Pengaruh Splitting Data terhadap Akurasi Klasifikasi Demam Berdarah Dengue Menggunakan K-Nearest Neighbors - Ibrahim Akbar, Muhammad; Rudi, Rudy Heriansyah; irfani, muhammad haviz (Algoritme Jurnal Mahasiswa Teknik Informatika, 2026)
Early detection of Dengue Hemorrhagic Fever (DHF) is crucial to prevent serious complications and improve treatment effectiveness, particularly in high-case areas such as the Dempo Primary Health Center. This study aims to develop and evaluate a DHF classification system using the K-Nearest Neighbors (K-NN) algorithm with an optimal K value of 5, determined via the Elbow Method. The dataset consists of 200 medical records with an imbalanced class distribution between positive and negative DHF cases. Three data-splitting scenarios (70:30, 80:20, and 90:10) were tested to analyze the effect of data proportion on model performance. Evaluation metrics included accuracy, precision, recall, and F1-score. Results show that the 70:30 scenario achieved the best performance, with 90% accuracy, 96.67% precision, 85.29% recall, and 90.62% F1-score. For comparison, K-NN was tested against Decision Tree and Support Vector Machine (SVM) algorithms as baselines. K-NN demonstrated competitive and more stable performance, with an average accuracy difference of ±2% compared to the other methods. These findings confirm that K-NN provides reliable results for medical data with limited sample size and imbalanced class distribution. This study contributes empirical analysis regarding the influence of varying data split ratios on classification model stability and strengthens the application of machine learning for early DHF detection based on local medical data.