Article Per Year (5 Year)
p-Index From 2020 - 2025
4.651
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan Geografi Undiksha Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sumatra Journal of Disaster, Geography and Geography Education Georafflesia : Artikel Ilmiah Pendidikan Geografi Jurnal Geografi Journal of Education and Instruction (JOEAI) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Abdihaz: Jurnal Ilmiah Pengabdian pada Masyarakat Indonesian Journal of Electrical Engineering and Computer Science Internet of Things and Artificial Intelligence Journal Ma'had Aly Journal Of Islamic Studies
Mirna Yunita
Universitas Prof. Dr. Hazairin, SH
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

Published : 30 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)

 1 
Fast Human Recognition System on Real-Time Camera Yuliza, Yuliza; Muwardi, Rachmat; Rhozaly, Mustain; Lenni, Lenni; Yunita, Mirna; Yehezkiel, Galatia Erica
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27009

Abstract

Technology development is very rapid, so all fields are required to develop technology to increase the effectiveness and efficiency of work. One of the focuses is related to image processing technology. We can get many benefits by implementing this system, so various fields have implemented image processing systems, such as security, health, and education. One of the current obstacles is in the area of safety, namely in the field of searching for people, which is still done manually. Often search teams find it challenging to find people because of the significant search area, low light conditions, and complex search fields. Therefore, we need a tool capable of detecting humans to assist in finding people. Therefore, to detect human objects, the authors try to research human object detection using a simple device for the human object detection system. The authors use the You only look once (YOLO) method with the YoloV4-Tiny type, where this algorithm has high detection speed and accuracy. Using the YOLOV4-Tiny simulation method for human object recognition, a detection rate of 100% is obtained with an FPS value of 5.
Design Human Object Detection Yolov4-Tiny Algorithm on ARM Cortex-A72 and A53 Muwardi, Rachmat; Faizin, Ahmad; Adi, Puput Dani Prasetyo; Rahmatullah, Rizky; Wang, Yanxi; Yunita, Mirna; Mahabror, Dendi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27402

Abstract

Currently, many object detection systems still use devices with large sizes, such as using PCs, as supporting devices, for object detection. This makes these devices challenging to use as a security system in public facilities based on human object detection. In contrast, many Mini PCs currently use ARM processors with high specifications. In this research, to detect human objects will use the Mini PC Nanopi M4V2 device that has a speed in processing with the support of CPU Dual-Core Cortex-A72 (up to 2.0 GHz) + Cortex A53 (Up to 2.0 GHz) and 4 Gb DDR4 Ram. In addition, for the human object detection system, the author uses the You Only Look Once (YOLO) method with the YoloV4-Tiny type, With these specifications and methods, the detection rate and FPS score are seen which are the feasibility values for use in detecting human objects. The simulation for human object recognition was carried out using recorded video, simulation obtained a detection rate of 0,9845 or 98% with FPS score of 3.81-5.55.  These results are the best when compared with the YOLOV4 and YOLOV5 models. With these results, it can be applied in various human detection applications and of course robustness testing is needed.
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.
Raspberry Pi 4 and Ultrasonic Sensor for Real-Time Waste Classification and Monitoring with Capacity Alert System Yuliza, Yuliza; Muwardi, Rachmat; Kusuma, Prima Wijaya; Lenni, Lenni; Rahmatullah, Rizky; Yunita, Mirna; Dani, Akhmad Wahyu
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30036

Abstract

The problem of waste management creates daily rubbish buildup due to thorough sorting. garbage sometimes accumulates in public garbage receptacles due to officials' ignorance of bin capacity and collectors' schedules, causing unclean conditions and the development of deadly diseases. Internet of Things technology was used to create a smart waste classification system with a notification mechanism in this study. This system classifies waste into plastic, metal, B3, and organic using a Raspberry Pi 4, camera module, and deep learning model. The classification uses a Convolutional Neural Network to speed up waste processing and separation. This research can be linked with research on separating trash types in one container and then allocated to garbage bins by type. Ultrasonic sensors and Raspberry Pi 4 can continuously monitor waste levels by sending data to the Ubidots IoT platform over HTTP. Based on experimental device data, system analysis shows 90% classification accuracy for all four waste categories. A Wireshark network analysis showed 61,098 bytes/s of throughput, 16 ms of delay, and zero data loss, demonstrating the system's ability for real-time monitoring and alerting. This research provides a realistic, cost-effective, and minimal solution to improve garbage classification and reduce collection costs to promote sustainability.
Co-Authors Adi, Puput Dani Prasetyo Ahmad Faizin, Ahmad Akhmad Wahyu Dani, Akhmad Wahyu Al Furkon Sabda Aradepa Al Ikhsan Rama Komura Aprianto Soni Ariani, Winda Ade Chung, Gregorius Justin Dedi Hermon Edwar Edwar EDWAR EDWAR Etika Sihaini Fevi Wira Citra Framujiastri, Novira Esa Guntar, Dedi Ihsan, Hafid Jarno Jarno Kartika, Eka Anggara Yuda Ketty Siti Salamah Kusuma, Prima Wijaya Lenni Lenni, Lenni Lola Octalia Mahabror, Dendi Maulidina, Rachmani Melvi Yansi Muhammad Alfi Muhammad Alfi Muhammad Ali Nugroho, Ivan Prasetyo Nurmintan Silaban Nurmintani Silaban Pangestu, Heru Edlifilyas Paus Iskarni Pratama, Fadly Wahyu Priyanto, Gusti Puspita, Veny putri febrianti Rachmat Muwardi Rahmatullah, Rizky Rahmiah Nur R Ramadhani, Rahmad Rhozaly, Mustain Riki Nopiansyah Sintia Safrianti Supriyono, Supriyono Tegar Aldaka Fikri Utami, Elva Wang, Yanxi Warsa Sugandi Warsa Sugandi Warsa Sugandi Karman Warsa Sugandi Karman Yanmesli Yehezkiel, Galatia Erica Yuliza Yuliza Yuneva, Yuneva Zairin Zairin Zairin Zairin Zairin Zairin Zairn