Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Statistics and Its Applications
Indah Noor Safrida
Unknown Affiliation
Computer Science & IT Mathematics Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Application of the Random forest Method to Identify Food and Beverage Industries Experiencing Raw Material Difficulties : Penerapan Metode Random Forest untuk Mengidentifikasi Industri Makanan dan Minuman yang Mengalami Kesulitan Bahan Baku Iman Jihad Fadillah; Indah Noor Safrida; Rima Kusumaningtyas
Indonesian Journal of Statistics and Applications Vol 8 No 1 (2024)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i1p37-46

Abstract

The food and beverage industry experienced a significant increase after the pandemic. However, challenges continue to hit this industry, especially for micro and small scale businesses. To overcome this problem, the right approach is needed. One of the first steps is to provide quality data as a basis for decision making and problem solving. However, statistical activities such as censuses and surveys often face obstacles in the form of missing values. One effective method for dealing with this is using the random forest method. This research aims to use a machine learning-based imputation method, namely the random forest method, to identify micro and small scale food and beverage industries that are experiencing raw material difficulties. The research results show that the random forest method provides accurate and consistent predictions in identifying food and beverage industries experiencing raw material difficulties. However, it is also necessary to consider the relatively long computing time for implementing this method.
Co-Authors Iman Jihad Fadillah Rima Kusumaningtyas