Data Mining Memprediksi Kebutuhan Vaksin Imunisasi dengan Menggunakan Metode Naive Bayes (Studi kasus UPT Puskesmas Teladan)

Abstract

Vaccines are medical measures whose aim is to prevent disease, not cure, because vaccines are very important to be given to everyone before they are infected with certain diseases. Vaccines serve to protect the body from attack by foreign organisms such as viruses or bacteria, so the vaccine is given to children during infancy. One of the vaccines given is the BCG or Bacillus Calmette-Guerin vaccine which has a age of 0-2 years, the vaccine given to protect against tuberculosis (TB), an infectious disease that primarily attacks the lungs. Data mining, often called knowledge discovery in database (KDD), is an activity that includes collecting and using historical data to find regularities, patterns or relationships in large data sets. The output of data mining can be used to improve future decision making. Naive Bayes is a simple probabilistic classification that calculates a set of probabilities by summing the frequencies and value combinations from a given dataset. Naive Bayes is based on the simplifying assumption that attribute values ??are conditionally independent of each other when given an output value. In other words, given the output value of the probability of observing together, it is a product of individual probability. The tools used to test are the Tanagra 1.4 software. With this research, it is hoped that it can help the UPT Puskesmas Teladan, especially in predicting the need for the immunization vaccines needed by using the Naive Bayes method to make it even more effective and improve the quality of the health center.