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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization Altasia : Jurnal Pariwisata Indonesia
Rangkuti, Abdul Haris
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Enhancing Vision-Based Vehicle Detection and Counting Systems with the Darknet Algorithm and CNN Model Rangkuti, Abdul Haris; Athala, Varyl Hasbi
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2550

Abstract

This study focuses on developing an algorithm that accurately calculates the volume of vehicles passing through a busy crossroads in Indonesia using object recognition. The high density of vehicles and their proximity often pose a challenge when distinguishing between vehicle types using a camera. Therefore, the proposed algorithm is designed to assign a unique identity (ID) to each vehicle and other objects, such as pedestrians, ensuring that volume calculations are not repeated. The objective is to provide an equitable comparison of road density and the total number of detected vehicles, enabling the determination of whether the road is crowded. To accomplish this, the algorithm incorporates the Non-Max Suppression function, which displays bounding boxes around objects with confidence values and counts the objects within each box. Even when objects are nearby, the algorithm tracks them effectively, thanks to the support of the Darknet Algorithm. The main capabilities of this algorithm for improving vehicle detection include enhanced accuracy, speed, and generalization ability. Typically, it is used in conjunction with the You Only Look Once (YOLO) object detection framework. Five convolutional neural network models are tested to assess the algorithm's accuracy: YOLOv3, YOLOv4, CrResNext50, DenseNet201-YOLOv4, and YOLOv7-tiny. The training process utilizes the Darknet Algorithm. The best-performing models, YOLOv3 and YOLOv4, achieve exceptional accuracy and F1 scores of up to 99%. They are followed by CrResNext50 and DenseNet201-YOLOv4, which achieve accuracy rates of 92% and 98% and F1 scores of 94% and 98%, respectively. The YOLOv7-tiny model achieves an accuracy rate and F1 score of 86% and 88%, respectively. Overall, the results demonstrate the algorithm's success in accurately detecting and calculating the volume of vehicles and other objects in a busy intersection. This makes it a valuable tool for regional government decision-making.
Identification of Indonesian Traditional Foods Using Machine Learning and Supported by Segmentation Methods Rangkuti, Abdul Haris; Kerta, Johan Muliadi; Mogot, Roderik Yohanes; Athala, Varyl Hasbi
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.8.4.2545

Abstract

Traditional food is essential in preserving cultural heritage and is a vital part of Indonesian cuisine. In this research, we implement a methodology to identify the traditional Indonesian food using machine learning algorithms supported by various segmentation methods. This research aims to provide an efficient and accurate approach to classifying traditional foods, which can contribute to promoting and preserving Indonesia's culinary heritage. To conduct this research, we conducted experiments on 34 types of conventional Indonesian food originating from various provinces in Indonesia. The analysis of food images involved several segmentation algorithms, including Sobel, Prewitt, Robert, Scharr, and Canny filters. After the segmentation process, we proceeded with feature extraction and classification using traditional machine learning algorithms such as the Random Forest algorithm, Decision Tree, and derivatives of the SVM algorithm. These algorithms aimed to recognize the 34 types of traditional food. After conducting several experiments, we found that Random Forest with Robert's segmentation method was the highest-performance algorithm. It produced extraordinarily accurate results on the test dataset, with an accuracy performance of 85.52%, recall of 84.63%, precision of 83.77%, and an f1 score of 82.49%. Additionally, the best-performing algorithms with execution time averaged less than 1 minute. Another experimental result showed that the Random Forest algorithm with the Canny operator achieved an accuracy of 81.51%, recall of 84.97%, precision of 86.8%, and an f1 score of 85.61% on the test dataset. Furthermore, the Random Forest algorithm with the Sobel operator achieved accuracy results of 78.4%, recall of 65.3%, precision of 62.3%, and an f1 score of 63.71%.  In the SVM algorithms derivative, the Sigmoid SVM combined with the Scharr operator achieved the highest performance in its category across all classification metrics. In conclusion, this research offers valuable insights into classifying traditional Indonesian dishes using traditional machine learning algorithms. Simultaneously, this research aims to promote the appropriate and effective preservation and recognition of traditional Indonesian food.
Automated Detection of Molting Crabs Using YOLO: Enhancing Efficiency in Soft-Shell Crab Aquaculture Saputra, Dany Eka; Rangkuti, Abdul Haris; Dwi Putra, Sulistyo Emantoko; Daru Kusuma, Purba; Kurniawan, Albert; Gabriela, Melanie
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3468

Abstract

Crab molting detection is a crucial process in aquaculture, particularly to produce soft-shell crabs, which are considered a delicacy in many markets. Traditional methods of manually monitoring crabs for molting are labor-intensive and susceptible to human error. To address this challenge, this study examines the application of the YOLO (You Only Look Once) object detection model for automating the detection of molting crabs. YOLO is renowned for its capability to perform real-time object detection, making it an ideal choice for this application. Our research focuses on developing a YOLO-based system that accurately identifies molting crabs from videos or images captured in farming environments. The model was trained on a comprehensive dataset comprising images of crabs at various stages of molting, ensuring robustness against environmental variations and different lighting conditions commonly encountered in aquaculture settings. The results indicate that the YOLO model achieves high accuracy in detecting molting crabs, significantly enhancing the efficiency and reliability of the detection process compared to manual observation and other machine learning approaches. These advancements facilitate timely intervention and harvesting, which are critical for optimizing the quality and yield of soft-shell crabs. In our experiments, the recognition of the crab molting process was categorized into three classes: the molting crab, the crab skin, and the newly molted crab. Overall, the YOLOv8 and YOLOv11 models demonstrated impressive performance, achieving an average accuracy of 96% to 98%. This research on molting crab detection has proven successful and can be further extended to include other types of crabs.
Improving Accuracy in Deep Learning-Based Mushroom Image Classification through Optimal Use of Classification Techniques Kerta, Johan Muliadi; Rangkuti, Abdul Haris; Lun Lau, Sian; Kurniawan, Albert; Gabriela, Melanie; Tandianto, Alicia
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2820

Abstract

The primary purpose of this research is to address the existing knowledge gap surrounding various lesser-known types of edible mushrooms. A common understanding exists that mushrooms are edible and possess numerous health benefits. This research is intended to advance that understanding by deploying AI technology and deep learning models specifically designed to recognize and identify various fungi. During this research, we have developed a unique derivative of deep learning. This involved testing several Convolutional Neural Network (CNN) models aimed at automatically identifying and detecting different types of mushrooms and understanding the benefits associated with each type. The research methodology was divided into several stages: Collection of mushroom images, Preprocessing of images, Feature extraction, and Classification. The preprocessing involved adjustments such as scale, image rotation, and setting the brightness range. The goal of selecting and training the CNN model was to enhance the classification accuracy of mushroom images within each class. The data was divided into training, testing, and validation sets for the experimental stage. The purpose was to process image data from test and validation images based on the training images that have been processed. We evaluated the classification layer to be shorter, but it demonstrated excellent accuracy in assessing similarity performance. Based on several experiments conducted using different CNN models, DenseNet, MobileNetV2, and InceptionResNetV2 models achieved an accuracy of more than 90%, specifically 95%, 94%, and 92%, respectively. The most accurately recognized mushroom types include Snow, Dried Shitake, King Oyster, Straw, Button, and Truffle; some CNN models could identify these up to 100%. Overall, the models and algorithms used in this research successfully facilitated the identification and detection of various types of fungi. They were fast and displayed high accuracy performance. Hopefully, this research can be extended to process images of even more diverse types of mushrooms, particularly in terms of shape, color, and texture characteristics. This will enhance the depth and breadth of knowledge in this field and further advance our understanding of the beneficial properties of various mushrooms.
Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods Rangkuti, Abdul Haris; Hasbi Athala, Varyl; Haridhi Indallah, Farrel; Tanuar, Evawaty; Muliadi Kerta, Johan
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1359

Abstract

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.
Optimization of Vehicle Object Detection Based on UAV Dataset: CNN Model and Darknet Algorithm Rangkuti, Abdul Haris; Athala, Varyl Hasbi
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.2.1159

Abstract

This study was conducted to identify several types of vehicles taken using drone technology or Unmanned Aerial Vehicles (UAV). The introduction of vehicles from above an altitude of more than 300-400 meters that pass the highway above ground level becomes a problem that needs optimum investigation so that there are no errors in determining the type of vehicle. This study was conducted at mining sites to identify the class of vehicles that pass through the highway and how many types of vehicles pass through the road for vehicle recognition using a deep learning algorithm using several CNN models such as Yolo V4, Yolo V3, Densenet 201, CsResNext –Panet 50 and supported by the Darknet algorithm to support the training process. In this study, several experiments were carried out with other CNN models, but with peripherals and hardware devices, only 4 CNN models resulted in optimal accuracy. Based on the experimental results, the CSResNext-Panet 50 model has the highest accuracy and can detect 100% of the captured UAV video data, including the number of detected vehicle volumes, then Densenet and Yolo V4, which can detect up to 98% - 99%. This research needs to continue to be developed by knowing all classes affordable by UAV technology but must be supported by hardware and peripheral technology to support the training process.
Co-Authors Albert Kurniawan, Albert Athala, Varyl Hasbi Dwi Putra, Sulistyo Emantoko Gabriela, Melanie Haridhi Indallah, Farrel Hasbi Athala, Varyl Jajat sudrajat Johan Muliadi Kerta, Johan Muliadi Lun Lau, Sian Mogot, Roderik Yohanes Morika, Doni Muliadi Kerta, Johan Purba Daru Kusuma Saputra, Dany Eka Sijoatmodjo, Felix Octavio Tandianto, Alicia Tanuar, Evawaty Tjokrowerdojo, Adrianto Wibowo Wijanto, Natalia Marijani Yapson, Febriant