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All Journal JUITA : Jurnal Informatika Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Muhammad Ricky Perdana Putra
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering

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A Blending Ensemble Approach to Predicting Student Dropout in Massive Open Online Courses (MOOCs) Muhammad Ricky Perdana Putra; Ema Utami
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24061

Abstract

The problem faced in the implementation of Massive Open Online Course (MOOC) is the high dropout rate (DO) reaching 90% which exceeds the formal school dropout rate. Preventive action needs to be taken to minimize the impact on MOOCs, instructors, and students. One solution is to do machine learning (ML) based prediction. The use of ML does not escape the problem of prediction performance that is still less accurate so it needs to be improved by blending ensemble learning (BEL). This research builds a BEL model consisting of two layers including base model with KNN, Decision Tree, and Naïve Bayes algorithms, then meta model with XGBoost. The dataset from KDD Cup 2015 contains clickstream from XuetangX website. The pre-processing stage includes selecting the course with the most participants, normalization, SMOTE, feature selection, and breaking it into three: ensemble, blender, and test data. The BEL model evaluation results obtained an accuracy value of 90.16%, precision of 85.64%, recall of 97.31%, F1-Score of 91.10%, and AUC of 92.83%.
Flood Prediction Using Machine Learning Model Integrated with Geographical Information System Muhammad Ricky Perdana Putra; Rama Ashari; Muhirin; Azib Widad Zuhaily Imam; Kusrini
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.3723

Abstract

Flooding in Indonesia is still a frequent natural disaster compared to other types of disasters. In addition, the number of flood events also shows an increase every year. This research aims to develop a flood prediction model as a preventive measure as an early warning system and flood risk mitigation management that may occur based on Geographical Information System (GIS). It is expected that areas that have the potential to experience flooding can be more proactive in making preparations before flooding. This prediction model uses a classification type machine learning (ML) algorithm with training data involving rainfall within 12 months. The model evaluation results use two techniques: confusion matrix and K-Fold cross validation and each fold is calculated for accuracy. The K-Nearest Neighbors (KNN) model with a value of K = 31 gets the highest accuracy value of 88.89%, Decision Tree (DT) of 72.22%, and Naive Bayes of 78%. The average accuracy using K-Fold resulted in 89.09% for KNN, 77.12% for DT, and 86.59% for Naive Bayes. By considering these results, this research chose the KNN method to be applied in the prediction model. The code was rewritten in the Flask framework to be used as an API and integrated with Laravel as a Backend platform and Frontend using Bootstrap, JQuery, Axios, and LeafletJS as map visualisation. With this research, it is hoped that it can be one of the solutions in predicting as well as early warning of floods so that it can provide sufficient time for affected residents to make preparations for flooding.
Co-Authors Azib Widad Zuhaily Imam Ema Utami Kusrini Muhirin Rama Ashari