision Making

The financial services industry has been a vigorous user of expert system techniques. Advisory programs have been created to assist bankers in determining whether to make loans to businesses and individuals. Insurance companies have used expert systems to assess the risk presented by the customer and to determine a price for the insurance. A typical application in the financial markets is in foreign exchange trading.

Knowledge Publishing

This is a relatively new, but also potentially explosive area. The primary function of the expert system is to deliver knowledge that is relevant to the user's problem, in the context of the user's problem. The two most widely distributed expert systems in the world are in this category. The first is an advisor which counsels a user on appropriate grammatical usage in a text. The second is a tax advisor that accompanies a tax preparation program and advises the user on tax strategy, tactics, and individual tax policy.

Process Monitoring and Control

Systems falling in this class analyze real-time data from physical devices with the goal of noticing anomalies, predicting trends, and controlling for both optimality and failure correction. Examples of real-time systems that actively monitor processes can be found in the steel making and oil refining industries.

Design and Manufacturing

These systems assist in the design of physical devices and processes, ranging from high-level conceptual design of abstract entities all the way to factory floor configuration of manufacturing processes.

Bricks and Mortar


The fundamental working hypothesis of AI is that intelligent behavior can be precisely described as symbol manipulation and can be modeled with the symbol processing capabilities of the computer.

In the late 1950s, special programming languages were invented that facilitate symbol manipulation. The most prominent is called LISP (LISt Processing). Because of its simple elegance and flexibility, most AI research programs are written in LISP, but commercial applications have moved away from LISP.

In the early 1970s another AI programming language was invented in France. It is called PROLOG (PROgramming in LOGic). LISP has its roots in one area of mathematics (lambda calculus), PROLOG in another (first-order predicate calculus).

PROLOG consists of English-like statements which are facts (assertions), rules (of inference), and questions. Here is an inference rule: "If object-x is part-of object-y then a component-of object-y is object-x."

Programs written in PROLOG have behavior similar to rule-based systems written in LISP. PROLOG, however, did not immediately become a language of choice for AI programmers. In the early 1980s it was given impetus with the announcement by the Japanese that they would use a logic programming language for the Fifth Generation Computing Systems (FGCS) Project. A variety of logic-based programming languages have since arisen, and the term prolog has become generic.

Tools, Shells, and Skeletons


Compared to the wide variation in domain knowledge, only a small number of AI methods are known that are useful in expert systems. That is, currently there are only a handful of ways in which to represent knowledge, or to make inferences, or to generate explanations. Thus, systems can be built that contain these useful methods without any domain-specific knowledge. Such systems are known as skeletal systems, shells, or simply AI tools.

Building expert systems by using shells offers significant advantages. A system can be built to perform a unique task by entering into a shell all the necessary knowledge about a task domain. The inference engine that applies the knowledge to the task at hand is built into the shell. If the program is not very complicated and if an expert has had some training in the use of a shell, the expert can enter the knowledge himself.

Many commercial shells are available today, ranging in size from shells on PCs, to shells on workstations, to shells on large mainframe computers. They range in price from hundreds to tens of thousands of dollars, and range in complexity from simple, forward-chained, rule-based systems requiring two days of training to those so complex that only highly trained knowledge engineers can use them to advantage. They range from general-purpose shells to shells custom-tailored to a class of tasks, such as financial planning or real-time process control.

Although shells simplify programming, in general they don't help with knowledge acquisition. Knowledge acquisition refers to the task of endowing expert systems with knowledge, a task currently performed by knowledge engineers. The choice of reasoning method, or a shell, is important, but it isn't as important as the accumulation of high-quality knowledge. The power of an expert system lies in its store of knowledge about the task domain -- the more knowledge a system is given, the more competent it becomes.

Knowledge engineering



Knowledge engineering is the art of designing and building expert systems, and knowledge engineers are its practitioners. Gerald M. Weinberg said of programming in The Psychology of Programming: "'Programming,' -- like 'loving,' -- is a single word that encompasses an infinitude of activities" (Weinberg 1971). Knowledge engineering is the same, perhaps more so. We stated earlier that knowledge engineering is an applied part of the science of artificial intelligence which, in turn, is a part of computer science. Theoretically, then, a knowledge engineer is a computer scientist who knows how to design and implement programs that incorporate artificial intelligence techniques. The nature of knowledge engineering is changing, however, and a new breed of knowledge engineers is emerging. We'll discuss the evolving nature of knowledge engineering later.

Today there are two ways to build an expert system. They can be built from scratch, or built using a piece of development software known as a "tool" or a "shell." Before we discuss these tools, let's briefly discuss what knowledge engineers do. Though different styles and methods of knowledge engineering exist, the basic approach is the same: a knowledge engineer interviews and observes a human expert or a group of experts and learns what the experts know, and how they reason with their knowledge. The engineer then translates the knowledge into a computer-usable language, and designs an inference engine, a reasoning structure, that uses the knowledge appropriately. He also determines how to integrate the use of uncertain knowledge in the reasoning process, and what kinds of explanation would be useful to the end user.

Next, the inference engine and facilities for representing knowledge and for explaining are programmed, and the domain knowledge is entered into the program piece by piece. It may be that the inference engine is not just right; the form of knowledge representation is awkward for the kind of knowledge needed for the task; and the expert might decide the pieces of knowledge are wrong. All these are discovered and modified as the expert system gradually gains competence.

The discovery and cumulation of techniques of machine reasoning and knowledge representation is generally the work of artificial intelligence research. The discovery and cumulation of knowledge of a task domain is the province of domain experts. Domain knowledge consists of both formal, textbook knowledge, and experiential knowledge -- the expertise of the experts.

The Building Blocks of Expert Systems


Every expert system consists of two principal parts: the knowledge base; and the reasoning, or inference, engine.

The knowledge base of expert systems contains both factual and heuristic knowledge. Factual knowledge is that knowledge of the task domain that is widely shared, typically found in textbooks or journals, and commonly agreed upon by those knowledgeable in the particular field.

Heuristic knowledge is the less rigorous, more experiential, more judgmental knowledge of performance. In contrast to factual knowledge, heuristic knowledge is rarely discussed, and is largely individualistic. It is the knowledge of good practice, good judgment, and plausible reasoning in the field. It is the knowledge that underlies the "art of good guessing."

Knowledge representation formalizes and organizes the knowledge. One widely used representation is the production rule, or simply rule. A rule consists of an IF part and a THEN part (also called a condition and an action). The IF part lists a set of conditions in some logical combination. The piece of knowledge represented by the production rule is relevant to the line of reasoning being developed if the IF part of the rule is satisfied; consequently, the THEN part can be concluded, or its problem-solving action taken. Expert systems whose knowledge is represented in rule form are called rule-based systems.

Another widely used representation, called the unit (also known as frame, schema, or list structure) is based upon a more passive view of knowledge. The unit is an assemblage of associated symbolic knowledge about an entity to be represented. Typically, a unit consists of a list of properties of the entity and associated values for those properties.

Since every task domain consists of many entities that stand in various relations, the properties can also be used to specify relations, and the values of these properties are the names of other units that are linked according to the relations. One unit can also represent knowledge that is a "special case" of another unit, or some units can be "parts of" another unit.

The problem-solving model, or paradigm, organizes and controls the steps taken to solve the problem. One common but powerful paradigm involves chaining of IF-THEN rules to form a line of reasoning. If the chaining starts from a set of conditions and moves toward some conclusion, the method is called forward chaining. If the conclusion is known (for example, a goal to be achieved) but the path to that conclusion is not known, then reasoning backwards is called for, and the method is backward chaining. These problem-solving methods are built into program modules called inference engines or inference procedures that manipulate and use knowledge in the knowledge base to form a line of reasoning.

The knowledge base an expert uses is what he learned at school, from colleagues, and from years of experience. Presumably the more experience he has, the larger his store of knowledge. Knowledge allows him to interpret the information in his databases to advantage in diagnosis, design, and analysis.

Though an expert system consists primarily of a knowledge base and an inference engine, a couple of other features are worth mentioning: reasoning with uncertainty, and explanation of the line of reasoning.

Knowledge is almost always incomplete and uncertain. To deal with uncertain knowledge, a rule may have associated with it a confidence factor or a weight. The set of methods for using uncertain knowledge in combination with uncertain data in the reasoning process is called reasoning with uncertainty. An important subclass of methods for reasoning with uncertainty is called "fuzzy logic," and the systems that use them are known as "fuzzy systems."

Because an expert system uses uncertain or heuristic knowledge (as we humans do) its credibility is often in question (as is the case with humans). When an answer to a problem is questionable, we tend to want to know the rationale. If the rationale seems plausible, we tend to believe the answer. So it is with expert systems. Most expert systems have the ability to answer questions of the form: "Why is the answer X?" Explanations can be generated by tracing the line of reasoning used by the inference engine (Feigenbaum, McCorduck et al. 1988).

The most important ingredient in any expert system is knowledge. The power of expert systems resides in the specific, high-quality knowledge they contain about task domains. AI researchers will continue to explore and add to the current repertoire of knowledge representation and reasoning methods. But in knowledge resides the power. Because of the importance of knowledge in expert systems and because the current knowledge acquisition method is slow and tedious, much of the future of expert systems depends on breaking the knowledge acquisition bottleneck and in codifying and representing a large knowledge infrastructure.

Expert Systems


Expert systems are computer programs that are derived from a branch of computer science research called Artificial Intelligence (AI). AI's scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior. It is concerned with the concepts and methods of symbolic inference, or reasoning, by a computer, and how the knowledge used to make those inferences will be represented inside the machine.
Of course, the term intelligence covers many cognitive skills, including the ability to solve problems, learn, and understand language; AI addresses all of those. But most progress to date in AI has been made in the area of problem solving -- concepts and methods for building programs that reason about problems rather than calculate a solution.
AI programs that achieve expert-level competence in solving problems in task areas by bringing to bear a body of knowledge about specific tasks are called knowledge-based or expert systems. Often, the term expert systems is reserved for programs whose knowledge base contains the knowledge used by human experts, in contrast to knowledge gathered from textbooks or non-experts. More often than not, the two terms, expert systems (ES) and knowledge-based systems (KBS), are used synonymously. Taken together, they represent the most widespread type of AI application. The area of human intellectual endeavor to be captured in an expert system is called the task domain. Task refers to some goal-oriented, problem-solving activity. Domain refers to the area within which the task is being performed. Typical tasks are diagnosis, planning, scheduling, configuration and design. An example of a task domain is aircraft crew scheduling, discussed in Chapter 2.
Building an expert system is known as knowledge engineering and its practitioners are called knowledge engineers. The knowledge engineer must make sure that the computer has all the knowledge needed to solve a problem. The knowledge engineer must choose one or more forms in which to represent the required knowledge as symbol patterns in the memory of the computer -- that is, he (or she) must choose a knowledge representation. He must also ensure that the computer can use the knowledge efficiently by selecting from a handful of reasoning methods. The practice of knowledge engineering is described later. We first describe the components of expert systems.