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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : -
Articles 1,172 Documents
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Sample An Improved Lite-Yolov4 Object Detection Model for Mobile Augmented Reality Mansoor Nafea, Mohammed; Siok Yeeb, Tan; Tareq, Mustafa; Ahmed Jubair, Mohammed; Fatikhan Ataalla, Abdalrahman
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.9.6.3341

Abstract

Augmented reality (AR) enhances user experiences by overlaying digital information on real-world objects or places. Augmented reality makes unprecedentedly immersive experiences possible in marketing, industry, education, entertainment, fashion, and healthcare. While current augmented reality methods can identify 3D items in their environment, the recognition of tiny, complex objects remains a problem for most object detection methods. In addition, object detection is a key in computer vision and AR systems. The Object detection process aims to classify and localize objects in applications like face detection, text detection, and people counting. Many natural features detection models were proposed, like YOLO, YOLO-LITE, and YOLOv4-tiny. However, the detection of objects from natural images remains a challenging task, often compromising accuracy or requiring longer processing times. To overcome these challenges, this article suggests a novel method that combines the strengths of YOLO-LITE and YOLOv4-tiny into a hybrid model. The suggested model name is LITE-YOLOv4, which stands for “LITE-You Only Look Once Version 4. The model design depends on YOLO-LITE as a backbone. LITE-YOLOv4 uses a feature pyramid network to extract feature maps of various sizes. It also utilizes a "shallow and narrow" convolution layer to optimize its object detection capability. The proposed model aims to achieve a speed and accuracy balance, making it suitable for use in AR apps on portable devices and PCs without GPUs. LITE-YOLOv4 achieved a mean average precision (mAP) of 52.6% on the PASCAL VOC dataset and 33.3% on the COCO dataset. The suggested model achieved a respectable speed, which is 20 frames per second (FPS). LITE-YOLOv4 provides better accuracy and reasonable computational time than state-of-the-art non-GPU models.
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.
Improved Cognitive Distortion Detection using IndoBERT and Important Words Approach for Bahasa Indonesia Suputra, I Putu Gede Hendra; Linawati, Linawati; Sukadarmika, Gede; Putra Sastra, Nyoman; Ari Wilani, Ni Made; Agus Setiawan, I Made
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.9.6.3576

Abstract

Irrational or deviant thinking is a cognitive condition characterized by distortion in perception and reasoning. Such cognitive distortions are often reflected in an individual's speech and writing. Detecting distorted thinking at an early stage is crucial, as it can help mitigate the risk of severe depression. Cognitive Behavior Therapy (CBT) is one of the most widely studied approaches in psychotherapy research for depression. It has been recognized as an effective method for addressing cognitive distortions, depression, and negative thought patterns. Recent advancements in online CBT, particularly those incorporating Natural Language Processing (NLP) techniques, have significantly improved the diagnosis and treatment of cognitive distortions. Numerous studies have explored detecting and classifying cognitive distortions using machine learning models. Cognitive distortion detection is a form of short-text classification that presents a notable challenge – the limited availability of features that effectively capture a text's meaning or intent. Despite these challenges, BERT remains a consistently effective model across various text classification tasks. This study proposes a novel model for detecting cognitive distortions by introducing a new approach that combines sentence-level features from IndoBERT with keyword features derived from the class-based TF-IDF framework. The integration of these two feature sets demonstrated promising results, achieving an average accuracy of 0.787 and an F1 score of 0.769. These values represent improvements of 3.39% and 3.45%, respectively, compared to the IndoBERT-based detection model only. These findings highlight the potential of the proposed model as a valuable early detection tool to support online CBT programs.
An Efficient Unknown Detection Approach for RFID Data Stream Management System Yaacob, Siti Salwani; Mahdin, Hairulnizam; Wijayanto, Inung; Aamir, Muhammad; Jaya, M. Izham; Mohd Radzuan, Nabilah Filzah; Mubarak-Ali, Al-Fahim
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.9.6.2824

Abstract

The presence of unknown RFID tags can occur when new, unread tagged objects are introduced into the system, either accidentally or intentionally. Additionally, unknown tags can result from tag duplication errors, where multiple tags have the same identifier, or tag malfunctions, where a tag fails to transmit its identifier correctly. This research addresses the critical issue of detecting unknown tags, focusing on optimizing processing time and energy efficiency in terms of memory usage when detecting these tags. A novel algorithm called SWOR (Sliding Window XOR-based Detection) is introduced, specifically designed to identify unknown tags within RFID data streams. SWOR utilizes a sliding window mechanism combined with an XOR filter, enabling efficient detection of unknown tags while reducing unnecessary processing, which can lead to prolonged processing times, high memory consumption, and scalability issues. Experimental results demonstrate that SWOR decreases execution time by an average of 27% across various tests, outperforming existing approaches in terms of processing time for RFID event streams. The materials and methods employed include comprehensive simulations and real-world RFID data streams to validate the algorithm's effectiveness. This study highlights the potential for significant improvements in RFID system efficiency and paves the way for future research in optimizing RFID tag detection methodologies. The implications for further research include exploring the integration of SWOR with other RFID system components and examining its performance in diverse operational environments. This research contributes to the development of more robust and efficient RFID systems, thereby enhancing their reliability and scalability for various future applications.
Optimization of Shape, Texture, and Color Extraction Methods in Concrete Strength Detection Ramadhanu, Agung; Hendri, Hallifia; Majid, Mazlina Abdul; Enggari, Sofika; Andini, Silfia; Hidayat, Rahmad
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.9.6.4164

Abstract

The growing demand for an accurate and rapid method to assess concrete strength has driven the development of non-destructive and cost-effective techniques. This paper aims to enhance the process of extracting shape, texture, and color features from concrete surface images to improve the accuracy of strength classification through digital image processing and artificial intelligence (AI). The study uses a dataset of 300 high-resolution photographs of concrete samples, categorized by their compressive strength levels: weak, moderate, and strong. These images were taken under controlled background and lighting conditions to ensure consistency. The methodology involves three stages: image preprocessing, feature extraction, and classification. During preprocessing, RGB images are converted to the Lab color space, and a three-layer median filter is applied to reduce noise. The K-Means clustering algorithm segments the images, and relevant features such as Metric, Eccentricity, Contrast, Correlation, Energy, Homogeneity, Hue, and Saturation are extracted. Among these, Correlation and Energy are the most influential in classification accuracy. The experimental results show that the proposed approach can reach up to 90 percent accuracy in classifying concrete strength into three categories. This suggests that visual features have strong potential to replace traditional destructive testing methods. The findings also point to the possibility of enhancing prediction accuracy with deep learning models and developing real-time, field-based evaluation tools to aid quality control in the construction industry.
Information Technology Risk Management Analysis Using Cobit and ISO at Jumputan Industry Sutabri, Tata; Pratama, Yoga; Herdiansyah, M. Izman; Cholil, Widya
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.9.6.3236

Abstract

The Jumputan industry, a cultural heritage and creative sector in Palembang, utilizes data technology to streamline and improve business operations, thereby enhancing its competitiveness in transactions. This involves the use of computer devices and third-party applications accessible to the public. The integration of IT/IS in the Jumputan industry is often hindered by various challenges, such as connectivity issues, system malfunctions, outdated support, resource errors, insufficiently skilled personnel, shifts in technology policies by third-party providers, changes in government regulations, and other limiting factors. This study examined the risks associated with IT/IS implementation in the Jumputan industry, using the COBIT 2019 and ISO 31000 frameworks to identify risks, create risk profiles, and assess data technology management within the culture-based creative sector. This study aimed to identify and anticipate the risks associated with IT/IS implementation. A qualitative descriptive approach was employed, by data analyzed through the COBIT 2019 and ISO 31000 frameworks. The findings revealed that the Jumputan industry must adopt data technology risk management, as over 10 potential risk sources could negatively impact its business processes. Ultimately, this risk profile is intended to help the industry proactively address potential challenges and unforeseen risks. Additionally, it is hoped that this study will serve as a valuable resource for future research focused on the role of data technology in the creative industry.
Enhancement Infrared, Visible, Manganic Resonance Imaging and Computed Tomography: A Comparative Study Jasim Alhamdane, Haider; Nickray, Mohsen; Ali Salah, Hussein
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.9.6.3155

Abstract

Images are merged to produce a single image with increased image quality and the integrity of key characteristics while combining complementing multi-temporal, multi-view, and multi-sensor information. The goal of the study is to enhance the focus of three wavelet transform methods, namely image fusion based on discrete wavelet transform, stationary wavelet transform, and dual tree-complex wavelet transform, in order to improve the quality of medical images such as computed tomography images, magnetic resonance images, and the quality of merging visible images and infrared images using the technique of image fusion based on wavelet transform. The fuzzy histogram equalization method, the Lucy-Richardson algorithm, the recovery of the pictures prior to the fusion process, and the convolutional filters based on linear spatial filters were all utilized for the optimization process. Seven scales were employed in the study to evaluate the performance effectiveness of the suggested strategies and to contrast them with the conventional approaches. The results showed that, when compared to the other methods, image fusion based dual tree-complex wavelet transform and spatial filters produced the best results. This paper discusses numerous state-of-the-art image fusion techniques at various levels, along with their benefits and drawbacks, as well as various spatial and transform-based techniques with quality measures and their applications in many fields. This review has finished with a number of future directions for various image fusion applications.
Attention-Enhanced Convolutional Neural Network for Context Extraction in Andersen's Fairy Tales Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Nafalski, Andrew
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.9.6.4056

Abstract

Event extraction in classic literature and fairy tales remains highly challenging due to their non-linear plot structures, archaic linguistic expressions, and intricate character interactions, while advances in modern NLP still show limitations in capturing subtle narrative cues in historical texts. This study aims to address these gaps by developing an event extraction model tailored to the narrative characteristics of Hans Christian Andersen’s fairy tales. We propose a BERT-enhanced Context-aware Convolutional Neural Network (CNN) that integrates an attention mechanism to overcome the limited contextual range of traditional CNNs. The model leverages BERT’s contextual embeddings enriched with an attention layer to detect event triggers, character relations, and narrative transitions across nonlinear storylines. A hybrid dataset was constructed through system-generated annotations refined via manual verification and combined with AN/an cartoon-based representations for model training and final testing. Experimental results show that the proposed model surpasses both the CNN-only baseline and a rule-based approach, achieving precision of 0.92, recall of 0.89, F1-score of 0.90, and accuracy of 0.91, outperforming the CNN baseline (0.85/0.82/0.83/0.84) and rule-based system (0.78/0.75/0.76/0.77). These findings highlight the effectiveness of context-aware representations for processing literary narratives and demonstrate interdisciplinary relevance to digital humanities and AI-based storytelling, with future extensions envisioned for multilingual settings and genre-specific adaptations.
Integrated Paddy Pest Detection System Using Hybrid Model and Edge Computing with LoRa Communication and GIS Interface Lazuardi, Mochamad Riswandha; Hadi, Mochammad Zen Samsono; Kristalina, Prima; Uehara, Hideyuki
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.9.6.3529

Abstract

There is an emerging requirement for early detection of pests in the field concerning agricultural yield and quality improvement. Traditional methods often result in a loss of the desired outcome due to delayed intervention and increased crop losses. This work focuses on establishing an integrated pest detection system using a hybrid model that combines MobileNet and Faster R-CNN, optimized for real-time performance at the edge. Additionally, LoRa-based data transmission was employed, along with a GIS interface for monitoring. The system is further tested with the diverse dataset of 4,736 images representing common rice pests. It included lightweight feature extraction with precision object detection, as it produced the lowest loss among other models tested. Further implementation is made on a Raspberry Pi, which shows optimal performance in detecting at a distance of 15 cm and with 100 lux of lighting. LoRa communication was adopted for effective data transmission with low power consumption and extensive coverage, while the GIS interface enabled real-time monitoring of pests in space and time. Field tests demonstrated that this system achieved very high accuracy, rapid response, and was applicable in the field for pest control, offering the potential to increase yields and improve farmer welfare. Further research could focus on adapting the system to a wide range of environmental conditions and scaling it up for more extensive agricultural use. The integral approach forms necessary steps toward smart farming. However, it also provides a scalable, low-cost solution for early pest detection.
Prediction Intervals for Extreme Rainfall in Indonesia using Monotone Composite Quantile Regression Neural Networks Saputri, Prilyandari Dina; Azwarini, Rahmania; Adipradana, Dimaz Wisnu
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.9.6.3186

Abstract

Rainfall data may contain nonlinear, complex, and extreme characteristics. Weather monitoring can be performed by predicting rainfall as the cause of flooding and providing early warnings to ensure smooth evacuation. Classical methods, such as ARIMA, are unable to capture rainfall data patterns. A standard method for forecasting complex datasets is the use of neural networks. The neural network method failed to produce a prediction interval due to the limitation of the standard error calculation. The use of the Monotone Composite Quantile Regression Neural Network (MCQRNN) enables the accommodation of complex patterns and the production of interval predictions through its quantiles. The crossing problems in the quantile estimation were also resolved. In this study, we utilized four rainfall datasets from different locations: Central Java, West Java, South Sumatra, and North Sumatra. The lower and upper bounds were compiled from 2.5% and 97.5%, respectively. The point forecasts are constructed from the 50% quantile. Furthermore, the point forecast and interval prediction were compared to the standard classical forecasting method, i.e., ARIMA. The results demonstrated that the MCQRNN model outperforms the ARIMA model in terms of point forecasting. As the forecasting period is extended, the interval prediction of MCQRNN tends to become more consistent, whereas the width prediction of the ARIMA model becomes broader. Hence, the MCQRNN interval predictions are also suitable for long-term forecasting. Further research was required to evaluate the performance of prediction intervals.

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