Measuring entropy of a uniform distribution source What is entropy? I do not understand it at all.  One article states:

When there is an equal chance for all items to appear, we have a
uniform distribution. In uniform distribution the entropy is high.

I'm confused. If entropy is high, we should consider that disorder is high too, but in a uniform distribution we don't have disorder because all items have the same chance to appear.
Is it possible for someone to explain entropy in cryptography and make this concept clear for me?
In cryptography and information theory, Shannon entropy $H(X)$ is a characteristic of a source $X$ of discrete symbols; e.g. a characteristic of a dice. It's usually expressed per symbol produced, and with unit the bit (per symbol). It tells the expected/average number of bits necessary to encode the value of a symbol, knowing the characteristics of the source. In particular, the entropy of a known¹ deterministic source (such as a Pseudo Random Number Generator with known¹ seed) is $0$.
Assuming the symbols can take values $x_i$, are produced independently at random (that is, earlier outcomes have no influence on later ones), and each symbol $x_i$ has fixed probability $p(x_i)\ge0$ with $1=\sum p(x_i)$, then
$$H(X)=\ \sum_{i\text{ with }p(x_i)\ne0}p_i\,\log_2\frac1{p(x_i)}$$
For example, a fair dice has entropy $H(X)=6\,\left(\frac16\log_26\right)=\log_26\approx2.585\ldots\,$bit/symbol. Another dice, biased towards generating 6 with probability $1/5$, and the 5 other sides having probability $4/25$, has $H(X)=\frac15\log_25\,+5\,\left(\frac4{25}\log_2\frac{25}4\right)\approx2.579\ldots\,$bit/symbol.
This illustrate the fact that for a given number $n$ of symbols, entropy per symbol is maximal at $\log_2 n\,$bit/symbol when all the symbols have the same probability, that is uniform distribution. The article mentioned in the question has it right!
Intuitive justification: when things have a high probability to be in a corner (uneven distribution), there is less disorder/entropy than when they are spread evenly all around. In the later case it takes more information to describe where each thing is.

¹ What happens if we remove known is a matter of perspective. Any PRNG implementable with finite resources has zero entropy, for we can define all it's output with a finite amount of data. On the other hand, for practical adversaries, a Cryptographically Secure PRNG that outputs integers symbols in $[0,n)$ is indistinguishable from a true RNG with entropy of $\log_2 n\,$bit per symbol and the same output domain.