Perbandingan Arsitektur CNN untuk Klasifikasi Citra Sel Darah Putih

ICOSNIKOM 2022 - IEEE International Conference of Computer Science and Information Technology
2 Februari 2025 oleh
Perbandingan Arsitektur CNN untuk Klasifikasi Citra Sel Darah Putih
kelvin leonardi

Abstrak


Advances  in  medical  technology  have  enabled  the  application  of  machine  learning  for  disease  classification, including  monkeypox.  Monkeypox  is  a  zoonotic disease  caused  by  the  monkeypox  virus  and  can  be  detected through patient data. This study aims to compare the performance of Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Naïve Bayes algorithms in building a monkeypox classification model. The dataset used consists of 25,000 patient records. The results show that the SVM model with a linear kernel achieved the best accuracy compared to KNN and Naïve Bayes. These findings demonstrate that the SVM model with a linear kernel is highly effectivein classifying monkeypox, offering great potential for further medical applications.

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Kelvin Leonardi Kohsasih

Universitas Potensi Utama

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Citation:

K. L. Kohsasih. "Comparison of SVM, KNN, And Naïve Bayes Algorithms in Monkeypox Disease Classification" TAMIKA, vol. 4, no. 2(Semnastik)(2024),hlm. 168-174.

Publication:
Vol. 4 No. 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
DOI:
https://doi.org/10.46880/tamika.Vol4No2(SEMNASTIK).pp168-174

Copyright:

Copyright (c) Tamika
Perbandingan Arsitektur CNN untuk Klasifikasi Citra Sel Darah Putih
kelvin leonardi 2 Februari 2025
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