Implementasi Algoritma Gaussian Naive Bayes Classifier Untuk Prediksi Potensi Tsunami Berbasis Mikrokontroler - Dede Irawan Saputra; Dadang Lukman Hakim (EPSILON: Journal of Electrical Engineering and Information Technology, 2022)
The classification carried out by the Gaussian Naive Bayes Classifier algorithm can use continuous data such as the parameters that are considered when a tsunami occurs. The data collected for the classification process is some earthquake data that has occurred in Indonesia in the last 20 years. Data from the occurrence of earthquakes that are taken include the time of occurrence, the place where the earthquake occurred, the magnitude of the earthquake, the depth of the earthquake, and also the distance from the epicenter to the nearest city where the earthquake occurred. The parameters needed in implementing the prediction process are the average value of the magnitude, the depth of the epicenter, and the distance to the epicenter. Next, the values ​​of each standard deviation of magnitude, depth of the epicenter, and distance of the epicenter are also required. The microcontroller can implement the Probabilistic Density Function equation to calculate the potential for a tsunami. the microcontroller-based Gaussian Naive Bayes Classifier algorithm with the classification "Tsunami Potential" and "No Tsunami Potential" has an accuracy of 96%.