{"title":"基于k近邻(KNN)的BAZNAS网站人工智能方法","authors":"Y. Sari, M. Maulida, Endi Gunawan, J. Wahyudi","doi":"10.1109/ICIC54025.2021.9632954","DOIUrl":null,"url":null,"abstract":"Amil Zakat National Agency (BAZNAS) is a national institution for the distribution of zakat. As one of the main foundations in Islam, zakat is, obviously, very important to be fulfilled. However, it is very often that the data of the recipient became unclear that it caused problems in terms of a fair distribution of zakat. This research tried to offer a solution by doing a classification of the recipient of zakat on the BAZNAS websites into two categories: indigent and poor, using K-Nearest Neighbor method. This research concluded that the accuracy of KNN method by using classification report, confusion matrix, and ROC-AUC respectively resulted in accuracy of 97%, 96.7%, and 97.7%","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Artificial Intelligence Approach For BAZNAS Website Using K-Nearest Neighbor (KNN)\",\"authors\":\"Y. Sari, M. Maulida, Endi Gunawan, J. Wahyudi\",\"doi\":\"10.1109/ICIC54025.2021.9632954\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Amil Zakat National Agency (BAZNAS) is a national institution for the distribution of zakat. As one of the main foundations in Islam, zakat is, obviously, very important to be fulfilled. However, it is very often that the data of the recipient became unclear that it caused problems in terms of a fair distribution of zakat. This research tried to offer a solution by doing a classification of the recipient of zakat on the BAZNAS websites into two categories: indigent and poor, using K-Nearest Neighbor method. This research concluded that the accuracy of KNN method by using classification report, confusion matrix, and ROC-AUC respectively resulted in accuracy of 97%, 96.7%, and 97.7%\",\"PeriodicalId\":189541,\"journal\":{\"name\":\"2021 Sixth International Conference on Informatics and Computing (ICIC)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Sixth International Conference on Informatics and Computing (ICIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIC54025.2021.9632954\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Sixth International Conference on Informatics and Computing (ICIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIC54025.2021.9632954","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial Intelligence Approach For BAZNAS Website Using K-Nearest Neighbor (KNN)
Amil Zakat National Agency (BAZNAS) is a national institution for the distribution of zakat. As one of the main foundations in Islam, zakat is, obviously, very important to be fulfilled. However, it is very often that the data of the recipient became unclear that it caused problems in terms of a fair distribution of zakat. This research tried to offer a solution by doing a classification of the recipient of zakat on the BAZNAS websites into two categories: indigent and poor, using K-Nearest Neighbor method. This research concluded that the accuracy of KNN method by using classification report, confusion matrix, and ROC-AUC respectively resulted in accuracy of 97%, 96.7%, and 97.7%