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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.
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Articles 33 Documents
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Search results for , issue "Vol 9, No 6 (2025)" : 33 Documents clear
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.
Extraction Model for Musical Elements of Javanese Traditional Songs from Gendhing Music Sheets based on Kepatihan Notation Kurniawati, Arik; Arrova Dewi, Deshinta; Satria Erlangga, Bima; Damayanti, Fitri; Oktavia Suzanti, Ika
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.9.6.3573

Abstract

Traditional Javanese gamelan music, particularly its songs, is an integral part of Indonesian culture and identity. However, gamelan music notation remains manual, disorganized, and difficult to access. This poses challenges to balanced education, community sustainability, and digital preservation. This study introduces an automated data extraction and gamelan notation transcription process for transforming Javanese gamelan notation in PDF format into a structured CSV. The innovation process involves parsing PDF-based Kepatihan notation, symbol-to-number conversion, musical section recognition (e.g., buka, lagu, suwuk), and organization in gatra units—each of four melodic notes. The process produces detailed metadata, such as song title, tuning (laras), mode (pathet), and gendhing classification. To evaluate extraction accuracy, the validation period also included a comparison of the converted gatra with the original PDF. The results show that the system achieved 100% accuracy on a sample size of 10 gatra and reduced processing time by 97.5% compared with manual methods. The completed dataset consists of 31 gendhing songs, providing an analyzable and scalable collection for future musicological research and education training. This study contributes to the fields of Music Information Retrieval (MIR) and Digital Humanities by enabling the efficient, standardized digitization of historical music notation. This structured dataset empowers the development of automatic notation generators, making inclusive learning tools accessible to novices and facilitating the documentation of cultural heritage through technology.
Attention Mechanism with Kalman Smoothing Improved Long Short-Term Memory Mechanism for Obesity Weight Forecasting Pranolo, Andri; Utami, Nurul Putrie; Anasyua, Fairuz Khairunnisa
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.9.6.4633

Abstract

This study aims to evaluate and compare the performance of several variants of Long Short-Term Memory (LSTM) based models in predicting obesity weight data. The main contribution of this research was to perform an extensive assessment of the effectiveness of LSTM-based models, including the combination of Attention-LSTM with Kalman Smoothing (KS), using two different data normalization methods (Z-score and Min-Max). This research used a publicly available dataset on obesity levels based on eating habits and physical condition, available at the UCI Machine Learning Repository. The models evaluated include the standard LSTM, Attention-LSTM, KS-LSTM, and the proposed KS-Attention-LSTM. The evaluation is conducted using the Root Mean Square Error (RMSE), the Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results showed that the proposed KS-Attention-LSTM model with Min-Max normalization achieved the lowest MAPE (0.28372) and the highest R² (0.79527) among the models. This suggests that the proposed model offers advantages in terms of prediction accuracy and has a good ability to handle data variations. Therefore, the KS-Attention-LSTM model with Min-Max normalization is strongly recommended for practical implementation, particularly for time-series data prediction in the health sector. This research is beneficial and contributes an effective alternative model that improves prediction accuracy, supports decision-making in the health sector, and enriches forecasting methods. 

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