R. F. Malik, E. Pratama, H. Ubaya, Rido Zulfahmi, D. Stiawan, Kemahyanto Exaudi
{"title":"Object Position Estimation Using Naive Bayes Classifier Algorithm","authors":"R. F. Malik, E. Pratama, H. Ubaya, Rido Zulfahmi, D. Stiawan, Kemahyanto Exaudi","doi":"10.1109/ICECOS.2018.8605198","DOIUrl":null,"url":null,"abstract":"This study discusses the estimated position of objects in buildings with the value of Recieved Signal Strenght Indicator (RSSI) on ieee 802.11 used as the research parameter. The algorithm used in estimating the location of the RSS Fingerprint measurement is the naive bayes classifier. Position Estimation is done on the 1st floor of building B majoring in computer system, university sriwijaya with an area 305,28 m2 with length 31.8 meters and width 9.6 meters. The result of the position estimation that has been done by comparing training dataset with data testing shows that the estimation is successful with the prediction of room 2 with the coordinate point (11.3).","PeriodicalId":149318,"journal":{"name":"2018 International Conference on Electrical Engineering and Computer Science (ICECOS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Electrical Engineering and Computer Science (ICECOS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICECOS.2018.8605198","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This study discusses the estimated position of objects in buildings with the value of Recieved Signal Strenght Indicator (RSSI) on ieee 802.11 used as the research parameter. The algorithm used in estimating the location of the RSS Fingerprint measurement is the naive bayes classifier. Position Estimation is done on the 1st floor of building B majoring in computer system, university sriwijaya with an area 305,28 m2 with length 31.8 meters and width 9.6 meters. The result of the position estimation that has been done by comparing training dataset with data testing shows that the estimation is successful with the prediction of room 2 with the coordinate point (11.3).