If you never heard the word big-oh notation before and want to know what is the meaning of it, this tutorial is for you. If you still think computer can solve all the computational problems, this short tutorial is also for you. By the end of this tutorial, you will know the important of analysis of algorithm, how to understand the meaning of computer jargon such as O(n^2) or O(log n), how to measure the running time of an algorithm, how to compare the efficiency of two algorithms and how to get the estimate of the running time (without the need to run the code) and how to rank the best, good and bad algorithms and how do we measure programmer's productivity that influence your IT cost.

## Contents

Click the topics below.

Algorithm Motivating Story

Computational Complexity

Complexity Measures

Estimate Running Time

Best Algorithms based on Order of Complexity

Asymptotic Functions

Programmersâ€™ Productivity

These tutorial is copyrighted .

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How to cite this tutorial:
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Teknomo, Kardi. (2019) Analysis of Algorithm .

http://people.revoledu.com/kardi/tutorial/Algorithm/