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All Journal Journal of Education Technology Buletin Ilmiah Sarjana Teknik Elektro Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT)
Ibrahim, Sutrisno
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Computer Science & IT Control & Systems Engineering 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.
Application of LSTM Algorithm to Assist Diagnosis of Epilepsy Based on Electroencephalogram (EEG) Signals Ibrahim, Sutrisno; Zebua, Kaleb Nathan; Rahutomo, Faisal; Naufal, Muhammad Alif Rizky
Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 1 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.7.1.100360

Abstract

Epilepsy is a common disease that affects the brain's ability and has the potential to destroy the quality of life of sufferers. Diagnosis of epilepsy can be done by clinical testing and by using the electroencephalography (EEG) method. This research aims to apply artificial intelligence to improve the effectiveness and accuracy of EEG signal analysis. Epilepsy diagnosis is done automatically based on trained EEG signal files. This application can be done by applying the Long-Short Term Memory (LSTM) machine learning algorithm for recognizing patterns from brain signals that lead to epilepsy. The development was carried out using the EEG signal dataset from the University of Bonn which consists of 5 data sets. The detection process consists of the stages of data loading, augmentation, filtering, training, and classification. The developed system will be loaded into a GUI to facilitate users. The result of this research is a machine learning model with Long Short-Term Memory (LSTM) algorithm that has an accuracy rate of 91%, validation accuracy of 94% and loss of 0.2. Compared to other machine learning approaches such as SVM, KNN, and ANN, the proposed method achieves higher accuracy without the need for explicit feature extraction, highlighting its effectiveness in time-series signal classification. The model evaluation results show that this research is successful in assisting the detection of epilepsy using EEG signals with a high level of accuracy and efficiency.
Implementation of Object Detection Method for Intelligent Surveillance Systems at the Faculty of Engineering, Universitas Sebelas Maret (UNS) Surakarta Fauzan, Aris Maulana; Ibrahim, Sutrisno; Sulistyo, Meiyanto Eko
Journal of Electrical, Electronic, Information, and Communication Technology Vol 4, No 1 (2022): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.4.1.61197

Abstract

The number of positive Covid-19 cases in Indonesia continue to increase. This increase influenced by the behavior of Indonesian citizens in dealing with the pandemic, one of which is rarely wearing masks. In this study, we implemented an object detection method for intelligent surveillance systems (ISS) at the Faculty of Engineering, Universitas Sebelas Maret (UNS), Surakarta. By implementing face detection and mask detection, the surveillance system can recognize whether a person in a CCTV video frame is wearing a mask or not. In addition, deep metric learning and histogram of gradient (HOG) are applied to recognize faces of unmasked people in images. The test results show that the surveillance system can recognize the use of masks with 75%-87% accuracy rate. Furthermore, the accuracy rate for facial recognition on images ranges from 69% -100% for each person
AR-NAVIS: Mobility Application for Blind and Deaf Students Based on Augmented Reality Saputro, Joko Slamet; Gunardi, Gunardi; Anggarani, Fadjri Kirana; Anastasya, Najya; Setiabudi, Reva; Ibrahim, Sutrisno
Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 1 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.6.1.85475

Abstract

Students with disabilities, especially those with visual and hearing impairments, face challenges in navigating through the campus environment. Hence, the development of AR-NAVIS as an Augmented Reality (AR)-based mobility orientation application stands as a significant innovation in providing services for them. This application aims to assist disabled students in moving within the campus environment, both indoors and outdoors. AR-NAVIS identifies the safest and most efficient routes, enabling disabled students to engage in independent activities and enhancing both their academic and non-academic performance. The application development process involves analyzing students' needs, prototype design, model validation, trials, and dissemination. Its features include AR-based 3D guidance, directional text, voice, vibration mode, and hazard information. The app is expected to provide accurate information about buildings or locations that are the destination for disabilities students. The result show that application development can guide disabilities user move between buildings smoothly. The experiment found that there was an increase in student activity after having this application.
Co-Authors Adriyanto, Feri Aljalal, Majid Anastasya, Najya Anwar, Miftahul Fadjri Kirana Anggarani Faisal Rahutomo Fauzan, Aris Maulana Gunardi Gunardi Henda, Reihan Joko Slamet Saputro Latifan, Syaifullah Filard Meiyanto Eko Sulistyo Naufal, Muhammad Alif Rizky Setiabudi, Reva Zebua, Kaleb Nathan