Perbandingan efisiensi algoritma linear search dengan algoritma binary search
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.
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References
Pratama, M. A. (2020). Analisis perbandingan algoritma linear dan binary pada aplikasi kamus bahasa Indonesia-Jawa. Program Studi Teknik Informatika. Universitas Islam Negeri Syarif Hidayatullah Jakarta.
Saputra, M. A. W., Faisal, M., & Kusumawati, R. (2023). K-Means binary search centroid with dynamic cluster for Java island health clustering. Jurnal Riset Informatika, 5 (3). Pp. 539-546. ISSN 26561735. http://repository.uin-malang.ac.id/15072/
Situmorang, H. (2018). Analisa Algoritma Pada Metoda Pencarian Linier, Biner Dan Interpolasi. Jurnal Mahajana Informasi, 2(2), 31–41. https://doi.org/10.51544/jurnalmi.v2i2.177
Sugiarti, T. (2024). Integrasi Teknologi Benar-benar memberikan manfaat yang maksimal bagi pembelajaran. PT. Pawitra Baskara Abadi Astana Jabal Sirr, Mojokerto Jawa Timur. http://repository.uin-malang.ac.id/19411/
Zaman, S., & Yaqin, M. A. (2024). Klasterisasi industri berbasis tren keterampilan teknologi menggunakan algoritma k-means clustering. http://repository.uin-malang.ac.id/22674/
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