Improving classification algorithm on education dataset using hyperparameter tuning

Daud Muhajir, Muhammad Akbar, Affindi Bagaskara, Retno Vinarti*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

12 Citations (Scopus)


In this paper, researchers propose a classification method for any institution's campus placement possibility using Placement Data Full Class for campus recruitment dataset. Researchers attempt to study the supervised learning classification algorithms such Logistic Regression, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, and Linear Discriminant Analysis (LDA). Hyperparameter optimization also used to optimize the supervised algorithms for better results. Experimental results have found that by using hyperparameter tuning in Linear Discriminant Analysis (LDA), it can increase the accuracy performance results, and also given a better result compared to other algorithms.

Original languageEnglish
Pages (from-to)538-544
Number of pages7
JournalProcedia Computer Science
Publication statusPublished - 2021
Event6th Information Systems International Conference, ISICO 2021 - Virtual, Online, Italy
Duration: 7 Aug 20218 Aug 2021


  • Campus recruitment
  • Decision tree
  • Gaussian naive bayes
  • Gradient boosting
  • Hyperparameter optimization
  • K Nearest neighbors (KNN)
  • Linear discriminant analysis (LDA)
  • Logistic regression
  • Random forest
  • Supervised classification
  • Support vector classifier (SVC)


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