Klasifikasi Rambut Rontok Menggunakan Metode Naive Bayes
DOI:
https://doi.org/10.54259/satesi.v5i2.5612Keywords:
Hair Loss, Data Mining, Classification, Naïve Bayes, PredictionAbstract
Hair loss is a common problem experienced by various groups, both men and women. In 2023, a survey found that 64.7% of hair problems were hair loss, followed by dandruff, dry hair, dull hair, limp hair, and oily hair. The causes of hair loss are diverse, ranging from genetic and hormonal factors to lifestyle. To accurately identify and classify the types of hair loss, a method is needed that can handle data efficiently and accurately. This study aims to classify types of hair loss using the Naive Bayes method, a classification algorithm in data mining based on probability and Bayes' theorem. The data used consists of various attributes such as staying up late, brain activity duration, stress levels, dandruff, and hormones. The Naive Bayes method was chosen because of its ability to handle data with a large number of attributes and produce fairly accurate predictions even with the assumption of independence between features. The results of the study show that the Naive Bayes method is capable of classifying types of hair loss with an accuracy rate of 76.67%. These findings indicate that this method can be used as a tool to aid in the rapid and efficient initial diagnosis of hair loss problems.
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