Perbandingan efisiensi algoritma linear search dengan algoritma binary search

Authors

  • Ahmad Zamroni Zamroni Teknik Informatika, Sains dan Teknologi, Universitas Islam Negri Maulana Malik Ibrahim Malang

Keywords:

Linear Search, Binary Search, Algorithm Efficiency, Time Complexity, Space Complexity

Abstract

Linear search and binary search are two commonly used algorithms in data processing. Linear search is known for its simplicity and ability to work with unsorted datasets, while binary search is more efficient for large, sorted datasets. This study compares the efficiency of both algorithms based on time complexity, space complexity, data conditions, and dataset size. The analysis results show that linear search is more suitable for small datasets or unsorted data, whereas binary search is significantly more efficient for large, sorted datasets. By understanding the characteristics of each algorithm, users can choose the most appropriate one for their specific needs.

Downloads

Download data is not yet available.

Downloads

Published

2025-06-26

How to Cite

Zamroni, A. Z. (2025). Perbandingan efisiensi algoritma linear search dengan algoritma binary search. Maliki Interdisciplinary Journal, 3(4), 86–92. Retrieved from https://urj.uin-malang.ac.id/index.php/mij/article/view/13620

Issue

Vol. 3 No. 4 (2025): APRIL

Section

Articles

License

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.