Analysis Of Bubble Sort And Insertion Sort Algorithm On Memory Efficiency Using Data Mining Approach

research
  • 12 Aug
  • 2020

Analysis Of Bubble Sort And Insertion Sort Algorithm On Memory Efficiency Using Data Mining Approach

Sorting algorithm in the computational process makes it easy for users when the data sorting process because the data is sorted by the process quickly and automatically. In addition to speed in sorting data, memory efficiency must also be considered. In this research, a retesting of two sorting methods is conducted, namely the bubble sort method and the insertion sort method based on the comparison of two programming languages, Java with Visual Basic 2010 using the decision tree method. This research aims to find out which algorithm has lower memory consumption in the sorting process using Java or Visual Basic 2010. The results of the comparison show, in Visual Basic 2010. insertion sort algorithm which has the lowest average memory consumption of 4.3243KB for .vb extensions and 2.0145KB for .exe extensions. while the bubble sort method with a consumption amount of 4.4358KB for the .vb extension and 2.0352 for extension.exe. Furthermore, if you use the Java programming language. So the bubble sort method still consumes the highest average memory, which is 546,242KB for the .jar extension and 4,337KB for the .exe extension, whereas from the insertion sort method, which has a low average memory consumption of 543,578 KB for extension .jar, and 4,381KB for extension .exe.

Unduhan

 

  • 1165-Article Text-2958-1-10-20200504.pdf

    Paper Pilar Nusa Mandiri - Analysis Of Bubble Sort And Insertion Sort Algorithm On Memory Efficiency Using Data Mining Approach

    •   diunduh 763x | Ukuran 1,085,119

REFERENSI

Badrul, M. (2014). Perbandingan Algoritma C4.5 Dan Neural Network Untuk Memprediksi Hasil Pemilu Legislatif DKI Jakarta. Juurnal Pilar Nusa Mandiri, 10(2), 127–138. https://doi.org/10.33480/pilar.v10i2.470

Iskandar, I. D. (2019). Parents’ Sum of Salaries Analyses towards School Tuition Fee Arrears Potential with Decision Tree Method. Indonesian Journal of Information Systems, 2(1), 45. https://doi.org/10.24002/ijis.v2i1.2168

Iskandar, I. D., Amirulloh, I., Pertiwi, M. W., Kusmira, M., Hikmah, A. B., & Supriadi, D. (2020). Laporan Akhir Penelitian Mandiri: Analisis Algoritma Bubble Sort Dan Insertion Sort Terhadap Efisiensi Memori Menggunakan Pendekatan Data Mining.

Jiang, H., & Wang, R. (2016). Fault mode prediction based on decision tree. IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), 1729–1733. https://doi.org/10.1109/IMCEC.2016.7867514

Rahayu, E. S., Wahono, R. S., & Supriyanto, C. (2015). Penerapan Metode Average Gain, Threshold Pruning dan Cost. Journal of Intelligent Systems, 1(2), 91–97. http://www.journal.ilmukomputer.org/index.php?journal=jis&page=article&op=view&path%5B%5D=80

Saptadi, A. H., & Sari, D. W. (2012). Analisis Algoritma Insertion Sort, Merge Sort Dan Implementasinya Dalam Bahasa Pemrograman C++. JURNAL INFOTEL - Informatika Telekomunikasi Elektronika, 4(2), 10. https://doi.org/10.20895/infotel.v4i2.103

Suryani, D. (2013). Perbandingan Metode Bubble Sort dan Insertion Sort Terhadap Efisiensi Memori. Jurnal Teknologi Informasi & Pendidikan, 6(1), 146–162.

Sutoyo, I. (2018). Implementasi Algoritma Decision Tree Untuk Klasifikasi Data Peserta Didik. Jurnal Pilar Nusa Mandiri, 14(2), 217. https://doi.org/10.33480/pilar.v14i2.926

Tjaru, S. N. B. (2009). Kompleksitas Algoritma Pengurutan Selection Sort dan Insertion Sort. Makalah IF2091 Strategi Algoritmik Tahun 2009. http://informatika.stei.itb.ac.id/~rinaldi.munir/Matdis/2009-2010/Makalah0910/MakalahStrukdis0910-074.pdf

Vijay Kotu, & Deshpande, B. (2015). Predictive Analytics and Data Mining: Concepts and Practice with Rapidminer. Elsevier Inc. https://doi.org/10.1016/C2014-0-00329-2