## Sabtu, 28 Januari 2012

### Gini coefficient

The Gini coefficient (also known as the Gini index or Gini ratio) is a measure of statistical dispersion developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper "Variability and Mutability" (Italian: Variabilità e mutabilità).[1][2]
The Gini coefficient measures the inequality among values of a frequency distribution (for example levels of income). A Gini coefficient of zero expresses perfect equality where all values are the same (for example, where everyone has an exactly equal income). A Gini coefficient of one (100 on the percentile scale) expresses maximal inequality among values (for example where only one person has all the income).[3]
It has found application in the study of inequalities in disciplines as diverse as sociology, economics, health science, ecology, chemistry, engineering and agriculture.[4] It is commonly used as a measure of inequality of income or wealth.[5] Worldwide, Gini coefficients for income range from approximately 0.23 (Sweden) to 0.70 (Namibia) although not every country has been assessed.

## Definition

Graphical representation of the Gini coefficient

The graph shows that the Gini is equal to the area marked 'A' divided by the sum of the areas marked 'A' and 'B' (that is, Gini = A/(A+B)). It is also equal to 2*A, as A+B = 0.5 (since the axes scale from 0 to 1).
The Gini coefficient is usually defined mathematically based on the Lorenz curve, which plots the proportion of the total income of the population (y axis) that is cumulatively earned by the bottom x% of the population (see diagram). The line at 45 degrees thus represents perfect equality of incomes. The Gini coefficient can then be thought of as the ratio of the area that lies between the line of equality and the Lorenz curve (marked 'A' in the diagram) over the total area under the line of equality (marked 'A' and 'B' in the diagram); i.e., G=A/(A+B).
The Gini coefficient can range from 0 to 1; it is sometimes expressed as a percentage ranging between 0 and 100. More specifically, the upper bound of the Gini coefficient equals 1 only in populations of infinite size. In a population of size N, the upper bound is equal to 1 − 2 / (N + 1).
A low Gini coefficient indicates a more equal distribution, with 0 corresponding to complete equality, while higher Gini coefficients indicate more unequal distribution, with 1 corresponding to complete inequality. To be validly computed, no negative goods can be distributed. Thus, if the Gini coefficient is being used to describe household income inequality, then no household can have a negative income. When used as a measure of income inequality, the most unequal society will be one in which a single person receives 100% of the total income and the remaining people receive none (G=1); and the most equal society will be one in which every person receives the same income (G=0).
An alternative approach would be to consider the Gini coefficient as half of the relative mean difference, which is a mathematical equivalence. The mean difference is the average absolute difference between two items selected randomly from a population, and the relative mean difference is the mean difference divided by the average, to normalize for scale.

## Calculation

The Gini index is defined as a ratio of the areas on the Lorenz curve diagram. If the area between the line of perfect equality and the Lorenz curve is A, and the area under the Lorenz curve is B, then the Gini index is A/(A+B). Since A+B = 0.5, the Gini index, G = A/(0.5) = 2A = 1-2B. If the Lorenz curve is represented by the function Y = L(X), the value of B can be found with integration and:
$G = 1 - 2\,\int_0^1 L(X) dX.$
In some cases, this equation can be applied to calculate the Gini coefficient without direct reference to the Lorenz curve. For example:
• For a population uniform on the values yi, i = 1 to n, indexed in non-decreasing order ( yiyi+1):
$G = \frac{1}{n}\left ( n+1 - 2 \left ( \frac{\Sigma_{i=1}^n \; (n+1-i)y_i}{\Sigma_{i=1}^n y_i} \right ) \right )$
This may be simplified to:
$G = \frac{2 \Sigma_{i=1}^n \; i y_i}{n \Sigma_{i=1}^n y_i} -\frac{n+1}{n}$
• For a discrete probability function f(y), where yi, i = 1 to n, are the points with nonzero probabilities and which are indexed in increasing order ( yi < yi+1):
$G = 1 - \frac{\Sigma_{i=1}^n \; f(y_i)(S_{i-1}+S_i)}{S_n}$
where
$S_i = \Sigma_{j=1}^i \; f(y_j)\,y_j\,$ and $S_0 = 0\,$
• For a cumulative distribution function F(y) that is piecewise differentiable, has a mean μ, and is zero for all negative values of y:
$G = 1 - \frac{1}{\mu}\int_0^\infty (1-F(y))^2dy = \frac{1}{\mu}\int_0^\infty F(y)(1-F(y))dy$
• Since the Gini coefficient is half the relative mean difference, it can also be calculated using formulas for the relative mean difference. For a random sample S consisting of values yi, i = 1 to n, that are indexed in non-decreasing order ( yiyi+1), the statistic:
$G(S) = \frac{1}{n-1}\left (n+1 - 2 \left ( \frac{\Sigma_{i=1}^n \; (n+1-i)y_i}{\Sigma_{i=1}^n y_i}\right ) \right )$
is a consistent estimator of the population Gini coefficient, but is not, in general, unbiased. Like G, G(S) has a simpler form:
$G(S) = 1 - \frac{2}{n-1}\left ( n - \frac{\Sigma_{i=1}^n \; iy_i}{\Sigma_{i=1}^n y_i}\right )$.
There does not exist a sample statistic that is in general an unbiased estimator of the population Gini coefficient, like the relative mean difference.
For some functional forms, the Gini index can be calculated explicitly. For example, if y follows a lognormal distribution with the standard deviation of logs equal to σ, then $G = 2 \Phi(\sigma/\sqrt{2})-1$ where Φ() is the cumulative distribution function of the standard normal distribution.
Sometimes the entire Lorenz curve is not known, and only values at certain intervals are given. In that case, the Gini coefficient can be approximated by using various techniques for interpolating the missing values of the Lorenz curve. If ( X k, Yk ) are the known points on the Lorenz curve, with the X k indexed in increasing order ( X k - 1 < X k ), so that:
• Xk is the cumulated proportion of the population variable, for k = 0,...,n, with X0 = 0, Xn = 1.
• Yk is the cumulated proportion of the income variable, for k = 0,...,n, with Y0 = 0, Yn = 1.
• Yk should be indexed in non-decreasing order (Yk>Yk-1)
If the Lorenz curve is approximated on each interval as a line between consecutive points, then the area B can be approximated with trapezoids and:
$G_1 = 1 - \sum_{k=1}^{n} (X_{k} - X_{k-1}) (Y_{k} + Y_{k-1})$
is the resulting approximation for G. More accurate results can be obtained using other methods to approximate the area B, such as approximating the Lorenz curve with a quadratic function across pairs of intervals, or building an appropriately smooth approximation to the underlying distribution function that matches the known data. If the population mean and boundary values for each interval are also known, these can also often be used to improve the accuracy of the approximation.
The Gini coefficient calculated from a sample is a statistic and its standard error, or confidence intervals for the population Gini coefficient, should be reported. These can be calculated using bootstrap techniques but those proposed have been mathematically complicated and computationally onerous even in an era of fast computers. Ogwang (2000) made the process more efficient by setting up a “trick regression model” in which the incomes in the sample are ranked with the lowest income being allocated rank 1. The model then expresses the rank (dependent variable) as the sum of a constant A and a normal error term whose variance is inversely proportional to yk;
$k = A + \ N(0, s^{2}/y_k)$
Ogwang showed that G can be expressed as a function of the weighted least squares estimate of the constant A and that this can be used to speed up the calculation of the jackknife estimate for the standard error. Giles (2004) argued that the standard error of the estimate of A can be used to derive that of the estimate of G directly without using a jackknife at all. This method only requires the use of ordinary least squares regression after ordering the sample data. The results compare favorably with the estimates from the jackknife with agreement improving with increasing sample size. The paper describing this method can be found here: http://web.uvic.ca/econ/ewp0202.pdf
However it has since been argued that this is dependent on the model’s assumptions about the error distributions (Ogwang 2004) and the independence of error terms (Reza & Gastwirth 2006) and that these assumptions are often not valid for real data sets. It may therefore be better to stick with jackknife methods such as those proposed by Yitzhaki (1991) and Karagiannis and Kovacevic (2000). The debate continues.
The Gini coefficient can be calculated if you know the mean of a distribution, the number of people (or percentiles), and the income of each person (or percentile). Princeton development economist Angus Deaton (1997, 139) simplified the Gini calculation to one easy formula:
$G = \frac{N+1}{N-1}-\frac{2}{N(N-1)u}(\Sigma_{i=1}^n \; P_iX_i)$
where u is mean income of the population, Pi is the income rank P of person i, with income X, such that the richest person receives a rank of 1 and the poorest a rank of N. This effectively gives higher weight to poorer people in the income distribution, which allows the Gini to meet the Transfer Principle.

## Generalised inequality index

The Gini coefficient and other standard inequality indices reduce to a common form. Perfect equality—the absence of inequality—exists when and only when the inequality ratio, $r_j = x_j / \overline{x}$, equals 1 for all j units in some population; for example, there is perfect income equality when everyone’s income xj equals the mean income $\overline{x}$, so that rj = 1 for everyone). Measures of inequality, then, are measures of the average deviations of the rj = 1 from 1; the greater the average deviation, the greater the inequality. Based on these observations the inequality indices have this common form:[6]
$\mathrm{Inequality} = \Sigma_j \, p_j \, f(r_j)\, ,$
where pj weights the units by their population share, and f(rj) is a function of the deviation of each unit’s rj from 1, the point of equality. The insight of this generalised inequality index is that inequality indices differ because they employ different functions of the distance of the inequality ratios (the rj) from 1.

## Gini coefficient of income distributions

While developed European nations and Canada tend to have Gini indices between 0.24 and 0.36, the United States' and Mexico's Gini indices are both above 0.40, indicating that the United States (according to the US Census Bureau[7][8][9]) and Mexico have greater inequality. Using the Gini can help quantify differences in welfare and compensation policies and philosophies. However it should be borne in mind that the Gini coefficient can be misleading when used to make political comparisons between large and small countries (see criticisms section).
The Gini index for the entire world has been estimated by various parties to be between 0.56 and 0.66.[10][11] The graph shows the values expressed as a percentage, in their historical development for a number of countries.

### EU Gini index

In 2005 the Gini index for the EU was estimated at 31.[12]

### US income Gini indices over time

Gini indices for the United States at various times, according to the US Census Bureau:[13][14][15]
• 1929: 45.0 (estimated)
• 1947: 37.6 (estimated)
• 1967: 39.7 (first year reported)
• 1968: 38.6 (lowest index reported)
• 1970: 39.4
• 1980: 40.3
• 1990: 42.8
• 2000: 46.2
• 2005: 46.9
• 2006: 47.0 (highest index reported)
• 2007: 46.3
• 2008: 46.69
• 2009: 46.8

### Advantages of Gini coefficient as a measure of inequality

The Gini coefficient's main advantage is that it is a measure of inequality by means of a ratio analysis. This makes it easily interpretable, and avoids references to a statistical average or position unrepresentative of most of the population, such as per capita income or gross domestic product. The simplicity of Gini makes it easy to use for comparison across diverse countries and also allows comparison of income distributions across different groups as well as countries; for example the Gini coefficient for urban areas differs from that of rural areas in many countries (though not in the United States). Like any time-based measure, Gini coefficients can be used to compare income distribution over time, thus it is possible to see if inequality is increasing or decreasing independent of absolute incomes. The Gini coefficient satisfies four principles suggested to be important:[16]
• Anonymity: it does not matter who the high and low earners are.
• Scale independence: the Gini coefficient does not consider the size of the economy, the way it is measured, or whether it is a rich or poor country on average.
• Population independence: it does not matter how large the population of the country is.
• Transfer principle: if income (less than the difference), is transferred from a rich person to a poor person the resulting distribution is more equal.

### Disadvantages of Gini coefficient as a measure of inequality

The limitations of Gini largely lie in its relative nature: It loses information about absolute national and personal incomes. Countries may have identical Gini coefficients, but differ greatly in wealth. Basic necessities may be equal (available to all) in a rich country, while in the poor country, even basic necessities are unequally available.
By measuring inequality in income, the Gini ignores the differential efficiency of use of household income. By ignoring wealth (except as it contributes to income) the Gini can create the appearance of inequality when the people compared are at different stages in their life. Wealthy countries (e.g. Sweden) can appear more equal, yet have high Gini coefficients for wealth (for instance 77% of the share value owned by households is held by just 5% of Swedish shareholding households).[17] These factors are not assessed in income-based Gini.
Gini has some mathematical limitations as well. For instance, different sets of people cannot be averaged to obtain the Gini coefficient of all the people in the sets: if a Gini coefficient were to be calculated for each person it would always be zero. For a large, economically diverse country, a much higher coefficient will be calculated for the country as a whole than will be calculated for each of its regions. (The coefficient is usually applied to measurable nominal income rather than local purchasing power, tending to increase the calculated coefficient across larger areas.)
As is the case for any single measure of a distribution, economies with similar incomes and Gini coefficients can still have very different income distributions. This results from differing shapes of the Lorenz curve. For example, consider a society where half of individuals had no income and the other half shared all the income equally (i.e. whose Lorenz curve is linear from (0,0) to (0.5,0) and then linear to (1,1)). As is easily calculated, this society has Gini coefficient 0.5 -- the same as that of a society in which 75% of people equally shared 25% of income while the remaining 25% equally shared 75% (i.e. whose Lorenz curve is linear from (0,0) to (0.75,0.25) and then linear to (1,1)).
Too often only the Gini coefficient is quoted without describing the proportions of the quantiles used for measurement. As with other inequality coefficients, the Gini coefficient is influenced by the granularity of the measurements. For example, five 20% quantiles (low granularity) will usually yield a lower Gini coefficient than twenty 5% quantiles (high granularity) taken from the same distribution. This is an often encountered problem with measurements.
Care should be taken in using the Gini coefficient as a measure of egalitarianism, as it is properly a measure of income dispersion. For example, if two equally egalitarian countries pursue different immigration policies, the country accepting a higher proportion of low-income or impoverished migrants will be assessed as less equal (gain a higher Gini coefficient).
Expanding on the importance of life-span measures, the Gini coefficient as a point-estimate of equality at a certain time, ignores life-span changes in income. Typically, increases in the proportion of young or old members of a society will drive apparent changes in equality, simply because people generally have lower incomes and wealth when they are young than when they are old. Because of this, factors such as age distribution within a population and mobility within income classes can create the appearance of differential equality when none exist taking into account demographic effects. Thus a given economy may have a higher Gini coefficient at any one point in time compared to another, while the Gini coefficient calculated over individuals' lifetime income is actually lower than the apparently more equal (at a given point in time) economy's.[18] Essentially, what matters is not just inequality in any particular year, but the composition of the distribution over time.

#### General problems of measurement

• Comparing income distributions among countries may be difficult because benefits systems may differ. For example, some countries give benefits in the form of money while others give food stamps, which might not be counted by some economists and researchers as income in the Lorenz curve and therefore not taken into account in the Gini coefficient. The Soviet Union was measured to have relatively high income inequality: by some estimates, in the late 1970s, Gini coefficient of its urban population was as high as 0.38,[19] which is higher than many Western countries today. This number would not reflect those benefits received by Soviet citizens that were not monetized for measurement, which may include child care for children as young as two months, elementary, secondary and higher education, cradle-to-grave medical care, and heavily subsidized or provided housing. In this example, a more accurate comparison between the 1970s Soviet Union and Western countries may require one to assign monetary values to all benefits – a difficult task in the absence of free markets. Similar problems arise whenever a comparison between more liberalized economies and partially socialist economies is attempted. Benefits may take various and unexpected forms: for example, major oil producers such as Venezuela and Iran provide indirect benefits to its citizens by subsidizing the retail price of gasoline.
• Similarly, in some societies people may have significant income in other forms than money, for example through subsistence farming or bartering. Like non-monetary benefits, the value of these incomes is difficult to quantify. Different quantifications of these incomes will yield different Gini coefficients.
• The measure will give different results when applied to individuals instead of households. When different populations are not measured with consistent definitions, comparison is not meaningful.
• As for all statistics, there may be systematic and random errors in the data. The meaning of the Gini coefficient decreases as the data become less accurate. Also, countries may collect data differently, making it difficult to compare statistics between countries.
As one result of this criticism, in addition to or in competition with the Gini coefficient entropy measures are frequently used (e.g. the Theil Index and the Atkinson index). These measures attempt to compare the distribution of resources by intelligent agents in the market with a maximum entropy random distribution, which would occur if these agents acted like non-intelligent particles in a closed system following the laws of statistical physics.

#### Credit risk

The Gini coefficient is also commonly used for the measurement of the discriminatory power of rating systems in credit risk management.
The discriminatory power refers to a credit risk model's ability to differentiate between defaulting and non-defaulting clients. The above formula G1 may be used for the final model and also at individual model factor level, to quantify the discriminatory power of individual factors. This is as a result of too many non defaulting clients falling into the lower points scale e.g. factor has a 10 point scale and 30% of non defaulting clients are being assigned the lowest points available e.g. 0 or negative points. This indicates that the factor is behaving in a counter-intuitive manner and would require further investigation at the model development stage.[20]

## Relation to other statistical measures

Gini coefficient closely related to the AUC (Area Under receiver operating characteristic Curve) measure of performance[21]. The relation follows the formula AUC = (G + 1) / 2. Gini coefficient is also closely related to Mann–Whitney U.

## Other uses

Although the Gini coefficient is most popular in economics, it can in theory be applied in any field of science that studies a distribution. For example, in ecology the Gini coefficient has been used as a measure of biodiversity, where the cumulative proportion of species is plotted against cumulative proportion of individuals.[22] In health, it has been used as a measure of the inequality of health related quality of life in a population.[23] In education, it has been used as a measure of the inequality of universities.[24] In chemistry it has been used to express the selectivity of protein kinase inhibitors against a panel of kinases.[25] In engineering, it has been used to evaluate the fairness achieved by Internet routers in scheduling packet transmissions from different flows of traffic.[26] In statistics, building decision trees, it is used to measure the purity of possible child nodes, with the aim of maximising the average purity of two child nodes when splitting, and it has been compared with other equality measures.[27]

### Sejarah Komputer dari dulu hingga sekarang

Sejarah Komputer dan Perkembanganya – Sejak dahulu, proses pengolahan data telah dilakukan oleh manusia. Manusia juga menemukan alat-alat mekanik dan elektronik untuk membantu manusia dalam penghitungan dan pengolahan data supaya bisa mendapatkan hasil lebih cepat. Komputer yang kita temui saat ini adalah suatu evolusi panjang dari penemuan-penemuan manusia sejak dahulu kala berupa alat mekanik maupun elektronik
Saat ini komputer dan piranti pendukungnya telah masuk dalam setiap aspek kehidupan dan pekerjaan. Komputer yang ada sekarang memiliki kemampuan yang lebih dari sekedar perhitungan matematik biasa. Diantaranya adalah sistem komputer di kassa supermarket yang mampu membaca kode barang belanja, sentral telepon yang menangani jutaan panggilan dan komunikasi, jaringan komputer dan internet yang menghubungkan berbagai tempat di dunia.
• Alat Hitung Tradisional dan Kalkulator Mekanik
• Komputer Generasi Pertama
• Komputer Generasi Kedua
• Komputer Generasi Ketiga
• Komputer Generasi Keempat
• Komputer Generasi Kelima

1. Komputer Generasi Pertama (1946­ – 1959)
Dengan terjadinya Perang Dunia II, negara­negara yang terlibat dalam perang tersebut berusaha mengembangkan untuk mengeksploit potensi strategis yang dimiliki komputer.
Hal ini meningkatkan pendanaan pengembangan komputer serta mempercepat kemajuan teknik komputer.
(1) Colassus
(2) Mark I
(3) ENIAC
(4) EDVAC
(5) UNIVAC I
- Penggunaan tube vakum (yang membuat komputer pada masa tersebut berukuran sangat besar)
- Adanya silinder magnetik untuk penyimpanan data.
- Instruksi operasi dibuat secara spesifik untuk suatu tugas tertentu.
- Setiap komputer memiliki program kode­biner yang berbeda yang disebut “bahasa mesin” (machine language). Hal ini menyebabkan komputer sulit untuk diprogram dan membatasi kecepatannya.
2. Komputer Generasi Kedua (1959­ – 1964)
Stretch dan LARC
Mesin pertama yang memanfaatkan teknologi baru ini adalah superkomputer. IBM membuat superkomputer bernama Stretch, dan Sprery­ Rand membuat komputer bernama LARC. Komputer­komputer ini,yang dikembangkan untuk laboratorium energi atom, dapat menangani sejumlah besar data, sebuah kemampuan yang sangat dibutuhkan oleh peneliti atom. Mesin tersebut sangat mahal dan cenderung terlalu kompleks untuk kebutuhan komputasi bisnis, sehingga membatasi kepopulerannya.
Hanya ada dua LARC yang pernah dipasang dan digunakan: satu di Lawrence Radiation Labs di Livermore, California, dan yang lainnya di US Navy Research and Development Center di Washington D.C. Komputer generasi kedua menggantikan bahasa mesin dengan bahasa assembly. Bahasa assembly adalah bahasa yang menggunakan singkatan­singakatan untuk menggantikan kode biner.
Pada awal 1960­an, mulai bermunculan komputer generasi kedua yang sukses di bidang bisnis, di universitas, dan di pemerintahan. Komputer generasi kedua ini merupakan komputer yang sepenuhnya menggunakan transistor. Mereka juga memiliki komponen­komponen yang dapat diasosiasikan dengan komputer pada saat ini: printer, penyimpanan dalam disket, memory, sistem operasi, dan program.
- Penggunaan transistor sehingga ukurannya lebih kecil
- Adanya pengembangan memori inti­magnetik membantu pengembangan komputer generasi kedua yang lebih kecil, lebih cepat, lebih dapat diandalkan, dan lebih hemat energi dibanding para pendahulunya
- Penggantian dari bahasa mesin menjadi bahasa Asembly
- Muncul bahasa pemrograman COBOL dan FORTRAN
3. Komputer Generasi Ketiga (1964­ – 1970)
Walaupun transistor dalam banyak hal mengungguli tube vakum, namun transistor menghasilkan panas yang cukup besar, yang dapat berpotensi merusak bagian­bagian internal komputer. Batu kuarsa (quartz rock) menghilangkan masalah ini. Jack Kilby, seorang insinyur di Texas Instrument, mengembangkan sirkuit terintegrasi (IC : integrated circuit) di tahun 1958. IC mengkombinasikan tiga komponen elektronik dalam sebuah piringan silikon kecil yang terbuat dari pasir kuarsa.
Pada ilmuwan kemudian berhasil memasukkan lebih banyak komponen­komponen ke dalam suatu chiptunggal yang disebut semikonduktor. Hasilnya, komputer menjadi semakin kecil karena komponen­komponen dapat dipadatkan dalam chip. Kemajuan komputer generasi ketiga lainnya adalah penggunaan system operasi (operating system) yang memungkinkan mesin untuk menjalankan berbagai program yang berbeda secara serentak dengan sebuah program utama yang memonitor dan mengkoordinasi memori komputer.
- Penggunaan IC(Intregrated Circuit)
- Ukuran komputer menjadi lebih kecil
- Ditemukannya Sistem Operasi
4. Komputer Generasi Keempat (1979­ – sekarang)
Setelah IC, tujuan pengembangan menjadi lebih jelas: mengecilkan ukuran sirkuit dan komponen­komponen elektrik. Large Scale Integration (LSI) dapat memuat ratusan komponen dalam sebuah chip. Pada tahun 1980­ an, Very Large Scale Integration (VLSI) memuat ribuan komponen dalam sebuah chip tunggal. Ultra­Large Scale Integration (ULSI) meningkatkan jumlah tersebut menjadi jutaan. Kemampuan untuk memasang sedemikian banyak komponen dalam suatu keping yang berukurang setengah keping uang logam mendorong turunnya harga dan ukuran komputer. Hal tersebut juga meningkatkan daya kerja, efisiensi dan keterandalan komputer.
Chip Intel 4004 yang dibuat pada tahun 1971 membawa kemajuan pada IC dengan meletakkan seluruh komponen dari sebuah komputer (central processing unit, memori, dan kendali input/output) dalam sebuah chip yang sangat kecil. Sebelumnya, IC dibuat untuk mengerjakan suatu tugas tertentu yang spesifik. Sekarang, sebuah mikroprosesor dapat diproduksi dan kemudian diprogram untuk memenuhi seluruh kebutuhan yang diinginkan. Tidak lama kemudian, setiap perangkat rumah tangga seperti microwave oven, televisi, dan mobil dengan electronic fuel injection dilengkapi dengan mikroprosesor.
Perkembangan yang demikian memungkinkan orang­orang biasa untuk menggunakan komputer biasa. Komputer tidak lagi menjadi dominasi perusahaan­perusahaan besar atau lembaga pemerintah. Pada pertengahantahun 1970­an, perakit komputer menawarkan produk komputer mereka ke masyarakat umum. Komputer­komputer ini, yang disebut minikomputer, dijual dengan paket piranti lunak yang mudah digunakan oleh kalangan awam. Piranti lunak yang paling populer pada saat itu adalah program word processing dan spreadsheet. Pada awal 1980­an, video game seperti Atari 2600 menarik perhatian konsumen pada komputer rumahan yang lebih canggih dan dapat diprogram.
Pada tahun 1981, IBM memperkenalkan penggunaan Personal Computer (PC) untuk penggunaan di rumah, kantor, dan sekolah. Jumlah PC yang digunakan melonjak dari 2 juta unit di tahun 1981 menjadi 5,5 juta unit di tahun 1982. Sepuluh tahun kemudian, 65 juta PC digunakan. Komputer melanjutkan evolusinya menuju ukuran yang lebih kecil, dari komputer yang berada di atas meja (desktop computer) menjadi komputer yang dapat dimasukkan ke dalam tas (laptop), atau bahkan komputer yang dapat digenggam (palmtop).
IBM PC bersaing dengan Apple Macintosh dalam memperebutkan pasar komputer. Apple Macintosh menjadi terkenal karena mempopulerkan system grafis pada komputernya, sementara saingannya masih menggunakan komputer yang berbasis teks. Macintosh juga mempopulerkan penggunaan piranti mouse.
Pada masa sekarang, kita mengenal perjalanan IBM compatible dengan pemakaian CPU: IBM PC/486, Pentium, Pentium II, Pentium III, Pentium IV (Serial dari CPU buatan Intel). Juga kita kenal AMD k6, Athlon, dsb. Ini semua masuk dalam golongan komputer generasi keempat. Seiring dengan menjamurnya penggunaan komputer di tempat kerja, cara­ cara baru untuk menggali potensial terus dikembangkan. Seiring dengan bertambah kuatnya suatu komputer kecil, komputer­komputer tersebut dapat dihubungkan secara bersamaan dalam suatu jaringan untuk saling berbagi memori, piranti lunak, informasi, dan juga untuk dapat saling berkomunikasi satu dengan yang lainnya. Komputer jaringan memungkinkan komputer tunggal untuk membentuk kerjasama elektronik untuk menyelesaikan suatu proses tugas. Dengan menggunakan perkabelan langsung (disebut juga local area network, LAN), atau kabel telepon, jaringan ini dapat berkembang menjadi sangat besar.