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All Journal QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT JURNAL CRANKSHAFT SENTRI: Jurnal Riset Ilmiah Journal Of Artificial Intelligence And Software Engineering
Augustino, Immanuel Freddy
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Peningkatan Kesadaran Masyarakat Terhadap Keselamatan dan Kesehatan Kerja (K3) di Bidang Pengelasan: Studi Kasus pada Proyek Pariwisata Desa Bachtiar; Widodo, Eriek Wahyu Restu; Prastyawan, Rikat Eka; Mukhlis; Wahyudi, M. Thoriq; Ari, Muhamad; Munir, Moh. Miftachul; Kusminah, Imah Luluk; Andiana, Rocky; Augustino, Immanuel Freddy; Andika, Yudi; Sa'diyah, Rahmatus; Habibi, Tsaaqif Haani
Qardhul Hasan: Media Pengabdian kepada Masyarakat Vol. 11 No. 2 (2025): Qardhul Hasan: Media Pengabdian kepada Masyarakat
Publisher : Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/qh.v11i2.20927

Abstract

Meningkatkan pemahaman tentang Keselamatan dan Kesehatan Kerja (K3) sangat krusial, terutama bagi pekerja yang terlibat dalam lingkungan kerja berisiko tinggi seperti proses pengelasan. Risiko kecelakaan di sektor ini tidak hanya berpotensi merusak material, tetapi juga dapat menyebabkan cedera serius hingga mengancam nyawa. Salah satu tantangan utama yang dihadapi oleh mitra dalam kegiatan pengabdian masyarakat ini adalah rendahnya tingkat kesadaran dan pengetahuan terkait keselamatan kerja, khususnya dalam bidang pengelasan. Untuk itu, pelatihan mengenai pengenalan dan penerapan K3 perlu dilakukan secara terstruktur guna membentuk tenaga kerja yang sehat, aman, dan profesional. Program pelatihan ini menyasar warga Desa Balun dan dilaksanakan melalui beberapa tahapan, mulai dari persiapan, pelaksanaan, hingga evaluasi. Materi yang disampaikan mencakup prinsip dasar K3 dalam pekerjaan pengelasan dan praktik penggunaan alat pelindung diri. Seluruh proses dipersiapkan dengan matang agar pelatihan berjalan efektif. Harapannya, kegiatan ini dapat meningkatkan kesadaran masyarakat terhadap pentingnya keselamatan kerja, mengurangi potensi kecelakaan, dan menciptakan lingkungan kerja yang lebih aman dan produktif di sektor pengelasan.
Butterfly Feature Extraction Using HSV, Lacunarity, and CNN Rahayu, Putri Nur; Sukarno, Friska Intan; Augustino, Immanuel Freddy; Yuniati, R. A. Norromadani; Rakhmadi, Ardhon
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6876

Abstract

This study aims to extract the morphological features of butterflies using the HSV (Hue, Saturation, Value) and lacunarity. The HSV method is used to obtain color information from butterfly images. lacunarity is used to extract texture characteristic to enhance the visual representation of the object. These extracted features are used as input for the processing of classification using algorithm of Convolution Neural Network (CNN). Based on the experimental result, the classification has accuracy 70%. This accuracy indicates that the combination of HSV and lacunarity methods is sufficiently effective in describing of the visual butterflies features for automatic classification.
ANALISIS EXPERIMENTAL STRATEGI PERLAKUAN PANAS UNTUK PENYUSUNAN WPS SAMBUNGAN LAS BAJA COR AAR M201 GR.B+ Augustino, Immanuel Freddy; Zulfika, Dicki Nizar; Azhad, Faizur Rijal; Tsany, Rahmat Basya Shahrys
JURNAL CRANKSHAFT Vol 8, No 3 (2025): Jurnal Crankshaft Vol. 8 No. 3 (2025)
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/cra.v8i3.15697

Abstract

Penelitian ini bertujuan mengevaluasi dan mengoptimalkan strategi perlakuan panas terhadap kualitas sambungan las komponen bogie kereta api berbahan casting AAR M201 Grade B+ berdasarkan standar AWS D15.1. Pengelasan dilakukan menggunakan metode Shielded Metal Arc Welding (SMAW) dengan elektroda E8018-B2, melalui empat variasi perlakuan: tanpa perlakuan panas, preheat 100°C, PWHT saja, serta kombinasi preheat dan PWHT. Pengujian meliputi uji tarik, tekuk, kekerasan, impak, radiografi, dan analisis struktur mikro. Hasil menunjukkan bahwa perlakuan panas tidak secara signifikan memengaruhi kekuatan tarik, namun mampu meningkatkan ketangguhan zona HAZ hingga 62%, menurunkan kekerasan hingga 19% akibat pelunakan dan pelepasan tegangan, serta menghasilkan struktur mikro yang lebih homogen. Kombinasi preheat dan PWHT dinilai paling optimal dalam meningkatkan performa sambungan, meskipun ketangguhan logam las sedikit menurun. Temuan ini mendasari penyusunan Welding Procedure Specification (WPS) dan Procedure Qualification Record (PQR) yang valid dan mendukung proses pengelasan bogie kereta api yang aman dan andal.
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.
Co-Authors Andiana, Rocky Ardhon Rakhmadi, Ardhon Azhad, Faizur Rijal Bachtiar Bone, Iskia Ipan Dua’ Brian, Thomas Eriek Wahyu Restu Widodo Habibi, Tsaaqif Haani Kusminah, Imah Luluk M. Thoriq Wahyudi Muhamad Ari Mukhlis Munir, Moh. Miftachul parman parman Prastyawan, Rikat Eka Pujiputra, Anggarjuna Puncak Putri Nur Rahayu Rahayu, Putri Nur Sa'diyah, Rahmatus Sukarno, Friska Intan Tsany, Rahmat Basya Shahrys Yudi Andika Yuniati, R. A. Norromadani Zulfika, Dicki Nizar