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All Journal Jurnal SOLMA SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Pascapanen dan Bioteknologi Kelautan dan Perikanan Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Pengabdian Masyarakat Bangsa Jurnal Abdita Naturafarm
Gantini, Sri Nevi
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Classification Analysis of Student Graduation Timeliness Using Decision Tree and Naïve Bayes Methods Gantini, Sri Nevi; Ningsi, Besse Arnawisuda; Arofah, Irvana
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 4, No 4 (2021): Budapest International Research and Critics Institute November
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v4i4.3182

Abstract

This study aims to determine the classification of student graduation timeliness by using the Decision Tree and Naïve Bayes methods. This study uses a quantitative method, where the approach used is the classification of various attributes that affect the timeliness of student graduation. The independent variables in the classification are mostly called attributes; In this study, the attributes of school of origin, gender, area of origin, profession of parents, study program and Grade Point Average (GPA) were used. While the dependent variable or in the classification is usually called a label, in this study the label used as a decision attribute is the timeliness of student graduation. In this study, two methods were used, namely using the nave Bayes method and a decision tree (decision tree) to determine the classification of the timeliness of student graduation and to determine the level of classification accuracy. Based on the results of the analysis, it can be concluded that the classification using the nave Bayes method obtained 36 predicted data according to the actual data and 7 different predicted data from the actual data. Meanwhile, in the 42 decision tree method, the predicted data is in accordance with the actual data and there is only 1 predicted data that is different from the actual data. Decision Tree method has a lower classification error rate than the Naïve Bayes method. The level of accuracy of prediction results using the Decision Tree method is higher than the Naïve Bayes method.
Co-Authors Adia Putra Wirman Anisa Amalia Arofah, Irvana Candra, Ricky Crista Chatarina Umbul Wahyuni Dewi, Elvira Ratna Etin Diah Permanasari Fatmawati, Sofia Firmanto, Johan Dwi Fith Khaira Nursal Hariyanti Hariyanti Hariyanti Hariyanti Nafi’ah, Khusnun Ningsi, Besse Arnawisuda Nining Nining, Nining Nugrahaeni, Fitria Nur Azizah Oktafiani, Erna Rahmayanti, Mice Supandi