Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal EMITTER International Journal of Engineering Technology CARADDE: Jurnal Pengabdian Kepada Masyarakat INOVTEK Polbeng - Seri Informatika
Dewi Mutiara Sari
Politeknik Elektronika Negeri Surabaya
Computer Science & IT Education Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Pemanfaatan Perangkat Mengajar Digital Guna Mendukung Blended Learning Maretha Ruswiansari; Bayu Sandi Marta; Dewi Mutiara Sari; Dias Agata; Heny Yuniarti
CARADDE: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2021): Agustus
Publisher : Ilin Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31960/caradde.v4i1.723

Abstract

Pandemi Covid-19 mendorong semua sekolah untuk beradaptasi dengan bentuk kegiatan pembelajaran secara daring. Namun, tidak semua sekolah dapat menerapkan metode pembelajaran daring tersebut dengan baik. Salah satunya yaitu SMK Islam Al Amal yang belum tersedia perangkat mengajar yang mendukung pembelajaran daring di sekolah. Hal tersebut membuat para guru kesulitan dalam menggunakan aplikasi pembelajaran daring. Kegiatan pengabdian masyarakat yang dilaksanakan bertujuan membantu para guru untuk dapat memanfaatkan perangkat mengajar digital. Metode yang digunakan dalam pelaksanaannya yaitu dengan memberikan pelatihan kepada para guru agar dapat melaksanakan model pembelajaran blended learning dengan optimal. Para guru sangat antusias selama pelatihan dimana mereka belajar penggunaan perangkat dan aplikasi yang mendukung mengajar daring sesuai yang mereka butuhkan saat ini. Hasil pelatihan menunjukkan peningkatan kemampuan peserta dalam memahami dan mempraktikkan penggunaan pen tablet, mic condenser, webcam, dan green screen yang diintegrasikan dengan aplikasi Zoom dan Open Broadcaster Software.  
Density-based Clustering for 3D Stacked Pipe Object Recognition using Directly-given Point Cloud Data on Convolutional Neural Network Alfan Rizaldy Pratama Pratama; Bima Sena Bayu Dewantara; Dewi Mutiara Sari; Dadet Pramadihanto
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.704

Abstract

One of the most commonly faced tasks in industrial robots is bin picking. Much work has been done in this related topic is about grasping and picking an object from the piled bin but ignoring the recognition step in their pipeline. In this paper, a recognition pipeline for industrial bin picking is proposed. Begin with obtaining point cloud data from different manner of stacking objects there are well separated, well piled, and arbitrary piled. Then followed by segmentation using Density-based Spatial Clustering Application with Noise (DBSCAN) to obtain individual object data. The systems then use Convolutional Neural Network (CNN) that consume raw point cloud data. Performance of the segmentation reaches an impressive result in separating objects and network is evaluated under the varying style of stacking objects and give the result with average Accuracy, Recall, Precision, and F1-Score on 98.72%, 95.45%, 99.39%, and 97.33% respectively. Then the obtained model can be used for multiple objects recognition in one scene.
Underwater Single and Multiple Objects Detection Based on the Combination of YOLOv7-tiny and Visual Feature Enhancement Dewi Mutiara Sari; Bayu Sandi Marta; R. Haryo Dwito Armono; Alfan Rizaldy Pratama; Firmansyah Putra Pratama
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/91b9qn06

Abstract

Breakwater construction in Indonesia frequently employs tetrapods to dissipate wave energy. However, the placement process remains manual, relying on divers to guide crane operators. This approach not only poses safety risks but also limits visibility due to underwater turbidity. While prior research has focused on underwater image enhancement, the integration of tetrapod object detection remains unexplored. This study proposes a combined method of underwater image enhancement and tetrapod object detection to support land-based operator visualization. Auto-level filtering and histogram equalization techniques were applied to enhance image clarity, followed by object detection using the YOLOv7-tiny model. Tetrapod models at a 1:20 scale were used for training and testing. The proposed system achieved a mean average precision (mAP) of 0.95. Evaluation was conducted across 12 scenarios, involving four lighting levels and two water conditions: clear and 45.8% turbidity. The object detection confidence scores were 0.80 without enhancement, 0.85 with histogram equalization, and 0.84 with auto-level filtering. Multiple object detection achieved an accuracy of 88.75%, outperforming previous approaches using YOLOv4-tiny. The results demonstrate the potential of integrating image enhancement and deep learning-based object detection for improving underwater operational safety and placement precision in breakwater construction.
Co-Authors Alfan Rizaldy Pratama Alfan Rizaldy Pratama Pratama Bima Sena Bayu Dewantara Dadet Pramadihanto Dias Agata Firmansyah Putra Pratama Heny Yuniarti Maretha Ruswiansari, Maretha Marta, Bayu Sandi R. Haryo Dwito Armono