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Last updated: Jun 20, 2024
Algorithms and programming are two fundamental concepts in the world of computers and information technology.
In general, an algorithm is a series of systematic and structured instructions or steps to solve a problem. It serves as a clear, detailed, and sequential guide to achieving the desired outcome.
Programming, on the other hand, is the process of creating computer programs using a specific programming language. Programming involves translating algorithms designed by humans directly into code or scripts.
Although they may appear similar, it should be noted that fundamentally, algorithms and programming are two different concepts. So, what is an algorithm, and its differences from programming?
An algorithm is a plan that describes the steps of a solution, while programming is the concrete implementation of that plan into code.
Despite the difference between algorithm and programming, you may have heard the term "programming algorithm." In this context, a programming data structure and algorithm is the foundational basis for providing structured guidance in solving problems and translating them into executable code for computers.
This concept serves important functions in various fields, such as:
Data structure and algorithms provide the foundation for designing structured solutions to various computational problems, while programming implements these algorithms in the form of code that can be executed by computers.
Without the existence of these two concepts computer science would not be able to advance and produce the various types of software, applications, and systems that you use in your daily life.
Many algorithms use mathematical principles, such as data analysis and optimization. It is indeed a crucial aspect used in programming to implement mathematical concepts in solving computational problems.
In the world of research, the two concepts are used to model and simulate various phenomena. In this context, algorithms are used to analyze data or develop models that can predict specific phenomena.
To implement these models, programming is used to gain a deep understanding of the phenomena being researched.
Artificial Intelligence, commonly known as AI, is a modern technology field that heavily relies on algorithms and programming.
Algorithms in AI are generally used to develop machine learning models, enabling computers to learn from data and make decisions. These models are then implemented and tested using programming in various applications.
The two conceps also play a vital role in the field of data science. In this context, algorithms are used to process, analyze, and extract information from large and complex datasets.
Meanwhile, programming is used to develop analytics, design visualizations, and implement data processing techniques.
In general, the operation is divided into three main stages: input, processing, and output. Here is a complete explanation of these three stages:
The first stage involves gathering the data or information needed to solve a problem. This information can take various forms, such as numbers, text, images, or even input from the user.
In addition, this information serves as the foundation for the programming algorithm to proceed to the next stage.
Once the input is collected, the algorithm processes this information to produce the desired outcome. This process involves a series of steps to manipulate the input and generate the expected output.
The result of the processing stage yields an output in the form of an answer or solution to the problem being solved. This output can take various forms, such as numbers, text, graphics, or actions taken by the computer.
In this digital era, there are various examples of algorithms and programming used in various situations. Below is a complete explanation of some of the examples:
Brute Force is an approach that makes various efforts to solve a problem. In its implementation, this approach will perform testing to sequentially test all possibilities until the correct solution is found.
However, the brute force option can become very slow and inefficient when dealing with complex problems.
Recursive programming breaks a problem down into smaller parts with a similar pattern. In broad strokes, the type of recursive has a concept similar to solving mathematical factor tree problems.
As the name suggests, this example rely on random elements in the problem-solving process. By using random elements, this type can reduce complexity in programming code.
Examples of randomized algorithms can be found in quicksort, which is commonly used to select a pivot in the sorting process.
Sorting algorithms are used to arrange a set of data in a specific order, such as numeric or alphabetical order. Examples of these types of programming algorithms include bubble sort, insertion sort, selection sort, merge sort, and quicksort.
Another example is searching. This algorithm is used to find a specific value within a data set. Examples of searching implementations can be seen in linear search and binary search.
This type has characteristics similar to searching algorithms. However, hashing use data mapping from one form to another using a hash function.
This mapping is often used in data structures. Common examples of hashing algorithms include MD5, SHA-1, and SHA-256.
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