Literatur Review: Klasifikasi Penyakit Menular Seksual (PMS) Menggunakan Naïve Bayes dan Metode Machine Learning Terkait

Raharja Adhi Putrayana; Rizki Ramadhan; Rangga Irgi Saputra; Rahmat Abdul Sahid

Authors

Raharja Adhi Putrayana Universitas Pamulang
Rizki Ramadhan Universitas Pamulang
Rangga Irgi Saputra Universitas Pamulang
Rahmat Abdul Sahid Universitas Pamulang

Keywords:

Sexually transmitted diseases, Classification, Naïve Bayes Method and Machine Learning Method, K-Nearest Neighbor (KNN), Decision Tree

Abstract

Sexually transmitted diseases (STD) are a significant health problem worldwide. Correct identification and classification of this disease is essential to support early diagnosis and effective treatment. Various machine learning methods, including Naïve Bayes, have been used to automatically classify these diseases. This article reviews existing literature regarding the use of the Naïve Bayes method and other machine learning techniques in PMS classification. Based on analysis of at least five research journals, Naïve Bayes shows good performance in disease classification, although the results still depend on data quality. Several other methods such as Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) are also often used as comparisons in this research. This review provides insight into the strengths and weaknesses of each method in PMS classification as well as the potential for their integration to increase the accuracy and speed of diagnosis.

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