{"title":"基于模糊推理系统的天津大学讲师成绩指标测量","authors":"K. Umam, Eldy Satriya Wibawa","doi":"10.1109/ICORIS50180.2020.9320794","DOIUrl":null,"url":null,"abstract":"As an effort to measure lecturer performance, STIKOM PGRI Banyuwangi implements the measurement of the Lecturer Achievement Index (LAI). The size is conducted by collecting student ratings of the lecturer's performance using a Likert scale on nine performance indicators. However, this method only produces an average value from the indicator scores. This method has not been able to describe the quality of a lecturer's performance. Therefore, another mechanism is needed so that the lecturer's performance quality can be known. One method that can be used is the Fuzzy Inference System (FIS). The technique is used to map the lecturer's performance average scores given by students into three levels of performance quality, i.e., Good, adequate, and PO, or based on predefined membership functions. These fuzzy values then evaluated using predefined rules to determine the lecturer's performance quality. The LAI score is then obtained from the defuzzification stage of these values. By using this method, the LAI score obtained is different from the old mechanism. It can enhance the score based on performance quality. The better the quality, the better the score that the lecturer gets, and vice versa.","PeriodicalId":280589,"journal":{"name":"2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lecturer Achievement Index Measurement Using Fuzzy Inference System at STIKOM PGRI Banyuwangi\",\"authors\":\"K. Umam, Eldy Satriya Wibawa\",\"doi\":\"10.1109/ICORIS50180.2020.9320794\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As an effort to measure lecturer performance, STIKOM PGRI Banyuwangi implements the measurement of the Lecturer Achievement Index (LAI). The size is conducted by collecting student ratings of the lecturer's performance using a Likert scale on nine performance indicators. However, this method only produces an average value from the indicator scores. This method has not been able to describe the quality of a lecturer's performance. Therefore, another mechanism is needed so that the lecturer's performance quality can be known. One method that can be used is the Fuzzy Inference System (FIS). The technique is used to map the lecturer's performance average scores given by students into three levels of performance quality, i.e., Good, adequate, and PO, or based on predefined membership functions. These fuzzy values then evaluated using predefined rules to determine the lecturer's performance quality. The LAI score is then obtained from the defuzzification stage of these values. By using this method, the LAI score obtained is different from the old mechanism. It can enhance the score based on performance quality. The better the quality, the better the score that the lecturer gets, and vice versa.\",\"PeriodicalId\":280589,\"journal\":{\"name\":\"2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)\",\"volume\":\"171 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICORIS50180.2020.9320794\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICORIS50180.2020.9320794","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lecturer Achievement Index Measurement Using Fuzzy Inference System at STIKOM PGRI Banyuwangi
As an effort to measure lecturer performance, STIKOM PGRI Banyuwangi implements the measurement of the Lecturer Achievement Index (LAI). The size is conducted by collecting student ratings of the lecturer's performance using a Likert scale on nine performance indicators. However, this method only produces an average value from the indicator scores. This method has not been able to describe the quality of a lecturer's performance. Therefore, another mechanism is needed so that the lecturer's performance quality can be known. One method that can be used is the Fuzzy Inference System (FIS). The technique is used to map the lecturer's performance average scores given by students into three levels of performance quality, i.e., Good, adequate, and PO, or based on predefined membership functions. These fuzzy values then evaluated using predefined rules to determine the lecturer's performance quality. The LAI score is then obtained from the defuzzification stage of these values. By using this method, the LAI score obtained is different from the old mechanism. It can enhance the score based on performance quality. The better the quality, the better the score that the lecturer gets, and vice versa.