**An article which is the first version to promote the Haryadi Index to become the Formula for Everything made one and a half years ago (31 December 2017)**

## Abstract

**“Harmony in Gradation”, “Fairness over Inequality”, or “Haryadi Index” is a formula, which was found in April 2016 by Sigit Haryadi, that promoted by the author to be a “Formula for Everything”, because the formula simultaneously contains two things that seem contradictory, which “the Harmony” and “the Gradation “, therefore no one has ever thought of putting it together, while the formulas that exist in all fields of science, currently contain only one, only ” the Harmony” or “the Gradation”.**

## Explanation of Haryadi Index

The formula found in April 2016, named as “Haryadi Index”, or referred to as “Fairness over Inequality”, or “Harmony in Gradation” [1], [2], [3]. For the purposes of the wider community, the explanation of this formula, as well as the prospect of its use in various fields of science, is not used a complex mathematical proofing, but using the “Extrasensory perception” [4]. The Harmony in Gradation formula combines the “spirits” of two groups of current formulas, which I refer to as “the Harmony” and “the Gradation”. The details, “The Harmony” are the “spirits” of the Herfindahl-Hirschman Index formulas [5], Mandelbrot Set [6], the Third law of Kepler’s [7], and other formulas which illustrate the existence of an equality, justice, harmony, or equilibrium in the elements that make up a problem of social or physical problems. While “the Gradation” is a “spirit” of the Gini Coefficient formula [8], Pearson Correlation Coefficient [9], Lorentz Factor [10], and all other formulas that explain the existence of the gradations or differences between the elements of the problem social or physical problems. The formula “Harmony in Gradation Index” is written as follows:

Where: Haryadi Index or Harmony in Gradation of the Random Variable (or a Union) which has N number of sample (or subset) and S_{i} = the Share of each sample (or subset) = ratio between the strength of each sample (or subset) and the total strength of the Random Variable (or Union). Examples of Haryadi Index formula are given as follows: (a) In the random variable (or a union) consist of two elements: HI (2), (b) in the random variable (or a union) consist of three elements: HI (3), (c) in the random variable (or a union) consist of four elements: HI (4) , and so on.

#### On this website, as of December 31, 2017, four calculators have been made using the Haryadi Index as the formula, as follows:

**Competition Law**

The law of business competition [11] is used to maintain a healthy level of competition among providers, generally worldwide today still using a formula called the Herfindahl or Herfindahl-Hirschman Index (HHI), which is exactly the form of “the Harmony” of the Haryadi Index formula, unfortunately, cannot produce a consistent index for the number of providers, this is because the HHI formula does not contain the “the Gradation” element. On the other hand, HiG will provide a consistent index, since it has both “the Harmony” and “the Gradation” elements, so the resulting index is always consistent, independent of the number of providers, the index = 1 indicates a perfect level of competition, index = 0.75 always shows the boundary between fair and unbalanced level of competition, while index = 0.50 indicates the boundary between unbalanced competition level and unfair competition.

I have provides an internet-calculator, so that experts of the related sciences can examine the feasibility of the theory which is at the link address: https://www.haryadi.org/competition-level-calculation-in-the-industry/

**Fairness over Inequality**

This is the new theory that uses the Haryadi Index as its core, and is promoted in all fields of science, called as the “Fairness over Inequality” [14], where the term of the “Fairness” is assumed as “a measurable relative-equality”, is a human or governmental undertaking against a condition of “normal inequality”, in which case the human effort or a government policy is referred to “Affecting variables” and a “normal inequality” is referred to “Affected Variables”.

In other areas, this theory may also be useful. For example, in the field of physical and mental health, where there is a “Normal Inequality”, such as differences in blood pressure systole and diastole in healthy human physique, and the differences of factor id, ego and superego in the soul of healthy humans. But I have not studied what kind of human endeavor is an “affecting variable”, which must be carried out fairly, where in fair conditions there will be the most optimal solution. The HiG formula in this theory will produce an index = 1 indicating the presence of a perfect level of health, the index = 0.75 shows the boundary between good health and less unhealthy, while index = 0.50 indicates a margin between less healthy levels and poor health. I have provided an internet-calculator, so that experts of the related sciences can examine the feasibility of the theory which is at the link address: https://www.haryadi.org/fairness-over-inequality/

**Non-Intercept Linear Regression**

Haryadi Index can also solve a simple linear regression problem, especially if we believe that the regression equation should have no intercept [16], [17], where this theory has other advantages: 1. We will obtain a correlation index between independent and dependent variables, and the level of confidence of the regression equation without having to dot the t-test and/or F-test, and 2. Accurate results are independent of the size or sample population. Unfortunately, the existing formula, the Pearson Correlation Coefficient [9] only has the “gradation” element, which causes it to concentrate only on how far the deviation of the sample points from the regression equation, but does not take into account the harmony or equilibrium between the sample points, then there is a possibility that the existing theory would be misinterpreted as the measurement data, where the regression equation should not have an intercept, but is forced to have an intercept, which means the dependent variable is not zero when the independent variable = 0.

I have provides an internet-calculator, so that experts of the related sciences can examine the feasibility of the theory which is at the link address: https://www.haryadi.org/linear-regression-without-intercept/

I submit to the statistic programming experts in using HiG on a multivariate regression that has or does not have an intercept.

**Fair Resource Allocation on 5G Ultra-Dense Cellular Network**

The concept is complete, but it needs to develop a software to be installed in the gateway of a macro-cell of a 5G cellular network. The 5G Ultra-Dense Cellular Network design that on a macro-cell with the range about 1 km will be the dozens of micro-cells of eNodeB, which can simply be imagined as an existing Local Area Network which has dozens of Wi-Fi routers. Even though, maintaining the performance of the networks in good order is done by a measurement of the performance parameters of the Key Performance Indicator, so that where there any parameter that does not meet the predetermined target value, then performs a manual or automatic repair. Especially, the notebook proposes the technique for performing an automatic performance improvements without measure the performance parameter, i.e. is to make a balanced allocation of resources, which, in the context of the 5G Ultra-Dense Cellular Network, is defined as an attempt to enable eNodeB in a macro-cell to have a balance of load in the form of a ratio between communication traffic and bandwidth of all eNodeBs since there is a consistent correlation between the index produced by HiG formulas and the network performance at which the network operates [12], [13] in detail, by keeping the index of HiG is close to 1, at least not less than 0.75. Thus, the process of maintaining the stability of network performance will more practical than the currently common procedure of measuring performance parameters and improving performance by altering the Grade of Service of the Network, whereas very often there is no consistent correlation between Grade of Service with Quality of Service.

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