Analisis Penerapan Algoritma Decision Tree, Random Forest, dan Naive Bayes pada Pengolahan Data untuk Sistem Cerdas - Judea Tirta Jordan Simamora; Alex Septama Sihite; Octav Kornelius Hutagaol; Marcel Alezandro Sihombing (Fakultas Teknik Universitas Persada Indonesia YAI, 2026)
The rapid growth of information technology has led to a significant increase in the volume of data generated across various sectors. This condition creates challenges in data processing, particularly in obtaining accurate, efficient, and timely information to support intelligent decision-making. Machine learning, as a branch of artificial intelligence, has emerged as one of the most widely used approaches for addressing these challenges due to its ability to learn patterns from data and generate predictions automatically. However, the successful implementation of machine learning is influenced by several factors, including data quality, algorithm selection, and model evaluation processes. This study aims to analyze the application of machine learning in data processing for intelligent systems through a literature review approach. The research method was conducted by collecting, selecting, reviewing, and synthesizing relevant scientific literature from journals, books, conference proceedings, and academic publications. The analysis employed a qualitative descriptive approach to identify commonly used algorithms, implementation benefits, challenges, and recent development trends in machine learning applications. The results indicate that algorithms such as Decision Tree, Random Forest, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naive Bayes are among the most frequently applied methods in intelligent data processing systems. The findings also show that machine learning contributes significantly to improving prediction accuracy, accelerating data analysis, automating decision-making processes, and enhancing system adaptability across various domains, including healthcare, education, business, finance, and information systems. Nevertheless, several challenges remain, such as low-quality datasets, imbalanced data, high computational requirements, overfitting, underfitting, and limited model interpretability. This study contributes by providing a comprehensive overview of machine learning applications, highlighting the importance of data preprocessing and appropriate algorithm selection, and identifying future research opportunities related to efficient, transparent, and explainable intelligent systems. Keywords : Machine Learning, Data Processing, Intelligent Systems, Artificial Intelligence.