Analisis Komparatif Performa Algoritma Quick Sort dan Selection Sort pada Dataset Numerik Riil Harga Perangkat Komputer
DOI:
https://doi.org/10.69714/63gs0968Keywords:
Quick Sort, Selection Sort, Data Sorting, Kaggle Dataset, Time ComplexityAbstract
Data processing efficiency is a big problem in today's computer science. This study looks at how Quick Sort and Selection Sort perform when applied to a real set of numbers taken from a computer (laptop) price dataset on Kaggle. The approach used is a quantitative experiment that relies on computer simulations written in the Java programming language. Testing was done by changing the size of the dataset to 1,000 rows, 5,000 rows, and 10,000 rows, all under normal random conditions. The experimental results show that Quick Sort performs much better as the data size increases. It takes 2 ms when there are 1,000 items, 8 ms for 5,000 items, and 25 ms for 10,000 items. These times match closely with the average time complexity of O (N log N). On the other hand, Selection Sort saw a huge increase in time, which matches its quadratic complexity of O(N2), taking 4,310 ms when $N=10,000$. In summary, algorithms that use the divide-and-conquer approach, such as Quick Sort, are much better and strongly suggested for handling large real-world data sets than older methods like Selection Sort.
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