A. A. P. Ratna, Naiza Astri Wulandari, Aaliyah Kaltsum, Ihsan Ibrahim, Prima Dewi Purnamasari
{"title":"基于潜在语义分析的印尼语自动简答评分系统K-Means答案分类方法","authors":"A. A. P. Ratna, Naiza Astri Wulandari, Aaliyah Kaltsum, Ihsan Ibrahim, Prima Dewi Purnamasari","doi":"10.1109/QIR.2019.8897845","DOIUrl":null,"url":null,"abstract":"The Automatic Short Answer Grading (Simple-O) has been created for grading short answer with Bahasa Indonesia using K-Means and Latent Semantic Analysis (LSA) method. In this experiment, the text document feature will be extracted using Term Frequency-Inverse Document Frequency (TF-IDF) and then classified using K-Means. From the experiment, 149 documents are expected to be clustered into five classes. The result of the clustering using K-Means is 100% matched with clustering using human rater. The result of grading with LSA is 74%.","PeriodicalId":284463,"journal":{"name":"2019 16th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Answer Categorization Method Using K-Means for Indonesian Language Automatic Short Answer Grading System Based on Latent Semantic Analysis\",\"authors\":\"A. A. P. Ratna, Naiza Astri Wulandari, Aaliyah Kaltsum, Ihsan Ibrahim, Prima Dewi Purnamasari\",\"doi\":\"10.1109/QIR.2019.8897845\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Automatic Short Answer Grading (Simple-O) has been created for grading short answer with Bahasa Indonesia using K-Means and Latent Semantic Analysis (LSA) method. In this experiment, the text document feature will be extracted using Term Frequency-Inverse Document Frequency (TF-IDF) and then classified using K-Means. From the experiment, 149 documents are expected to be clustered into five classes. The result of the clustering using K-Means is 100% matched with clustering using human rater. The result of grading with LSA is 74%.\",\"PeriodicalId\":284463,\"journal\":{\"name\":\"2019 16th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 16th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/QIR.2019.8897845\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 16th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/QIR.2019.8897845","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Answer Categorization Method Using K-Means for Indonesian Language Automatic Short Answer Grading System Based on Latent Semantic Analysis
The Automatic Short Answer Grading (Simple-O) has been created for grading short answer with Bahasa Indonesia using K-Means and Latent Semantic Analysis (LSA) method. In this experiment, the text document feature will be extracted using Term Frequency-Inverse Document Frequency (TF-IDF) and then classified using K-Means. From the experiment, 149 documents are expected to be clustered into five classes. The result of the clustering using K-Means is 100% matched with clustering using human rater. The result of grading with LSA is 74%.