Implementasi Support Vector Machine untuk Klasifikasi Kasus  Monkeypox: Pendekatan Oversampling dan Undersampling untuk  Mengatasi Ketidakseimbangan Kelas

Cindy; Tiffany Sabatini; Vincent Itan

Authors

Cindy Universitas Universal
Tiffany Sabatini Universitas Universal
Vincent Itan Universitas Universal

Keywords:

Imbalanced Data, Monkeypox, Random Under Sampling, Random Over Sampling, Support Vector Machine

Abstract

Monkeypox is an infectious disease caused by the monkeypox virus. This study applies the Support Vector Machine (SVM) method to classify monkeypox cases. Utilizing SVM aids in accurate diagnosis and prevention measures. Preprocessing involves Random Oversampling (ROS) and Random Undersampling (RUS) to address class imbalance in symptom datasets. SVM classification is based on systemic symptoms and clinical signs. Evaluation via Confusion Matrix assesses accuracy, sensitivity, specificity, and AUC, with average accuracy reaching 67.1% for imbalanced data and 36.5% for balanced data. The method outperforms conventional techniques, demonstrating its potential in monkeypox symptom pattern recognition. Results indicate higher accuracy in diagnosing monkeypox using SVM, despite class imbalances. This study contributes to understanding, predicting, and managing monkeypox outbreaks effectively.

References

A. Y. Pratama, R. J. S. Toisuta, and J. Y. Tamba, “Tinjauan atas Monkeypox”, Cermin Dunia Kedokteran, vol. 50, no. 2, pp. 75–81, Feb. 2023.

A.S. Nugroho, A.B. Witarto, dan D. Handoko, "Application of Support Vector Machine in Bioinformatics," dalam Proceeding of Indonesian Scientific Meeting in Central Japan, Gifu-Japan, 20 Desember 2003.

Muhamad Ahmed. (2022). Monkey-Pox PATIENTS Dataset. [Data set]. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/4271503.

O. Dhifa, Z. Jurusan, M. Fakultas, and M. Dan, “PENANGANAN IMBALANCE DATA DENGAN RANDOM OVERSAMPLING (ROS) PADA KLASIFIKASI PENDERITA DIABETES MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) (Skripsi),” 2023. Accessed: Apr. 29, 2024.

A. R. Purnajaya and F. D. Hanggara, “Perbandingan Performa Teknik Sampling Data untuk Klasifikasi Pasien Terinfeksi Covid-19 Menggunakan Rontgen Dada”, JAIC, vol. 5, no. 1, pp. 37-42, Jun. 2021.

A. R. Purnajaya and F. D. Hanggara, “Using Data Sampling Technique for Improving Classification of Covid-19 and Lung Diseases”, UNISET 2021, EAI, pp. 167-175, Aug. 2022, https://doi.org/ 10.4108/eai.2-12-2021.2320242

N. P. D. T. Yanti and I. M. D. P. Asana, “Sistem Klasifikasi Pengajuan Kredit dengan Metode Support Vector Machine (SVM),” Jurnal Sistem Cerdas, vol. 6, no. 2, pp. 123–133, Aug. 2023.

Purbolaksono, M. D., Irvan Tantowi, M., Imam Hidayat, A., & Adiwijaya, A. (2021). Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 393 - 399. https://doi.org/10.29207/resti.v5i2.3008

A. Zuhairah, “Penerapan Algoritma Random Forest, Support Vector Machines (SVM) Dan Gradient Boosted Tree (GBT) Untuk Deteksi Penipuan (Fraud Detection) Pada Transaksi Kartu Kredit,” (Skripsi), Feb. 2022.

T. Ridwansyah, “Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 2, no. 5, pp. 178–185, Apr. 2022.