A. Salam, Junta Zeniarja, Dhevan Muhamad Anthareza
{"title":"基于深度学习卷积神经网络(CNN)的学生毕业预测模型","authors":"A. Salam, Junta Zeniarja, Dhevan Muhamad Anthareza","doi":"10.1109/iSemantic55962.2022.9920449","DOIUrl":null,"url":null,"abstract":"Students are the most important part of a university's life cycle. When compared to the number of students obtained in the same academic year, the number of students graduating from a university often has a small ratio. This low student graduation rate can be attributed to a variety of factors, including the abundance of student activities, as well as economic and other considerations. This necessitates the existence of a model that can determine whether or not a student will be able to graduate on time. Student graduation on time is one of the most important factors in determining a university. With the same ratio, the higher the level of new students at a university, the more students who graduate on time. If many students do not graduate on time from all registered students, the number of student data and academic data increases. As a result, the university's profile and reputation will suffer, potentially jeopardizing the university's accreditation value. To address this, we need a model that can predict student graduation and then be used to inform policy decisions. The goal of this research is to propose a Deep Learning classification model that uses the Convolutional Neural Network (CNN) algorithm to predict student graduation. The classification model with the CNN algorithm produced a high accuracy value of 87.44 %.","PeriodicalId":360042,"journal":{"name":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Student Graduation Prediction Model using Deep Learning Convolutional Neural Network (CNN)\",\"authors\":\"A. Salam, Junta Zeniarja, Dhevan Muhamad Anthareza\",\"doi\":\"10.1109/iSemantic55962.2022.9920449\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Students are the most important part of a university's life cycle. When compared to the number of students obtained in the same academic year, the number of students graduating from a university often has a small ratio. This low student graduation rate can be attributed to a variety of factors, including the abundance of student activities, as well as economic and other considerations. This necessitates the existence of a model that can determine whether or not a student will be able to graduate on time. Student graduation on time is one of the most important factors in determining a university. With the same ratio, the higher the level of new students at a university, the more students who graduate on time. If many students do not graduate on time from all registered students, the number of student data and academic data increases. As a result, the university's profile and reputation will suffer, potentially jeopardizing the university's accreditation value. To address this, we need a model that can predict student graduation and then be used to inform policy decisions. The goal of this research is to propose a Deep Learning classification model that uses the Convolutional Neural Network (CNN) algorithm to predict student graduation. The classification model with the CNN algorithm produced a high accuracy value of 87.44 %.\",\"PeriodicalId\":360042,\"journal\":{\"name\":\"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iSemantic55962.2022.9920449\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Seminar on Application for Technology of Information and Communication (iSemantic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iSemantic55962.2022.9920449","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Student Graduation Prediction Model using Deep Learning Convolutional Neural Network (CNN)
Students are the most important part of a university's life cycle. When compared to the number of students obtained in the same academic year, the number of students graduating from a university often has a small ratio. This low student graduation rate can be attributed to a variety of factors, including the abundance of student activities, as well as economic and other considerations. This necessitates the existence of a model that can determine whether or not a student will be able to graduate on time. Student graduation on time is one of the most important factors in determining a university. With the same ratio, the higher the level of new students at a university, the more students who graduate on time. If many students do not graduate on time from all registered students, the number of student data and academic data increases. As a result, the university's profile and reputation will suffer, potentially jeopardizing the university's accreditation value. To address this, we need a model that can predict student graduation and then be used to inform policy decisions. The goal of this research is to propose a Deep Learning classification model that uses the Convolutional Neural Network (CNN) algorithm to predict student graduation. The classification model with the CNN algorithm produced a high accuracy value of 87.44 %.