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Journal : Scientific Journal of Informatics

Analysis of Student Graduation Prediction Using Machine Learning Techniques on an Imbalanced Dataset: An Approach to Address Class Imbalance Hermanto, Dedy; Desy Iba Ricoida; Desi Pibriana; Rusbandi; Muhammad Rizky Pribadi
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.5528

Abstract

Purpose: Machine learning is a key area of artificial intelligence, applicable in various fields, including the prediction of timely graduation. One method within machine learning is supervised learning. However, the results are influenced by the distribution of data, particularly in the case of imbalanced classes, where the minority class is significantly smaller than the majority class, affecting classification performance. Timely graduation from a university is crucial for its sustainability and accreditation. This research aims to identify a suitable method to address the issue of predicting timely graduation by managing class imbalance using SMOTE (Synthetic Minority Oversampling Technique). Methods: This study uses a five-year dataset with 26 attributes and 1328 records, including status labels. The preprocessing stages involve applying five classification algorithms: Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest (RF). Each algorithm is used both with and without SMOTE to handle the class imbalance. The dataset indicates that 60.84% of the cases represent timely graduations. To mitigate the imbalance, over/under-sampling methods are employed to balance the data. The evaluation metric used is the confusion matrix, which assesses the classification performance. Result: Without SMOTE, the accuracies were 89.12% for DT, 79.65% for NB, 89.47% for LR, 87.72% for KNN, and 90.88% for RF. With SMOTE, the accuracies were 88.89% for DT, 81.48% for NB, 91.05% for LR, 92.59% for KNN, and 89.81% for RF. The algorithms NB, LR, and KNN showed improvement with SMOTE, with KNN yielding the best results. Novelty: Based on the comparison results, a comparison of five algorithms with and without SMOTE can reasonably classify several of the algorithms being compared.
Co-Authors -, Felicia Adi Saputra Aditya Al Assad Adrian Chen Ahmad Dumyati Ahmad Farisi Ahmad Zaky Nadimsyah Alwin Marcellino Ampu Syura Andreas Andreas Andreas Danny Agus W Andreas Saputra Andrian Wijaya Angel Kelly Asyraq, Cerwyn Bakti Ananda Fernando Bautista, Christian Bella Jenni Ourelia Boy Putra Calvin Bertnas Valentino Calvin Saputra Carissa Maharani Chandra Chandra Saputra Clara Meyhazlinda Putri Clement, Michael Joy Daniel Daniel Daniel Johan Daniel Wijaya Darwin Saputra David Sebastian Dedy Hermanto Desta Rahman Theja Desy Iba Ricoida Dicky Ryanto Fernandes Diva Putri Kynta Dwi Apriyanti Sastika Effendi pratama, Samuel Egi Fransisco Saputra Eka Puji Widiyanto Evangs Mailoa Evi Maria Fadhil Sa'adat Farisi, Ahmad Fathimah Azzahra Felicia Felicia Fellyca Effendi Feriyanto Feriyanto Ferliansyah, Fernando Fernandi Indi Nizar G Fernando Fernando Fernando Namas Fionna Caroline Florence Renaldo Frans Bachtiar Fransiskus Daniel Chandra Frisky Wijaya Geraldo Wilson Gerry Christian Pilipus Gunawan, Michael Hafidz Irsyad Hafiz Irsyad Hansen Hansen Hendrawan, Malvin Hendry Hindriyanto Dwi Purnomo Ilham Indra Hidayat Imelia Dwinora Cahyati Indi Nizar G, Fernandi Ivan Luthfi Laksono Jackie Wijaya Jasen Jonathan Ja`Far Ja`Far Jelvin Krisna Putra Jerin, Nathaniel Kasanova, Sinyo Kelvin Dwi Wahyudi Kevin agustria zahri Kevin Andreas KGS M Ammar Yazid Kurniawan, Ricky Arie Laurentius Ricardo Wijaya Leo Chandra Leonardo Yahya Lin, Valen Julyo Armando Davincy Lipi Amanda Putra Lucretia, Jolyn M Lazuardi Ferdillian Michael michael Wijaya Millenia Mudita Chandra Muhammad Abdul Azizul Hakim Muhammad Alfa Rizi Muhammad Azril Fahrezi Muhammad Dafhi Mayrizkiy Muhammad Dody Muhammad Fadli Muhammad Hamdandi Muhammad Naufal Anugrah Muhammad Redho Saputra Muhammad Reyza Nirwana Muhammad Robi, Muhammad Nabila Syiva Altarisa Nabilah Dayanah Nathacia Lais Naufal Akbar Neilsen Nicholas Komah Nicolas Jacky Pratama Hasan Nova Ariansyah Pibriana, Desi Pratama, Brilliant Chandra Purwasih, Opita Putra Laksana, Jovansa Putri, Agnes Anastasia Regian batistuta, Putra Reza Satria Rika Maulina Riki Chandra Rio Ferdynand Riska Fajriati Rivaldo Therino Elevan Rivaldo, Mario Riza Umami Rizky Kurniawan Roby Julian Romi Laxi Ronaldo Putra Rusbandi rusbandi rusbandi, rusbandi Salwa Fakhira Imletta San Gabriel Vanness Kenrick Erwi Sanila Maharani Santoso, Fian Julio Saputra Edika, Nelson Sardika, Ricky Putra Shela, Shela Sherdian Djunaidi Sinshevan Viswanatan Kravizt Erwi Sonia Sonia Sri Yulianto Joko Prasetyo Stephanie Stephanie Stephen Setyawan Steven Tribethran Suparto, Adrian Suryasatria Trihadaru Sutarto Wijono Syahrani Nur Hakim Syifa Wahyuni Tad Gonsalves Tangguh Prana Welas Sukma Vannes Wijaya Vanness Bee Vincent Vincent Virgiansyah, Muhammad Rifqi Wijang Widhiarso Wijaya, Ananda Wilcent, Wilcent William Wijaya Wiwik Handayani Yennica Valentine Hagunawan Yohanes Andika Dharma Yohanes Fransisco Mardi Chandra Yoko Saputra Dewa Yosefa Camilia Moniung Yunarto Yunarto, Yunarto `Adelia Anjelina