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All Journal Journal of Education Technology Buletin Ilmiah Sarjana Teknik Elektro
Ibrahim, Sutrisno
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Computer Science & IT Education Electrical & Electronics Engineering

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Project-Based Learning Approach in Autonomous Vehicle Course During Pandemic Outbreak: A Study Case Saputro, Joko Slamet; Adriyanto, Feri; Anwar, Miftahul; Ibrahim, Sutrisno; Latifan, Syaifullah Filard
Journal of Education Technology Vol. 8 No. 3 (2024): August
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jet.v8i3.76342

Abstract

The COVID-19 pandemic has had a major impact on the education sector, including universities, which must immediately switch from classical learning to online learning. In the Electrical Engineering study program at Sebelas Maret University, one of the main challenges is achieving the learning output targets set in the courses, especially in the Autonomous Vehicles course. This study aims to examine the implementation of the Project-Based Learning (PBL) approach in the Autonomous Vehicles course during the pandemic, as well as to understand students' perceptions of the learning methods applied, including through online learning with blended learning. The study used a case study approach, where students were divided into three groups to work on an autonomous vehicle simulation project using Webots software. Each group faced different challenges, such as avoiding obstacles, overtaking other vehicles, and self-parking. A questionnaire was used to collect data on students' perceptions of the implementation of PBL. The results showed that PBL was successfully implemented well in the Autonomous Vehicles course, although there were several challenges, especially related to the duration of the project. Overall, students felt that this method was effective in improving their technical and soft skills, such as critical thinking, problem solving, and teamwork.
Comparison of Machine Learning Algorithms with Feature Engineering for Epileptic Seizure Prediction Based on Electroencephalogram (EEG) Signals Ibrahim, Sutrisno; Rahutomo, Faisal; Henda, Reihan; Aljalal, Majid
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.13145

Abstract

Epilepsy is a neurological disorder marked by recurrent seizures, which can greatly reduce patients' quality of life. Early and accurate seizure prediction is essential for effective clinical intervention and patient safety. This study proposes and evaluates a seizure prediction system using EEG signals processed through machine learning techniques combined with optimized feature extraction methods. The research contribution is the comprehensive comparative analysis of classifier-feature pairs for identifying the most effective configuration for seizure prediction tasks. Three classifiers—Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)—were systematically compared, each combined with precisely engineered feature extraction methods, including Common Spatial Pattern (CSP), Discrete Wavelet Transform (DWT), statistical features, and frequency domain features. EEG data from seven patients, totaling approximately 68 hours with 40 seizure events, were obtained from the Children's Hospital Boston database. The results demonstrate that XGBoost with CSP features achieved the highest overall accuracy at 88% and specificity at 88%, while XGBoost with DWT features reached the highest sensitivity at 87%. Additional metrics including F1-score (0.85) and AUC-ROC (0.91) confirmed XGBoost's superior performance. Comparison with five recent studies showed our approach offers a 3-5% improvement in accuracy and sensitivity. These findings highlight the critical impact of both classifier selection and feature engineering in improving EEG-based seizure prediction, with implications for developing real-time monitoring systems despite challenges in clinical implementation due to inter-patient variability.
Co-Authors Adriyanto, Feri Aljalal, Majid Anwar, Miftahul Faisal Rahutomo Henda, Reihan Joko Slamet Saputro Latifan, Syaifullah Filard