A semi-supervised learning approach for predicting student's performance: First-year students case study

Yekti Widyaningsih, Nur Fitriani, Devvi Sarwinda

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Students performance is an essential part of a higher learning institution because one of the criteria for a high -quality university is based on its excellent record of academic achievements. The first-year of the lecture is the student period of laying the foundation that will affect academic success because first-year plays an important role in shaping the attitudes and performance of students in the following years. In this study, a semi-supervised learning approach is used to classify the performance of first-year students in the Department of Mathematics, Universitas Indonesia. Student performance will be divided into two categories, namely medium and high. The sample in this study consist of 140 first-year students with 27 features. There are two processes used i.e. clustering and the classification process. In the clustering process, the data is divided into three clusters using K-Means Clustering and the Naïve Bayes Classifier is chosen to classify it. The performance of the proposed algorithms is stated by accuracy, sensitivity, and specificity value i.e. 96%, 92.86%, and 100% respectively.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages291-295
Number of pages5
ISBN (Electronic)9781728121338
DOIs
Publication statusPublished - 1 Jul 2019
Event12th International Conference on Information and Communication Technology and Systems, ICTS 2019 - Surabaya, Indonesia
Duration: 18 Jul 2019 → …

Publication series

NameProceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019

Conference

Conference12th International Conference on Information and Communication Technology and Systems, ICTS 2019
Country/TerritoryIndonesia
CitySurabaya
Period18/07/19 → …

Keywords

  • K-Means Clustering
  • Naïve Bayes Classifier
  • Semi-supervised Learning
  • Student Performance

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