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All Journal JOIV : International Journal on Informatics Visualization Adopsi Teknologi dan Sistem Informasi
Muhammad Abdillah Rahmat, Muhammad Abdillah
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Computer Science & IT

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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.
Penerapan Model BERT pada Chatbot dalam Platform E-Commerce Rahmat, Muhammad Abdillah; Karmila; Khatami, Husain Muhammad; Muhammad Alief Fahdal Imran Oemar
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 4 No. 1 (2025): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v4i1.3039

Abstract

This study develops chatshop, a BERT-based e-commerce chatbot designed to assist customers in searching for pizza menus. The BERT model was chosen because of its ability to understand sentence context bidirectionally, thereby increasing the accuracy in detecting user intent. This chatshop allows users to find menus, get recommendations, and access price and stock availability information in real time. The evaluation was carried out using BLEU and ROUGE-L metrics, with a ROUGE-L F1 score of 32.91%. These results indicate that the chatbot is able to handle simple interactions well, but still needs improvement in answering more complex questions accurately and completely.
A Cascading of YOLOv8 and Random Forest Regression in Oil Palm Fresh Fruit Bunch Mass Estimation System using Unmanned Aerial Vehicle Imagery Indrabayu, -; Nurhadi, Muhammad Ijlal; Tandungan, Sofyan; Rahmat, Muhammad Abdillah
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3137

Abstract

Efficient management of oil palm farms requires accurate pre-harvest planning to maximize productivity. Traditional methods for estimating the mass of Fresh Fruit Bunches (FFBs) typically involve manual sampling and weighing, which are time-consuming and prone to errors. This study presents a novel system combining unmanned aerial vehicle (UAV) photography with geometric feature extraction using YOLOv8-Segmentation and machine learning models—Random Forest Regression (RFR)—to estimate FFB mass. The system addresses challenges posed by dynamic drone imagery, including environmental variations and frond occlusions. Instead of directly integrating YOLOv8 with the regression models, geometric features such as the minor axis, perimeter, and eccentricity are extracted from the segmented images and used to train the RFR for mass estimation. The top-performing model, using features extracted from YOLOv8-Small-Segmentation with the minor axis and eccentricity, achieved a Root Mean Square Error (RMSE) of 3.95 and a Mean Absolute Error (MAE) of 2.87 for frond-covered FFBs. For frond-uncovered FFBs, the best-performing features were the minor axis, perimeter, and area extracted using YOLOv8-Large-Segmentation, resulting in an RMSE of 3.91 and MAE of 2.91. These results demonstrate the system's capability to accurately estimate FFB mass based on UAV-captured imagery and feature extraction. This approach offers a scalable and efficient solution for pre-harvest planning in oil palm plantations, addressing the limitations of traditional methods while improving operational efficiency and accuracy in yield estimation.
Co-Authors Andani Achmad Indrabayu Indrabayu Indrabayu, - Karmila Khatami, Husain Muhammad Muhammad Alief Fahdal Imran Oemar Nurhadi, Muhammad Ijlal Salam, Andi Ejah Umraeni Tandungan, Sofyan