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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Bulletin of Computer Science Research
Ade Eviyanti
Universitas Muhammadiyah Sidoarjo, Sidoarjo
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Deteksi Penyakit Epilepsi Melalui Sinyal EEG Menggunakan Metode DWT dan Extreme Gradient Boosting Erlina Agustin; Ade Eviyanti; Nuril Lutvi Azizah
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5412

Abstract

Epilepsy is a disorder of the central nervous system due to excessive patterns of electrical activity in the brain. This disease causes patients to experience repeated seizures in one or all parts of the body. Therefore, epilepsy must be detected early so that the patient immediately gets the right treatment so that the condition does not get worse. This study proposes the detection of epilepsy using the Discrete Wavelet Transform method for feature extraction and Extreme Gradient Boosting for classification. Detection results are classified into two classes, namely seizures and non-seizures. The EEG recording data used came from CHIB MIT Hospital Boston which was obtained online. In the classification process, this study uses four comparisons of the percentage of training data and test data as well as tuning parameters which are processed by Randomized Search Cross Validation. The combination of these methods produces the highest accuracy, namely 85.15% which is produced by the percentage of 80% training data and 20% test data. However, these results experienced a high overfitting of 13.54%. As for the most fit results produced by the research, namely an accuracy value of 81% with a training score of 88.65% and a test score of 81.20% resulting from a percentage of 80% training data and 20% test data.
Prediksi Kelulusan Mahasiswa Prodi Informatika dengan Algoritma Decision Tree (C4.5) dan Naïve Bayes Steven Gerrard; Ade Eviyanti; Hamzah Setiawan; Ika Ratna
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1035

Abstract

The primary parameter for measuring higher education quality, which also has a crucial impact on the accreditation process, is the percentage of students graduating on time. However, the reality on the ground shows that many students face obstacles in completing their studies within the ideal timeframe. Therefore, a data-driven strategy is needed to project students' chances of graduation early. This research aims to compare the performance of the Decision Tree (C4.5) and Naïve Bayes algorithms in classifying the potential for on-time graduation. The data utilized included 161 entries from the Informatics Study Program, class of 2022, at the University of Muhammadiyah Sidoarjo. The attributes analyzed were divided into academic and non-academic factors, including gender, first-semester social studies grades (IPS), GPA, PKMU (Community Service Program) graduation score and status, BQ and Ibadah scores, and accumulated SKEK points. The research process went through several phases: preprocessing, class labeling, model development, and performance evaluation through a confusion matrix and 5-fold cross-validation. The test was validated by separating the training and test data into ratios of 70:30, 80:20, and 90:10. Based on the test results, the C4.5 algorithm achieved a peak accuracy of 100% across all ratio scenarios, with an average cross-validation accuracy of 96.88%. Meanwhile, Naïve Bayes achieved a maximum accuracy of 94.13% with an average cross-validation of 93.00%. These findings indicate that the C4.5 algorithm has superior performance on this specific dataset. The output of this predictive model is expected to serve as an objective basis for institutions in establishing proactive academic policies.
Co-Authors Erlina Agustin Hamzah Setiawan Ika Ratna Nuril Lutvi Azizah Steven Gerrard