{"title":"基于C4.5算法的万隆县降水预报","authors":"Joko Azhari Suyatno, F. Nhita, A. A. Rohmawati","doi":"10.1109/ICOICT.2018.8528725","DOIUrl":null,"url":null,"abstract":"Bandung regency is one of Indonesian city that majority of the people are farmers. Farmers need weather information to determine the planting season. Weather forecasting become an orientation in agriculture sector to determining the beginning of planting season, and also quality and quantity of their harvest. One of the factors that affecting the harvest is rainfall. In this research, we make a classification model using C4.5 algorithm for rainfall forecasting at Bandung regency. Then, the post-pruning method is used to optimize pruning on the model. We used a weather data from BMKG (Meteorological, Climatological, and Geophysical Agency) in 2005 to 2016 period. The result of average accuracy testing without pruning is 60% and using pruning is 93.33%.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Rainfall Forecasting in Bandung Regency Using C4.5 Algorithm\",\"authors\":\"Joko Azhari Suyatno, F. Nhita, A. A. Rohmawati\",\"doi\":\"10.1109/ICOICT.2018.8528725\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Bandung regency is one of Indonesian city that majority of the people are farmers. Farmers need weather information to determine the planting season. Weather forecasting become an orientation in agriculture sector to determining the beginning of planting season, and also quality and quantity of their harvest. One of the factors that affecting the harvest is rainfall. In this research, we make a classification model using C4.5 algorithm for rainfall forecasting at Bandung regency. Then, the post-pruning method is used to optimize pruning on the model. We used a weather data from BMKG (Meteorological, Climatological, and Geophysical Agency) in 2005 to 2016 period. The result of average accuracy testing without pruning is 60% and using pruning is 93.33%.\",\"PeriodicalId\":266335,\"journal\":{\"name\":\"2018 6th International Conference on Information and Communication Technology (ICoICT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 6th International Conference on Information and Communication Technology (ICoICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOICT.2018.8528725\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 6th International Conference on Information and Communication Technology (ICoICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOICT.2018.8528725","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Rainfall Forecasting in Bandung Regency Using C4.5 Algorithm
Bandung regency is one of Indonesian city that majority of the people are farmers. Farmers need weather information to determine the planting season. Weather forecasting become an orientation in agriculture sector to determining the beginning of planting season, and also quality and quantity of their harvest. One of the factors that affecting the harvest is rainfall. In this research, we make a classification model using C4.5 algorithm for rainfall forecasting at Bandung regency. Then, the post-pruning method is used to optimize pruning on the model. We used a weather data from BMKG (Meteorological, Climatological, and Geophysical Agency) in 2005 to 2016 period. The result of average accuracy testing without pruning is 60% and using pruning is 93.33%.
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