Mutiara Cindy Nur Fitria, Naomi Nessyana Debataraja, S. W. Rizki
{"title":"基于C5.0算法的兰达克县村落状态分类","authors":"Mutiara Cindy Nur Fitria, Naomi Nessyana Debataraja, S. W. Rizki","doi":"10.30598/tensorvol3iss1pp33-42","DOIUrl":null,"url":null,"abstract":": Village development is an effort to improve the welfare and quality of life of rural communities. The development of a village, one of which can be measured by the Village Building Index (VBI). VBI is formed from three indices that are expected to cover all areas of village life. The lower the VBI value of a village, the more backward the village is. Classification of village status is very important for making policies that are in accordance with village conditions. This study used village status data based on the Village Building Index (VBI) of Landak Regency in 2020 obtained from the website of One Data West Kalimantan. The data used consists of Village Status variable ( 𝑌 ) which is the dependent variable and 12 independent variables, namely Health (X 1 ), Education (X 2 ), Social Capital (X 3 ), Settlement (X 4 ), Production Diversity (X 5 ), Trade (X 6 ), Distribution Access (X 7 ), Credit Access (X 8 ), Economic Institutions (X 9 ), Regional Openness (X 10 ), Environmental Quality (X 11 ), and Potential and Disaster Response (X 12 ). The purpose of this study is to classify the status of villages in Landak Regency using C5.0 Algorithm. Classification begins with data collection, then the data is divided into training and testing data in the 90:10 proportion. Next is the formation of a classification model using training data, after that, testing the classification model using data testing. Then the evaluation of the classification model and based on the results of the study obtained an accuracy value of 82.35%, which means the quality of the model is good and can be used, with the variables Health (X 1 ), Education (X 2 ), Settlement (X 4 ), and Credit Access (X 8 ), not too influential and the variable Potential and Disaster Response (X 12 ) the most influential in the classifying village status in Landak Regency.","PeriodicalId":294430,"journal":{"name":"Tensor: Pure and Applied Mathematics Journal","volume":"271 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification of Village Status in Landak Regency Using C5.0 Algorithm\",\"authors\":\"Mutiara Cindy Nur Fitria, Naomi Nessyana Debataraja, S. W. 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The data used consists of Village Status variable ( 𝑌 ) which is the dependent variable and 12 independent variables, namely Health (X 1 ), Education (X 2 ), Social Capital (X 3 ), Settlement (X 4 ), Production Diversity (X 5 ), Trade (X 6 ), Distribution Access (X 7 ), Credit Access (X 8 ), Economic Institutions (X 9 ), Regional Openness (X 10 ), Environmental Quality (X 11 ), and Potential and Disaster Response (X 12 ). The purpose of this study is to classify the status of villages in Landak Regency using C5.0 Algorithm. Classification begins with data collection, then the data is divided into training and testing data in the 90:10 proportion. Next is the formation of a classification model using training data, after that, testing the classification model using data testing. Then the evaluation of the classification model and based on the results of the study obtained an accuracy value of 82.35%, which means the quality of the model is good and can be used, with the variables Health (X 1 ), Education (X 2 ), Settlement (X 4 ), and Credit Access (X 8 ), not too influential and the variable Potential and Disaster Response (X 12 ) the most influential in the classifying village status in Landak Regency.\",\"PeriodicalId\":294430,\"journal\":{\"name\":\"Tensor: Pure and Applied Mathematics Journal\",\"volume\":\"271 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tensor: Pure and Applied Mathematics Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30598/tensorvol3iss1pp33-42\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tensor: Pure and Applied Mathematics Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30598/tensorvol3iss1pp33-42","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classification of Village Status in Landak Regency Using C5.0 Algorithm
: Village development is an effort to improve the welfare and quality of life of rural communities. The development of a village, one of which can be measured by the Village Building Index (VBI). VBI is formed from three indices that are expected to cover all areas of village life. The lower the VBI value of a village, the more backward the village is. Classification of village status is very important for making policies that are in accordance with village conditions. This study used village status data based on the Village Building Index (VBI) of Landak Regency in 2020 obtained from the website of One Data West Kalimantan. The data used consists of Village Status variable ( 𝑌 ) which is the dependent variable and 12 independent variables, namely Health (X 1 ), Education (X 2 ), Social Capital (X 3 ), Settlement (X 4 ), Production Diversity (X 5 ), Trade (X 6 ), Distribution Access (X 7 ), Credit Access (X 8 ), Economic Institutions (X 9 ), Regional Openness (X 10 ), Environmental Quality (X 11 ), and Potential and Disaster Response (X 12 ). The purpose of this study is to classify the status of villages in Landak Regency using C5.0 Algorithm. Classification begins with data collection, then the data is divided into training and testing data in the 90:10 proportion. Next is the formation of a classification model using training data, after that, testing the classification model using data testing. Then the evaluation of the classification model and based on the results of the study obtained an accuracy value of 82.35%, which means the quality of the model is good and can be used, with the variables Health (X 1 ), Education (X 2 ), Settlement (X 4 ), and Credit Access (X 8 ), not too influential and the variable Potential and Disaster Response (X 12 ) the most influential in the classifying village status in Landak Regency.