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All Journal ELKHA : Jurnal Teknik Elektro Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sinergi Jurnal Teknologi Elektro JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING InComTech: Jurnal Telekomunikasi dan Komputer Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Pengabdian Al-Ikhlas Jurnal Informatika dan Rekayasa Elektronik Jurnal Abdi Masyarakat (JAM) Jurnal Pengabdian dan Peningkatan Mutu Masyarakat (JANAYU) Jurnal Ilmiah Teknik Elektro
Ketty Siti Salamah
Universitas Mercu Buana
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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E-Beacon Card Training Based Application Internet Of Things (IOT) in The School Environment Simanjuntak, Imelda Uli Vistalina; Rahmawati, Yosy; Salamah, Ketty Siti; Dani, Akhmad Wahyu; Yuliza, Yuliza
ABDIMAS: Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2023): ABDIMAS UMTAS: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM Universitas Muhammadiyah Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35568/abdimas.v6i2.3312

Abstract

At that time, SMK Yadika 11 Jatirangga Bekasi still needed help communicating announcements within the school. In addition to being expensive, buying an intercom is also less effective during the teaching and learning process due to noise pollution. Therefore the PPM Team at Mercu Buana University wanted to provide a solution by introducing Internet of Things (IoT) technology on one of the Bluetooth ebacon devices. eBeacon Card is a Bluetooth Low Energy transmitter connected to various electronic devices. This device will be connected via a short message to each person's cell phone, such as an SMS notification. In this training, two eBeacon Cards uses, which should be applicable in two rooms with a radius of 20 m. However, due to space limitations, both are installed in one room. So that the target information announcement target can receive data from both eBeacon cards with the same announcement display twice. Some of the outputs used in evaluating this training were that they understood the IoT process, how to install and create eBeacon, and could use it for other needs such as announcements, advertisements, etc.
Real-time dental caries segmentation with an efficient Deformable U-Net (DU-Net) for teledentistry system Iklima, Zendi; Kadarina, Trie Maya; Salamah, Ketty Siti; Sentosa, Arrival Dwi
SINERGI Vol 29, No 2 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.2.015

Abstract

Digital technology has greatly improved teledentistry by facilitating telediagnostics and teleconsultations, particularly benefiting those in remote areas. Additionally, AI advancements enhance diagnostic accuracy and streamline clinical decision-making, reducing costs and resource disparities in dental care. This study presents an improved U-Net architecture, Deformable U-Net (DU-Net), for semantic dental caries segmentation, leveraging deformable convolutions to dynamically adjust sampling points for improved feature extraction and reduced computational redundancy. By connecting encoder-decoder blocks via skip-connections, the DU-Net architecture enables efficient real-time segmentation and balance accuracy while reducing computational demands. The deformable block in DU-Net and DDR U-Net shows a balanced performance and efficiency while maintaining accuracy despite reduced FLOPs. The proposed architecture was implemented in real-time dental caries segmentation on a Dual Core Cortex A72 system and web server. It shows a significant improvement in Dice score, reducing CPU and memory usage compared to conventional U-Net models. Moreover, the DU-Net and its half variants achieved competitive performance with much lower computational demands makes suitable for web servers and embedded applications. The result highlights the DU-Net capability to optimize both computational efficiency and segmentation accuracy, offering a promising solution for real-world applications where speed and resource management are critical, particularly in the medical imaging field.
The neural network adaptive behaviour model for localization and speed control in autonomous rescue mobile robot operation Hafizd Ibnu Hajar, Muhammad; Persada Nurani Hakim, Galang; Siti Salamah, Ketty; Septiyana, Diah
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9365

Abstract

In robotic operation, an autonomous operation for a mobile robot is needed to operate smoothly, hence, a control system is needed. Numerous architectures for robotics control systems have been put forth. Regretfully, creating a control system architecture is very challenging and occasionally results in inaccuracy in control. An alternative to conventional mobile robot control has emerged to address this issue: behavior-based control system architectures. This paper addresses the behavior of an autonomous mobile robot (AMR) control system in an outdoor rescue operation. The AMR behavior will be governed by the neural network methods, which are a computational intelligence to generate a dependable control algorithm. The architecture is used to coordinate behavior, especially to localize the victims, and for speed control to find the victim location with fast timing. In localization parameters to find the victim in the disaster area, this neural network adaptive model has the smallest error, which is 3.27, compared with other models such as free space model 43.46, and empirical model 4.735. While in robot speed parameter has a low error value, which is 1.47. With this small error, we can conclude that the neural network adaptive behaviour control architecture model for rescue mobile robot operation has been successfully developed.
Optimization of YOLOv4-Tiny Algorithm for Vehicle Detection and Vehicle Count Detection Embedded System Muwardi, Rachmat; Nugroho, Ivan Prasetyo; Salamah, Ketty Siti; Yunita, Mirna; Rahmatullah, Rizky; Chung, Gregorius Justin
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29693

Abstract

Currently, the implementation of object detection systems in the traffic sector is minimal. CCTV cameras on highways and toll roads are primarily used to monitor traffic conditions and document violations. However, the data recorded by these cameras can be further utilized to enhance traffic management systems. The author proposes a vehicle detection and counting system using YOLOv4-Tiny. The research aims to improve vehicle detection and counting accuracy by employing a median filter and grayscale processing, which simplify object detection. The proposed YOLOv4-Tiny algorithm has shown impressive results on various datasets, including MAVD, GRAM-RTM, and author dataset. The system achieved a detection accuracy of 98.95% on the MAVD dataset, 99.5% on the GRAM-RTM dataset (comparable to YOLOv4), and 99.1% on the author dataset. Furthermore, the system operates at 25 frames per second (FPS), a notably high rate compared to other methods. While the system demonstrates excellent accuracy in counting cars, it encounters some accuracy loss with other vehicle classifications. The author concludes that the system is highly suitable for real-world applications but notes that inaccurate labeling can lead to vehicle counting errors.
Co-Authors Adhi Ramli Agus Dendi Rochendi Akhmad Wahyu Dani Akhmad Wahyu Dani, Akhmad Wahyu Allya Alan Putra Syah Chung, Gregorius Justin Diah Sucita Safitri Dolly Lovfinha Putra Eko Ihsanto Ellisa Agustina Ellisa Agustina Ellisa Agustina, Ellisa Hafizd Ibnu Hajar, Muhammad Imelda Uli Vistalina Imelda Uli Vistalina Imelda Uli Vistalina Simanjuntak Imelda Uli Vistalina Simanjuntak Imelda Uli Vistalina Simanjuntak Julpri Andika Khoiron Amarullah Krisna Yudha Dewantara Loura Meilita Saragih Simarmata Lutfi Arif Setiyawan Mirna Yunita Muhammad Afif Assidiq Muhammad Hafizd Ibnu Hajar Muklas Iqbal Danifan Nugroho, Ivan Prasetyo Persada Nurani Hakim, Galang Rachmat Muwardi Rahmatullah, Rizky Samsul Anwar Sentosa, Arrival Dwi Septiyana, Diah Simanjuntak, Imelda Uli Vistalina Sulistyaningsih Sulistyaningsih Suwoyo, Heru Trie Maya Kadarina Triyanto Pangaribowo Yosy Rahmawati Yudhi Gunardi Yuliza Yuliza Yuliza Yuliza Yuliza Yuliza Zendi Iklima