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, 2024)
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.