Implementasi Data Mining Untuk Diagnosa Prediksi Penyakit Tuberculosis Dengan Menggunakan Algoritma Naïve Bayes
Keywords:
Sistem pakar, Naïve Bayesian, TuberkulosisAbstract
Tuberculosis (TB) is still a significant health problem in Indonesia, and early detection of TB cases is very important to prevent the spread of this disease. The Naive Bayes classification method has been proven effective in classifying disease cases, including TB. The purpose of this study was to apply the Naive Bayes classification method to TB case data in Indonesia and evaluate the performance of the resulting classification model. TB case data were obtained from Sari Asih Ciputat Hospital and processed using data processing and data cleaning techniques. Then, the data is divided into training data and test data. The Naive Bayes classification method is implemented on training data and then evaluated using test data. The results of this study indicate that the Naive Bayes classification method can be applied to TB cases in Indonesia with good classification accuracy. The factors that most influence the classification of TB cases are geography, age, and gender. This research is expected to help improve the early detection of TB cases in Indonesia and improve efforts to prevent and treat this disease.
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