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All Journal snimed Jurnal Teknoin Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Bahan Alam Terbarukan Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Ilmiah Matrik Communications in Science and Technology Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Biomedical Science and Bioengineering Jurnal Elektro Kontrol (ELKON)
Alvin Sahroni
Electrical Engineering Department, Faculty Of Industrial Technology, Universitas Islam Indonesia
Aerospace Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Single Channel Electrogastrogram Frequency Domain Analysis and Correspondence to Brain Activity in a Resting State Condition Sahroni, Alvin; Miladiyah, Isnatin; Adinandra, Sisdarmanto; Sofyan, Pramudya Rakhmadyansyah; Anora, Levina; Hanafi, Mhd.
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.590

Abstract

An electrogastrogram (EGG) is a well-known method to record gastric myoelectrical activity. However, some researchers believe that EGG measures the gastric slow wave and can be used as a surrogate for gastric motility, whereas others claim that EGG is flawed. Our proposed study broadens the scope of EGG research, particularly by offering the opportunity to observe gut-brain signaling pathways, which can enhance our understanding of brain properties and behavior in response to psychological changes. This study focuses on how to confirm single-channel EGG's setup with public datasets and previous studies and how to observe the relationship of gut-brain axis pathways. We gathered four subjects utilizing a 250 Hz bioamp to monitor brain wave activity on the head and scalp including gastric activity, and used Zenodo's EGG dataset for the confirmation phase. We placed single-channel electrodes around the stomach to investigate gastric myoelectrical activity and extracted the EGG's power spectrum using a specific band-pass filter (0.03 - 0.07 Hz). We extracted the EGG's power spectrum and dominant frequency as our main features. Regarding brain electricity activities, we applied the FIR filter to obtain each brain wave's properties. We found that each subject had different responses during pre- and postprandial, both from primary and secondary resources. We found that the increase in EGG activity caused a change in EEG properties, particularly in the alpha band (8-12 Hz). Additionally, the EEG P3 site in the parietal lobe followed the power change rates of the EGG between 0 to 0.015 of relative power. We conclude that P3 and slow-wave gastric movement from EGG correspond to each other and reflect gut-brain axis pathways. However, future studies with larger samples must strengthen our findings according to the gut-brain axis pathways in the P3 site and EGG
MENGATASI KONDISI PARTIAL SHADING PADA KINERJA SISTEM PANEL SURYA : KAJIAN LITERATUR Prabowo, Widodo Hadi; Setiawan, Hendra; Sahroni, Alvin
Jurnal Elektro Kontrol (ELKON) Vol 5, No 1 (2025): Jurnal ELKON
Publisher : Teknik Elektro Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/elkon.v5i1.15545

Abstract

Sistem panel surya merupakan salah satu solusi utama dalam pemanfaatan energi terbarukan, namun sering menghadapi masalah shading parsial yang dapat menurunkan efisiensi sistem hingga 50%. Banyak penelitian telah membahas berbagai algoritma MPPT (Maximum Power Point Tracking), namun sebagian besar fokus pada pengoptimalan MPPT pada kondisi pencahayaan yang stabil, dan belum banyak yang membahas penerapan MPPT dengan DC-DC Boost Converter dalam kondisi shading parsial. Shading parsial dapat menyebabkan titik daya maksimum yang lebih dari satu, sehingga mengganggu identifikasi titik daya maksimum global (GMPP) oleh sistem MPPT. Untuk itu, artikel ini bertujuan untuk mengkaji penerapan berbagai metode MPPT, termasuk metode konvensional (Perturb and Observe, Incremental Conductance), metaheuristik (Particle Swarm Optimization, Differential Evolution), dan metode hibrida yang menggabungkan teknik-teknik tersebut dengan DC-DC Boost Converter untuk meningkatkan efisiensi dalam kondisi shading parsial. Metode kajian literatur yang digunakan adalah pendekatan sistematis melalui pencarian literatur di database ilmiah seperti ScienceDirect, IEEE Xplore, dan Google Scholar. Pencarian dilakukan dengan kata kunci seperti “MPPT with DC-DC Boost Converter,” “Partial Shading,” dan “Photovoltaic Systems” dengan publikasi antara 2015 hingga 2025. Dari 30 artikel yang terpilih, hasil perbandingan menunjukkan bahwa metode hibrida, khususnya kombinasi Perturb and Observe (PO) dengan Artificial Neural Network (ANN), memiliki waktu steady-state tercepat (0,01 hingga 0,15 detik) dan efisiensi mencapai 99,99%. Metode ini juga mengurangi osilasi daya dan meningkatkan stabilitas output. Sebaliknya, metode konvensional menunjukkan efisiensi lebih rendah dan waktu steady-state yang lebih lama. Temuan ini menekankan pentingnya pengembangan metode MPPT hibrida dengan DC-DC Boost Converter untuk meningkatkan efisiensi sistem fotovoltaik pada kondisi shading parsial yang sering terjadi di lapangan.
Analisis Tren Historis Dan Prediksi Beban Listrik Pada Tenaga Listrik Menggunakan Artificial Neural Network Dengan Metode Backpropagation: Systematic Literature Review Septient Malini, Regina; Sahroni, Alvin; Setiawan, Hendra
Jurnal Ilmiah Matrik Vol. 27 No. 2 (2025): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/8kyfgz81

Abstract

Electric load forecasting is a critical step in ensuring the reliability of power systems amid rising energy demand driven by digitalization, industrialization, and urbanization. This article presents a Systematic Literature Review (SLR) on the application of Artificial Neural Networks (ANN) with backpropagation algorithms for load prediction based on historical data, employing the PRISMA framework for study screening and selection. The review analyzes nine relevant national journals to identify trends in accuracy, network configurations, and model effectiveness. Findings indicate that ANN with backpropagation can achieve low prediction error rates, such as a Mean Absolute Percentage Error (MAPE) of 0.05% in industrial sectors and up to 99.88% accuracy in specific cases. ANN also demonstrates strong capability in capturing dynamic changes in energy consumption, making it a reliable method for supporting operational planning and efficient electricity distribution. Despite promising performance, several aspects remain underexplored, including more complex ANN architectures, hyperparameter tuning techniques, limited cross-regional validation, and insufficient comparative analysis with alternative methods such as ensemble learning or deep learning-based algorithms. This review offers comprehensive insights into the integration of artificial intelligence in power systems and lays the groundwork for developing more adaptive, precise, and broadly generalizable load forecasting strategies in the future.
Co-Authors Aisha Widi Rahayu Anora, Levina Damar Anggit Eki Dipo Laksono Erlina Marfianti, Erlina Faizal Mahananto Hanafi, Mhd. Hendra Setiawan Isnatin Miladiyah Izza Alifa Hassya Khamdan Cahyari Medilla Kusriyanto, Medilla Nuryadi, Satyo Prabowo, Widodo Hadi Septient Malini, Regina Sisdarmanto Adinandra, Sisdarmanto Sofyan, Pramudya Rakhmadyansyah Zakaria, Hasballah