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All Journal MATAPPA: Jurnal Pengabdian Kepada Masyarakat Jurnal Elektronika dan Telekomunikasi
Egha Arya Affandi
Universitas Muhammadiyah Sidoarjo
Education

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Pelatihan Penggunaan Website bagi Guru dan Siswa SMK Hindarto Hindarto; Indah Apriliana Sari Wulandari; Fitria Eka Wulandari; Anisatul Makfiroh; Egha Arya Affandi
MATAPPA: Jurnal Pengabdian Kepada Masyarakat Volume 6 Nomor 3 Tahun 2023
Publisher : STKIP Andi Matappa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31100/matappa.v6i3.3066

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman keterampilan guru dan siswa SMK Muhammadiyah 2 Kutorejo Mojokerto dalam penggunaan website sebagai media pembelajaran dan peningkatan kualitas pendidikan. Pengabdian ini menggunakan pendekatan kuantitatif dengan desain pre-test dan post-test pada 20 guru dan siswa yang terlibat dalam pelatihan. Pelatihan dilakukan secara luring selama satu hari dengan materi yang disesuaikan dengan kebutuhan dan kemampuan peserta. Hasil penelitian menunjukkan bahwa pelatihan penggunaan website bagi guru dan siswa SMK Muhammadiyah 2 Kutorejo Mojokerto efektif dalam meningkatkan pemahaman dan keterampilan peserta. Rata-rata nilai pre-test peserta sebesar 51,6 meningkat menjadi rata-rata nilai post-test sebesar 88,2. Selain itu, sebagian besar peserta juga memberikan respon positif terhadap pelatihan ini. Pengabdian ini dapat menjadi referensi bagi institusi pendidikan lainnya dalam meningkatkan kualitas pembelajaran dengan menggunakan media digital seperti website. Diharapkan pelatihan ini dapat menjadi langkah awal dalam mendorong guru dan siswa untuk menggunakan website sebagai media pembelajaran yang efektif dan efisien
Epilepsy Classification Using Support Vector Machine with Frequency Domain Feature Extraction Hindarto Hindarto; Ade Eviyanti; Ahmad Ahfas; Egha Arya Affandi
Jurnal Elektronika dan Telekomunikasi Vol. 26 No. 1 (2026)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.797

Abstract

Epilepsy is a brain-related condition characterized by abnormal electrical activity in the brain. This condition can be identified by observing EEG signals, which record the brain's electrical activity. Automatically detecting seizures using EEG signals helps doctors diagnose the condition more quickly and accurately. In this study, a method is proposed that uses a Support Vector Machine (SVM) to classify EEG signals. The features used for classification are extracted from the frequency domain using a technique called Fast Fourier Transform (FFT). The dataset used is called the UCI Epileptic Seizure Recognition Dataset, which includes 11,500 EEG samples divided into five classes. These samples are then categorized into two main types: seizures and non-seizures. The research process includes data preprocessing with MinMaxScaler normalization, feature extraction using FFT, and data classification using SVM with varying numbers of features. Model performance is measured using several metrics, including accuracy, precision, recall, F1 score, and ROC-AUC. The results showed that the use of 21 features with the SVM model provided the best performance, with an accuracy of 97.7%, a precision of 93.2%, a recall of 95.4%, an F1 score of 94.3%, and an AUC value of 0.9930. These results are better than previous studies using similar methods, indicating that the combination of FFT and SVM is effective for detecting epilepsy using EEG signals. These findings help build a more reliable system for medical diagnosis and highlight the importance of using balanced evaluation measures in healthcare.
Co-Authors Ade Eviyanti Ahmad Ahfas Anisatul Makfiroh Fitria Eka Wulandari Hindarto Indah Apriliana Sari Wulandari