Klasifikasi Adopsi Berbasis Kecerdasan Buatan pada UMKM di Indonesia Menggunakan Algoritma Random Forest - Muhammad Ihsan Dirgantara; Fakhri Sepriansyah; Nulry Izzatul Maula; Farhan Daffazka; Ken Ditha Tania; Alsella Meiriza (METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi, 2026)
Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in the Indonesian economy; however, digital transformation based on artificial intelligence (AI) remains a significant challenge. This study aims to classify AI adoption among MSMEs in Indonesia using the Random Forest algorithm and to identify the factors that influence it. The dataset was obtained from the Zenodo repository, consisting of questionnaire results regarding AI adoption in MSMEs. The research stages included data cleaning, encoding, splitting the data into training (80%) and testing (20%) sets, implementing the Random Forest algorithm, evaluation, and result analysis. The evaluation results show an accuracy of 80.3% with an ROC-AUC of 0.884. The weighted precision, recall, and F1-score values are 81.2%, 80.3%, and 80.4%, respectively. These evaluation results indicate that the Random Forest algorithm performs well on this dataset. Furthermore, the feature importance analysis revealed several influential variables in AI adoption among MSMEs, including strategic decision-making (10.9%), digital leadership (8.3%), and respondent position (7.8%). In conclusion, the implementation of the Random Forest algorithm demonstrates strong performance in classifying AI adoption among MSMEs in Indonesia and highlights key influential variables such as strategic decision-making, digital leadership, and respondent position.