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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer INTEGER: Journal of Information Technology Jurnal Pengabdian kepada Masyarakat Nusantara Best : Journal of Applied Electrical, Science and Technology JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) SENTRI: Jurnal Riset Ilmiah Jurnal Cakrawala Maritim ORAHUA : Jurnal Pengabdian Kepada Masyarakat
Brian, Thomas
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Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Physics Social Sciences Other

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Penyuluhan Budidaya Perairan Terhadap Masyarakat Tanjung Tembaga Probolinggo untuk Mendorong Transformasi Ekonomi Biru Sukarno, Friska Intan; Widyastuti, Indri Ika; Wibowo, Sekarsari; Brian, Thomas; Pujiputra, Anggarjuna Puncak; Pratama , Moh. Andris; Rahayu, Putri Nur
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 2 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi April - Juni
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i2.6199

Abstract

Nelayan pada umumnya menggantungkan aktivitas ekonominya pada sumber daya laut dan pesisir melalui penangkapan ikan secara langsung, namun cara yang digunakan cenderung tidak efektif dan sangat bergantung pada kondisi cuaca, sehingga hasil tangkapan menjadi tidak stabil.  Metode yang digunakan meliputi penyuluhan, diskusi partisipatif, dan evaluasi melalui survei serta wawancara kepada nelayan peserta kegiatan. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pengetahuan dan kesadaran masyarakat pesisir Tanjung Tembaga, Kota Probolinggo, mengenai alternatif usaha perikanan yaitu budidaya laut. Permasalahan yang dihadapi nelayan lokal saat ini adalah ketergantungan terhadap hasil tangkapan yang cenderung menurun akibat overfishing dan perubahan iklim. Hasil kegiatan menunjukkan bahwa 82% peserta memperoleh pemahaman baru tentang budidaya perairan berbasis Keramba jaring apung, dan 90% menyatakan ketertarikan untuk mencoba usaha tersebut.  Kegiatan ini juga berhasil mengubah persepsi masyarakat terhadap potensi ekonomi budidaya laut dan meningkatkan keterlibatan dalam pengelolaan sumber daya pesisir secara berkelanjutan. Dengan demikian, program ini memberikan dampak positif dalam mendorong transformasi ekonomi biru melalui peningkatan kapasitas dan kemandirian nelayan lokal.
Optimasi Parameter Operasional Mini Pembangkit Listrik Tenaga Angin Berbasis Machine Learning untuk Meningkatkan Output Daya Parman, Parman; Hamzah, Fais; Basya Shahrys Tsany, Rahmat; Brian, Thomas; Nizar Zulfika, Dicki
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7578

Abstract

The utilization of renewable energy is experiencing significant growth, with wind turbines emerging as a key solution for generating environmentally friendly electricity. However, the efficiency of wind turbines is highly dependent on their operational parameters, such as wind speed, blade size, angular velocity, and torque. This research aims to optimize the operational parameters of small-scale wind turbines using an XGBoost-based Machine Learning model and an L-BFGS-B algorithm-based optimization method. A simulation dataset was generated based on the physical equations of wind turbine power and a MATLAB Simulink model, incorporating added noise to approximate real-world conditions. The XGBoost model was trained to predict the turbine's output power based on its operational parameters. Subsequently, an optimization method was employed to identify the parameter combination that yields maximum power. The experimental results demonstrate that the model exhibits strong performance, characterized by a low Mean Squared Error (MSE) and a high R-squared score. The optimization process successfully achieved a significant increase in power output compared to the initial configuration. Through this approach, wind turbine systems can operate more efficiently and generate optimal electrical power. This study contributes to the advancement of artificial intelligence-based optimization strategies for renewable energy systems.
Segmentasi Citra Pengelasan Kapal Menggunakan Convolutional Neural Network Brian, Thomas; Pujiputra, Anggarjuna Puncak; Putri Nur Rahayu; Augustino, Immanuel Freddy; Parman, Parman; Bone, Iskia Ipan Dua’
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 10 (2025): SENTRI : Jurnal Riset Ilmiah, Oktober 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i10.4767

Abstract

Welding inspection plays a critical role in the shipbuilding industry to ensure the integrity and quality of welded joints. However, the prevailing manual inspection procedures are inherently subjective, prone to bias, and result in inconsistent quality assessments. Therefore, there is a strong need for an automated and intelligent system capable of objectively detecting welding points. To address this, we propose an advanced segmentation model based on deep learning and computer vision techniques, specifically utilizing the enhanced Nested UNet architecture with extensive architectural modifications and comprehensive hyperparameter tuning. To further optimize the segmentation performance, we systematically compare different convolutional blocks integrated into the network architecture. The dataset used consists of 548 welding images. Each image is manually annotated using the VGG Image Annotator (VIA) application by marking the weld point areas as polygons. This research focuses on the development of a Nested UNet model, a deep learning-based image segmentation model for detecting weld points from previous models using the UNet architecture. During the training process, performance on both the training and validation datasets is continuously monitored and recorded. This results in several logs recording the training loss, validation loss, training IoU, and validation IoU for each of the three types of convolutional blocks used in the dense bottleneck. Our experimental evaluation shows that the use of VGG - Dense - VGG convolutional blocks in Nested UNet yields the highest performance, achieving a Training Dice score of 0.92970 and a Validation Dice score of 0.89695 on our collected dataset.
Desain dan Simulasi Sistem Pembangkit Listrik Tenaga Bayu Skala Kecil Untuk Penerangan di Perahu Nelayan Parman, Parman; Tsany, Rahmat Basya Shahrys; Zulfika, Dicki Nizar; Brian, Thomas
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 11 (2025): SENTRI : Jurnal Riset Ilmiah, November 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i11.4888

Abstract

The utilization of renewable energy in the maritime sector serves as a strategic solution to reduce fishermen's dependence on expensive and environmentally unfriendly fossil fuels. This study develops a mini Wind Power Plant (PLTB) model that can be implemented on fishing boats using a MATLAB Simulink-based simulation approach. The study encompasses turbine aerodynamic design, generator selection, and control system optimization to enhance the efficiency of wind energy conversion into electrical power. Simulation results indicate that at an average wind speed of 7.7 m/s, the designed mini wind turbine is capable of generating 125.8 Watts of mechanical power with a peak torque of 3.44 Nm at a wind speed of 12 m/s. The study also calculates the turbine shaft size at 6 mm, the turbine-side pulley diameter at 70 mm, and the generator-side pulley diameter at 39.63 mm, with a belt drive length of 473.75 mm, based on the generator specifications of 1500 RPM and 81% efficiency. Considering the dynamic maritime environment, the design is evaluated through performance simulations using MATLAB Simulink to ensure power output stability under various wind speed conditions. The analysis demonstrates that the system can maintain wind energy conversion efficiency at optimal wind speeds and has significant potential for small-scale applications.
Co-Authors Aisyi Maulidhia, Alief Nur Al Hazman, Muhammad Alwi Sina Khaqiqi Ardiana, Mirza Atmiasri Augustino, Immanuel Freddy Bone, Iskia Ipan Dua’ Fais Hamzah Gastriani, Ovi Prina Maulidhia, Alief Nur Aisyi Nisazarifa, Adristi Nizar Zulfika, Dicki Nur Rahayu, Putri Parman parman parman Pratama , Moh. Andris Pujiputra, Anggarjuna Puncak Puncak Pujiputra, Anggarjuna Putri Nur Rahayu Rahayu, Putri Nur Rahayuningsih, Siti Sanwidi, Ardhi Sholikah, Evi Nafiatus Sholikhah, Evi Nafiatus Sukarno, Friska Intan Tsany, Rahmat Basya Shahrys Tsany, Rahmat Basya Sharys Wibowo, Sekarsari Widyastuti, Indri Ika Zulfika, Dicki Nizar