Komparasi Algoritme Random Forest dan XGBoosting dalam Klasifikasi Performa UMKM - Moh Erkamim; Suswadi Suswadi; Muhammad Zidni Subarkah; Erni Widarti (Jurnal Sistem Informasi Bisnis, 2023)
The Covid-19 pandemic has greatly impacted the whole world, especially Indonesia. Various policies have been implemented starting from the implementation of lockdowns, restrictions on large-scale economic activities, and bans from leaving the region. The economic sector is a sector that has been affected quite a lot, one of which is Micro, Small, and Medium Enterprises (MSMEs). As a result of the Covid-19 pandemic, many MSMEs have suffered losses, so many investors have started to consider investing in MSMEs. Therefore, MSMEs need to know their business performance through potential analysis and financial reports to deal with the economic crisis during a pandemic. This study compares two algorithms namely Random Forest and XGBoosting in classifying the good or bad performance of MSME financial conditions. The performance of the developed algorithm will be improved using hyperparameter tuning to obtain the best parameter combination for each algorithm. In this study, the Random Forest algorithm has an accuracy value of 0.944 and an f1-score of 0.944, while the XGBoosting algorithm has an accuracy value of 0.944 and an f1-score of 0.950. Based on the model with the best evaluation metric, six important features are obtained: the 2021 profit and loss variable, 2020 cash, 2020 liabilities, 2020 capital, 2021 sales, and 2021 liabilities.