Analisis Perkembangan Machine Learning di Indonesia Berdasarkan Publikasi Ilmiah - Oktav Kornelius Hutagaol; Judea Tirta Jordan Simamora; Alex Septama Sihite; Marcel Alezandro Sihombing (Fakultas Teknik Universitas Persada Indonesia YAI, 2026)
Machine Learning is one of the fastest-growing branches of Artificial Intelligence and has been widely applied in various sectors, including education, healthcare, agriculture, industry, finance, and government. The increasing number of scientific publications indicates that this technology is increasingly utilized as a data-driven decision-making solution. This study aims to analyze the development of Machine Learning research in Indonesia based on scientific publications published in national and international journals. The research employs the Systematic Literature Review (SLR) method by identifying, selecting, evaluating, and synthesizing relevant scientific articles. The literature search was conducted through several academic databases, including Google Scholar, Garuda, SINTA, IEEE Xplore, and Scopus, using predetermined inclusion and exclusion criteria. The findings reveal that the number of Machine Learning publications in Indonesia has consistently increased over recent years. Education, healthcare, and industry are the dominant application areas, while the most frequently used algorithms include Decision Tree, Random Forest, Naïve Bayes, Support Vector Machine, and K-Nearest Neighbor. Despite its significant potential to support digital transformation, the development of Machine Learning in Indonesia still faces several challenges, including limited datasets, data quality, computational infrastructure, and the availability of skilled human resources. This study is expected to provide valuable references for researchers and practitioners in understanding the research trends and future directions of Machine Learning development in Indonesia. Keyword : Machine Learning, Artificial Intelligence, Systematic Literature Review, Scientific Publication, Indonesia.