E. Herdiana, Indra Rustiawan, Zatinniqotaini Zatinniqotaini, Nova Indarayana Yusman
{"title":"Deteksi Wajah Kehadiran Mahasiswa Saat Perkuliahan Daring Menggunakan Metode Klasifikasi Nearest Neighboarhood","authors":"E. Herdiana, Indra Rustiawan, Zatinniqotaini Zatinniqotaini, Nova Indarayana Yusman","doi":"10.32627/internal.v4i2.257","DOIUrl":null,"url":null,"abstract":"Recording student attendance during lectures with an online system [on the network] is very necessary to assist both lecturers and the academic department in recording each student's attendance. Therefore the author will make an approach method based on face detection [face recognition] with the K-Nearest Neighbor algorithm or often called the K-NN algorithm, which is a supervised learning algorithm where the results of the new instance are classified based on the majority of the k-nearest neighbors. . The purpose of this algorithm is to classify new objects based on attributes and samples of student attendance/attendance. The k-Nearest Neighbor algorithm uses the Neighborhood Classification which will be used as the predictive value of the new instance so that it will get a value that will approximate the student's facial resemblance.","PeriodicalId":421147,"journal":{"name":"INTERNAL (Information System Journal)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"INTERNAL (Information System Journal)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32627/internal.v4i2.257","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Recording student attendance during lectures with an online system [on the network] is very necessary to assist both lecturers and the academic department in recording each student's attendance. Therefore the author will make an approach method based on face detection [face recognition] with the K-Nearest Neighbor algorithm or often called the K-NN algorithm, which is a supervised learning algorithm where the results of the new instance are classified based on the majority of the k-nearest neighbors. . The purpose of this algorithm is to classify new objects based on attributes and samples of student attendance/attendance. The k-Nearest Neighbor algorithm uses the Neighborhood Classification which will be used as the predictive value of the new instance so that it will get a value that will approximate the student's facial resemblance.