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All Journal JOIV : International Journal on Informatics Visualization Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat
Salam, Andi Ejah Umraeni
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Public Health

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A Thorough Review of Vehicle Detection and Distance Estimation Using Deep Learning in Autonomous Cars Rahmat, Muhammad Abdillah; Indrabayu, Indrabayu; Achmad, Andani; Salam, Andi Ejah Umraeni
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2665

Abstract

Autonomous vehicle technologies are rapidly advancing, and one key factor contributing to this progress is the enhanced precision in vehicle detection and distance calculation. Deep Learning Networks (DLNs) have emerged as powerful tools to address this challenge, offering remarkable capabilities in accurately detecting and estimating vehicle positions. This study comprehensively reviews DLN applications for vehicle detection and distance estimation. It examines prominent DLN models such as YOLO, R-CNN, and SSD, evaluating their performance on widely used datasets such as KITTI, PASCAL VOC, and COCO. Analysis results indicate that YOLOv5, developed by Farid et al. achieves the highest accuracy level with a mAP (mean Average Precision) of 99.92%. Yang et al. showcased that YOLOv5 performs exceptionally in detection and distance estimation tasks, with a mAP of 96.4% and a low mean relative error (MRE) of 10.81% for distance estimation. These achievements highlight the potential of DLNs to enhance the accuracy and reliability of vehicle detection systems in autonomous vehicles. The study also emphasizes the importance of backbone architectures like DarkNet 53 and ResNet in determining model efficiency. The choice of the appropriate model depends on the specific task requirements, with some models prioritizing real-time detection and others prioritizing accuracy. In conclusion, developing DLN-based methods is crucial in advancing autonomous vehicle technology. Research and development remain crucial in ensuring road safety and efficiency as autonomous vehicles become more common in transportation systems.
Socialization of the Application of Internet of Things (IoT) Technology for Temperature and Humidity Control in Oyster Mushroom Cultivation for Women Farmers Groups at the Takalar Mushroom House Salam, Andi Ejah Umraeni; Subir, Ade Nur Fatimah; Suyuti, Ansar; Manjang, Salama; ., Yusran; Akil, Yusri Syam; Said, Sri Mawar; Kitta, Ikhlas; A, Hasniaty; Arief, Ardiaty; Dewi, Dianti Utami; B, Ian Adrian
JURNAL TEPAT : Teknologi Terapan untuk Pengabdian Masyarakat Vol 8 No 2 (2025): Collaboration for Accelerated Community Achievement
Publisher : Faculty of Engineering UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/jurnal_tepat.v8i2.631

Abstract

This community service activity was conducted by the Department of Electrical Engineering, Hasanuddin University, in collaboration with the Women Farmers Group (Kelompok Wanita Tani/KWT) Rumah Jamur Takalar in Takalar Regency. The program was initiated to address low efficiency and unstable temperature and humidity conditions in oyster mushroom (Pleurotus ostreatus) cultivation houses, which were previously managed manually. The main objective was to improve the knowledge and technical skills of mushroom farmers in understanding and applying Internet of Things (IoT) technology for automatic environmental monitoring and control. The activity was grounded in the concept of IoT-based smart farming, integrating temperature and humidity sensors, a microcontroller, and the Blynk application for real-time environmental supervision. The implementation stages included device design, system socialization and demonstration, and evaluation through pre- and post-activity questionnaires. A total of 15 KWT participants and 4 vocational students were actively involved in the training sessions. The results showed a significant enhancement in participants’ understanding, with the average knowledge score increasing from 2.0 (low awareness) to 4.0 (good understanding), indicating a 100% improvement after training. Participants also demonstrated high enthusiasm during the activities, actively engaging in discussions, operating the Blynk application, and recognizing the advantages of automated systems in maintaining stable temperature and humidity levels in mushroom cultivation houses. The impact of this program was reflected not only in improved knowledge but also in greater awareness and interest among participants in adopting IoT technology for their farming practices. Overall, the activity effectively introduced simple yet relevant technological innovations for small-scale farmers.
Co-Authors ., Yusran Akil, Yusri Syam Andani Achmad Ansar Suyuti Arief, Ardiaty B, Ian Adrian Dewi, Dianti Utami Hasniaty Ikhlas Kitta Indrabayu Indrabayu Muhammad Abdillah Rahmat, Muhammad Abdillah Salama Manjang Sri Mawar Said Subir, Ade Nur Fatimah