Optimasi Klasifikasi Risiko Ibu Hamil Menggunakan Support Vector Machine (SVM) Berbasis Particle Swarm Optimization (PSO) - Fely Prasetya; Prilian Ayu Minarni; Sonianto (LPPM Universitas Muhammadiyah Kotabumi, 2025)
Accurate classification of pregnancy risk is crucial for early detection of complications and reducing maternal and infant mortality rates. However, existing classification models still face challenges in terms of accuracy, particularly in distinguishing between high and very high-risk classes. This study aims to improve the accuracy of pregnancy risk classification models by optimizing Support Vector Machine (SVM) using Particle Swarm Optimization (PSO) algorithm. The study utilizes the