Warfare Simulation:Predicting Battleship Winner Using Random Forest
Naili Suri Intizhami, Ario Yudo Husodo, Wisnu Jatmiko · 2019
This paper proposed a framework system to analyze and predicts a battleship winner in the combat. The framework system is built by using machine learning methods, namely Random Forest (RF) method. This paper employs 9660 battleship datasets, which divided into 7728 data training and 1932 testing data. The battleship data will send to the server, then here, the battleship winner will be predict by utilized Random Forest. The accuracy will be compared, between the RF with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). The results show that the simulation based on computer network for a mutual connection and communication is adequate to implement in our warfare simulation. This simulation result can train and help the commander chooses the best battleship to use in warfare, especially in real warfare.