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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 48 Documents
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Search results for , issue "Vol 9, No 2 (2025)" : 48 Documents clear
Ontology Modeling for Subak Knowledge Management System Hariyanti, Ni Kadek Dessy; Linawati, Linawati; Oka Widyantara, I Made; Sukadarmika, Gede; Arya Astawa, I Nyoman Gede; Kamarudin, Nur Diyana
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3386

Abstract

Subak, as a Balinese traditional agricultural organization, has knowledge of cultural heritage, including both explicit and tacit elements. This research aimed to develop ontology knowledge model for the digital preservation of Subak culture in the form of Knowledge Management System (KMS). The development of model was based on three main stages, including requirement analysis, ontology development, and ontology assessments. Requirement analysis included data collection through field observations, in-depth interviews, and document analysis, while ontology development consisted of hierarchical classes, object and data properties, as well as individual entities. Furthermore, ontology assessments were the stage of evaluating and testing the resulting ontology. Protégé software was used to apply ontology model, generating Ontograph visualizations and producing Ontology Web Language (OWL). Validation was carried out using both Ontology Quality Analysis (OntoQA) and expert comments. The evaluation results showed a Relationship Richness (RR) value of 0.8, an Inheritance Richness (IR) value of 0.78, and an Attribute Richness (AR) value of 3.89, showing that ontology captured a comprehensive and representative body of knowledge. Expert comments stated that ontology model created was worthy of being used to represent Subak knowledge as a form of cultural preservation. The developed Subak ontology could serve as a foundational knowledge base for further research in related fields such as agricultural management, social organization, and cultural preservation.
Combination of Multidistance Signal Level Difference and Time Domain Features for Epileptic Seizure Classification Amalia, Qoriina Dwi; Beu, Donny Setiawan; Rizal, Achmad; Ziani, Said
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2692

Abstract

Epileptic seizures are neurological disorders characterized by abnormal electrical activity in the brain, causing a series of seizures or episodes of temporary loss of consciousness. This research aims to develop a method of detecting and classifying epileptic seizures using one-dimensional EEG signals with the Multidistance Signal Level Difference (MSLD) approach and time domain feature extraction. The goal is to improve accuracy in distinguishing normal, interictal, and ictal conditions in EEG signals. The dataset from Bonn University consists of one-dimensional EEG signals that include normal, interictal, and ictal states. The analysis method includes extracting time domain features from EEG signals, such as Integrated EMG (IEMG), Mean Absolute Value (MAV), and others. The next step is the application of three classification algorithms, namely linear SVM, quadratic SVM, and cubic SVM, to classify the three conditions. Testing is done by measuring the accuracy of the classification results. The results of this study show that by using 14-time domain features and the MSLD approach, the most significant classification accuracy achieved was 98.7%. This result demonstrates the effectiveness of the proposed method in distinguishing normal, interictal, and ictal conditions. This research provides a foundation for further study in developing EEG signal classification analysis models. Future research can expand the scope by considering larger datasets, using more sophisticated feature extraction techniques, and exploring more complex classification algorithms to improve the accuracy and reliability of the model in real-world applications, particularly in the medical field for the diagnosis of epileptic seizures.
Recommendation System for Mobile-Based Oil Palm Fertilization Period with Rainfall Prediction using ANN Isnaini, Mei Nanda; Sari, Juni; Kusuma Wardhani, Kartina Diah; Tri Wahyuni, Retno
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2883

Abstract

Weather conditions significantly affect human activities, including the oil palm plantation sector, which in practice considers weather and climate conditions. Oil palm is an annual crop that requires proper nutrition throughout the year. Plant nutrition through fertilization must be according to the specific needs of oil palms. Knowing the type of fertilizer, calculating the dosage, and evaluating the climatic characteristics significantly affect the effectiveness and efficiency of fertilization. According to one palm oil farmer, fertilization should ideally be done when the soil is moist or not during the dry season so plants can absorb fertilizers properly. If fertilization is ineffective, then the operational costs of plant maintenance to buy fertilizers become less efficient. Due to climate change, farmers often find it difficult to determine the optimal timing of fertilization. Therefore, rainfall prediction is essential. Thus, fertilization can run well and get maximum results. The recommendation system in this research includes a rainfall prediction system with machine learning methods and an Artificial Neural Network. The recommendation system is a mobile-based application that allows oil palm farmers to obtain information on the appropriate time to fertilize based on rainfall. The evaluation of rainfall prediction using ANN has the MSE value of 0.0019981 and the MAPE value of 9.355%. It can be concluded that the rainfall prediction model is working optimally. This system can be combined with harvesting forecasting and recommendations of oil palm plantation periods to become a monitoring system for oil palm productivity.
Performance Improvement of Cosine Similarity Algorithm with Bidirectional Encoder Representations from Transformers on Abstract Document Similarity Detection Pradana, Musthofa Galih; Irzavika, Nindy; Maulana, Nurhuda; Mu, Jesselyn; Wari, Valtrizt Khalifah
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2853

Abstract

In thesis courses or final projects, students are required to be able to conduct research by the science they are engaged in, find innovations, solve problems, and foster a culture and critical mindset. However, the issue that is often encountered is plagiarism. Plagiarism is taking a work that can be in the form of someone else's opinion and making it seem as if it is your own. The step in applying technology that can be done is to carry out early detection of the similarity of documents written by students. In this case, the document that will be detected is an abstract that must be collected by students when submitting a thesis title. The algorithm used is a cosine similarity algorithm, which is computationally efficient because of its ease of interpretation and compatibility with large-scale data. This research was carried out using two schematic approaches: bidirectional encoder representations from transformers (BERT) and not bidirectional encoder representations from transformers (BERT). The corpus data used in this study was 1450 data of student thesis abstract documents, with the test using 10 data to see the performance of the cosine similarity algorithm in detecting the similarity of abstract documents. The results showed that documents with optimization using the Bidirectional Encoder Representations from Transformers (BERT) approach had better results, with an average performance improvement of 23.48%.
Intelligent Monitoring System Framework for Peatland Management in IoT-Integrated Precision Agriculture Marleny, Finki Dona; Novriansyah, Irvan; Maulida, Ihdalhubbi; Ansari, Rudy; Mambang, Mambang; Saubari, Nahdi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2955

Abstract

Peatlands have excellent air retention capabilities and are crucial for environmental health. They act as natural sponges, absorbing and releasing air, which helps maintain soil moisture levels vital for crops. However, peatlands are highly sensitive ecosystems often threatened by unsustainable agricultural practices. When managed sustainably, peatlands scattered across the globe can be utilized for various farming activities. Managing peatlands for food crops presents an alternative to agriculture in peatland areas, enhancing economic growth in rural regions. This research aims to introduce a framework that integrates IoT into the intelligent monitoring of peatland management for precision agriculture. The primary challenge is implementing effective monitoring and management strategies for sensitive peatlands within precision agriculture. The main principle of precision agriculture is data-driven decision-making, supported by modern agricultural management that employs technology and data analysis to optimize farming practices. The proposed system framework can be utilized to identify the best types of food crops for making new decisions while ensuring high yields at the agricultural level. Precision agriculture principles are then applied to enhance the accuracy of monitoring peatland management, focusing on suitable land potential and food crops planted in areas with the highest potential. The results indicate that prioritizing peatlands for food crops reduces inappropriate decisions in selecting food crops. Furthermore, the efficiency of agricultural management can be improved with lower management costs. This framework provides a practical and user-friendly basis for informing all stakeholders on automating Peatland agriculture for food crops using precision agriculture systems integrated with IoT. Management practices that apply information technology aim to optimize crop inputs based on temporal and spatial variability. The cost-effectiveness from this perspective creates transition opportunities for communities, positioning our framework as a solution for designing Peatland management with intelligent monitoring.
Detection of Keratitis in the Cornea by Developing an Active Contour Method Based on Contrast Features Negoro, Wahyu Saptha; Sumijan, Sumijan; Bukhori, Saiful
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3356

Abstract

Digital Image Processing (DIP) is a scientific discipline that uses computer image processing techniques. The object of this research is keratitis on the cornea. The image of keratitis is obtained using a slit lamp at Padang Aye Center (PAC) Hospital, based on the results of the diagnosis, namely by looking at the development of the infiltrate or also called hypopyon, measuring the ulcer borders horizontally and vertically to evaluate improvement or response to the treatment given. The clinical results cannot determine the extent and circumference of the keratitis layer area that responds to treatment in the corneal area. The images used were 206 slit lamp images of keratitis. This research provides knowledge in the form of contrast values in the Active Contour method, resulting in an update called Active Contour Contrast Adjustment (ACCA) in correctly segmenting keratitis objects and providing measurements of the area and perimeter of the keratitis area. Overall. The research results from 206 slit lamp images, 195 slit lamp images of keratitis could detect keratitis correctly, and eleven slit lamp images of keratitis could not be detected, resulting in an accuracy of 94.66%. Meanwhile, the standard Active Contour accuracy was not detected at all or 100% undetected. Based on 11 images not detected using the (ACCA) method from 206 images, an accuracy of 5.33% was obtained. So, the results obtained are outstanding and can be used as a reference for medical personnel.
Bike Fitting System Based on Digital Image Processing on Road Bike Nasution, Tigor Hamonangan; Sitohang, Andreas; Seniman, Seniman; Soeharwinto, Soeharwinto
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2796

Abstract

This research aims to develop a bike fitting system based on digital image processing for road bikes. The method used in this study involves using the OpenCV and MediaPipe libraries in the Python programming language to detect the rider's body pose from a video stream captured using a webcam. The body pose data is then used to calculate important angles such as elbow, hip, knee, and ankle range related to the correct riding position for road bikes. In this research, a comparison is made between the body angles obtained and the angle range considered ideal for bike fitting on road bikes. If the body angles fall within the desired range, the system will label it as "Fit”; if the body angles are outside the selected range, the system will label it as "Not Fit." The results of this study indicate that the bike fitting system based on digital image processing using a webcam can provide helpful visual feedback in improving the rider's body position for road bikes. By observing the body angles produced and seeing the "Fit" or "Not Fit" label, cyclists can adjust their position to match the ideal position in bike fitting. The system test results show a low error rate, with elbow angle having an average error of 0.81%, hip angle of 1.37%, knee angle of 0.83%, and ankle range of 1.76%. Thus, this research contributes significantly to supporting cyclists in achieving a position appropriate to their inseam height.
Identifying Fraud Sellers in E-Commerce Platform Anand, Lovesh; Goh, Hui-Ngo; Ting, Choo-Yee; Quek, Albert
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3479

Abstract

The identification of fake reviews in e-commerce is crucial as they might impact the purchasing decisions and overall satisfaction of buyers. This work investigates the effectiveness of machine learning and transformer-based models for detecting fake reviews on the Amazon Fake Review Labelled Dataset. The dataset contains 20,000 computer-generated and 20,000 original reviews across various product categories with no missing values. In this study, machine learning and transformer-based models were compared, revealing that transformer-based models outperformed in terms of accuracy in detecting fake reviews, achieving an accuracy of 98% with the DistilBERT model. Additionally, this work too examines the impact of word embedding on machine learning models in enhancing fake review detection accuracy. The results show that the word embedding model Word2Vec displays notable improvements, achieving accuracies of 92% with SVM and 90% with Random Forest and Logistic Regression. Furthermore, a comparison study being carried out on comparing transformer models from previous work, which utilized the same full dataset, it was found that the DistilBERT model produced comparable accuracy despite its lighter architecture. In summary, this study underscores the effectiveness of transformer-based models and machine learning models in detecting fake reviews while at the same time highlighting the importance of word embedding techniques in enhancing the performance of machine learning models. With this work, it is hope that it would contribute to combating fake reviews and fostering trust in e-commerce platforms.
Integration of MQTT and Augmented Reality using Dobot Magician Lite Robot Rante, Hestiasari; Hanifati, Kirana; Arief, Muhammad Fauzan; Sukaridhoto, Sritrusta; Zainuddin, M. Agus; Mezouar, Youcef; Hill, David R. C.
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3198

Abstract

As robotics education evolves, there is a growing demand for effective tools that offer hands-on learning experiences while addressing the traditional challenges such as limited interactivity, high costs, and the need for sophisticated technical knowledge. This paper explores the integration of wireless controllers and Augmented Reality (AR) image tracking with the Dobot Magician Lite robotic platform to enhance robotics learning. The proposed solution utilizes MQTT (Message Queuing Telemetry Transport), a lightweight messaging protocol, to enable intuitive and user-friendly wireless control of the robot. By using AR image tracking, students may see and operate virtual overlays that are aligned with the actual robot, bridging the gap between theoretical understanding and practical application. This technique not only streamlines the control procedure, but it also creates an interesting and immersive learning environment, making robotics education more accessible to a larger range of people. Team Alpha (n=4) and Team Beta (n=4) conducted testing in Indonesia and found promising results: average response time was 160 ms and 165 ms, respectively; movement accuracy was 1.5 mm and 1.7 mm; AR display quality received scores of 8.8 and 8.6; and user satisfaction ratings were 9.0 and 8.9. Both teams reported great system adaptability and minimal issue frequency, with Team Beta mentioning minor performance difficulties including occasional latency.  These findings highlight the effectiveness and reliability of the proposed system, supporting its potential for broader application in robotics education.
Multi-Head Voting based on Kernel Filtering for Fine-grained Visual Classification Khairunnisa, Mutiarahmi; Wibowo, Suryo Adhi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2920

Abstract

Research on Fine-Grained Visual Classification (FGVC) faces a significant challenge in distinguishing objects with subtle differences within intra-class variations and inter-class similarities, which are critical for accurate classification. To address this complexity, many advanced methods have been proposed using feature coding, part-based components for modification, and attention-based efforts to facilitate different classification phases. Vision Transformers (ViT) has recently emerged as a promising competitor compared to other complex methods in FGVC applications for image recognition, which are mainly capable of capturing more fine-grained details and subtle inter-class differences with higher accuracy. While these advances have shown improvements in various tasks, existing methods still suffer from inconsistent learning performance across heads and layers in the multi-head self-attention (MHSA) mechanisms that result in suboptimal classification task performance. To enhance the performance of ViT, we propose an innovative approach that modifies the convolutional kernel.  Our method considerably improves the method's capacity to identify and highlight specific crucial characteristics required for classification by using an array of kernels. Experimental results show kernel sharpening outperforms other state-of-the-art approaches in improving accuracy across numerous datasets, including Oxford-IIIT Pet, CUB-200-2011, and Stanford Dogs. Our findings show that the suggested approach improves the method's overall performance in classification tasks by achieving more concentration and precision in recognizing discriminative areas inside pictures. Using kernel adjustments to improve Vision Transformers' ability to differentiate somewhat complicated visual features, our strategy offers a strong response to the problem of fine-grained categorization.

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