Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP)
Muhammad Iyan Syahfitrah Lubis
Universitas Prima Indonesia
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparison of Support Vector Machine (SVM) and Decision Tree Methods in Lyme Disease Data Classification Jepri Banjarnahor; Muhammad Iyan Syahfitrah Lubis; Julius Andrew; Bastian Brema Bangun; Yonata Laia; Elvis Sastra ompusunggu
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 2 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i2.7396

Abstract

Lyme Disease is a zoonotic infection caused by the bacterium Borrelia burgdorferi, transmitted to humans through bites from ticks of the Ixodes genus. This disease can lead to serious complications affecting the nervous system, joints, skin, and heart if not diagnosed and treated early. However, early diagnosis remains a major challenge due to the non-specific nature of its initial symptoms, which often resemble other common illnesses. In this context, artificial intelligence approaches particularly classification methods in machine learning can assist in achieving faster and more accurate diagnosis and medical decision-making. This study aims to compare the performance of two classification algorithms, Support Vector Machine (SVM) and Decision Tree, in classifying the spread level of Lyme Disease cases using historical data from the United States. The dataset includes temporal and geographic attributes spanning the years 1992 to 2011. Model performance is evaluated using accuracy, precision, recall, and F1-score. The results indicate that the Decision Tree algorithm outperforms SVM in terms of classification accuracy and interpretability. This finding suggests that Decision Tree may be more suitable for integration into clinical decision support systems for Lyme Disease diagnosis.
Co-Authors Banjarnahor, Jepri Bastian Brema Bangun Elvis Sastra Ompusunggu Julius Andrew Yonata Laia