Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Media Statistika Statistika Jurnal Ketahanan Nasional Indonesian Journal of Statistics and Its Applications Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Septiadi Padmadisastra
Departemen Statistik, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Padjajaran
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics Other

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Media Statistika

 1 
ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI Yogo Aryo Jatmiko; Septiadi Padmadisastra; Anna Chadidjah
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (403.528 KB) | DOI: 10.14710/medstat.12.1.1-12

Abstract

The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. Results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.
Co-Authors Anna Chadidjah Bernik Maskun Bertho Tantular Bertho Tantular Denta Anggakusuma Herlin Venny Johannes Irtania Muthia Rizki Jadi Suprijadi Reny Rian Marliana Yogo Aryo Jatmiko