@inproceedings{51d5c09eb63e4b4dae7432f3168fb6c5,
title = "Warfare Simulation:Predicting Battleship Winner Using Random Forest",
abstract = "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.",
keywords = "Battleship Winner, Computer Networks, Machine Learning, Random Forest, Warfare Simulation",
author = "Intizhami, {Naili Suri} and Husodo, {Ario Yudo} and Wisnu Jatmiko",
year = "2019",
month = aug,
day = "1",
doi = "10.1109/COMNETSAT.2019.8844049",
language = "English",
series = "2019 IEEE International Conference on Communication, Networks and Satellite, Comnetsat 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "30--34",
booktitle = "2019 IEEE International Conference on Communication, Networks and Satellite, Comnetsat 2019 - Proceedings",
address = "United States",
note = "8th IEEE International Conference on Communication, Networks and Satellite, Comnetsat 2019 ; Conference date: 01-08-2019 Through 03-08-2019",
}