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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 40 Documents
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Search results for , issue "Vol 9, No 3 (2025)" : 40 Documents clear
Enhancing Batik Classification Leveraging CNN Models and Transfer Learning Perdana, Am Akbar Mabrur; Fajar B, Muhammad; Mappalotteng, Abdul Muis
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.9.3.2535

Abstract

Batik is a traditional art originating from Indonesia and recognized by UNESCO. Batik motifs vary depending on the region of origin. The diverse batik motifs reflect the rich cultural heritage and unique traditions owned by each region in Indonesia. From Sabang to Merauke, each motif features a different story and values, depicting the beauty and diversity of nature and the lives of diverse local people. However, in the context of the modern era that continues to develop, batik motifs also experience renewal and creativity that always adapts to the times. As a result, the diversity of batik motifs is increasingly abundant in Indonesia. Thus, complicating efforts to identify and categorize batik motifs appropriately. Therefore, in the context of this study, we chose to combine the MobileNetV2 model with Transfer Learning to classify batik motifs. We used a dataset consisting of 3000 batik images and categorized them into three main classes, namely Kawung batik, Mega Mendung batik, and Parang batik. This approach not only leads to the introduction and understanding of traditional batik motifs but also applies the latest technology for a more in-depth and accurate analysis. The results of this model show an extremely high level of testing accuracy, reaching 0.9946%, and training accuracy of 0.8916%, and the time required by the model to train and test the entire dataset is 18 minutes 1 second. Future research can explore the integration of other technologies or new approaches to improve accuracy and efficiency in classifying batik motifs.
Classification of Coral Images Using Support Vector Machine with Gray Level Co-Occurrence Matrix Feature Extraction Nababan, Adi Pandu Rahmat; Haryanto, Toto; Wijaya, Sony Hartono
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.9.3.2708

Abstract

This research developed a coral image classification method using Support Vector Machine (SVM) with Gray Level Co-occurrence Matrix (GLCM) feature extraction to improve the accuracy of coral reef condition monitoring. Coral images were collected in the waters of Sangihe Islands Regency and labelled by experts for healthy, unhealthy, and dead categories. Preprocessing included cropping, background removal, sharpening, and image normalization. GLCM feature extraction was performed with a distance of 1, 2, and 3 pixels and directions of 0°, 45°, 90°, and 135°. SVM uses Linear, Radial Basis Function, and Polynomial kernels with parameters set in a grid. The results indicate that the polynomial kernel with parameters C=10, degree=3, and gamma=1 achieves the highest accuracy, at 91.85%. Oversampling increased the accuracy by 2.17%, while feature selection by boxplot and model-based decreased the accuracy by 0.8% and 0.2%, respectively. On the other hand, feature selection using correlation analysis significantly decreased accuracy by 16.11%. These findings significantly contribute to coral reef conservation by offering a more accurate and efficient classification method. This method enables better and timely monitoring of coral reef conditions, thus supporting more effective conservation interventions. Integrating these research results into IoT systems can improve overall coral reef monitoring and conservation efforts.
Comparative Analysis of Human Detection using Depth Data and RGB Data with Kalman Filter: A Study on Haar and LBP Methods Aulia, Fira; Oktavianto, Hary; Dewantara, Bima Sena Bayu
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.9.3.2739

Abstract

Accurate human detection in video streams with occlusions, illumination variances, and varying distances is crucial for various applications, including surveillance, human-computer interaction, and robotics. This study investigates the performance of two widely used object detection features, Haar-like and Local Binary Pattern (LBP), for detecting human upper bodies in color and depth images. The algorithms are combined with Adaptive Boosting Cascade classifiers to leverage the discriminative power of Haar-like features and LBP texture features. Extensive experiments were conducted on a dataset comprising color images and depth data captured from a Kinect camera to evaluate the algorithms' performance in terms of precision, recall, accuracy, F1-score, and computational efficiency measured in frames per second (fps). The results indicate that when tested on color images, the Haar-Cascade method outperforms LBP-Cascade, achieving higher precision (27.4% vs. 7.8%), recall (49.2% vs. 7.8%), accuracy (21.4% vs. 4.1%), and F1-score (35.2% vs. 7.8%), while maintaining a comparable computational speed (19.07 fps vs. 19.26 fps). However, when applied to depth data, the Haar-Cascade method, coupled with Kalman filtering, demonstrates significantly improved performance, achieving precision (79.3%), recall (79.3%), accuracy (65.8%), and F1-score (79.3%) above 70%, with a computational time of approximately 19.07 fps. The integration of Kalman filtering enhances the robustness and tracking capabilities of the system, making it a promising approach for real-world applications in human detection and monitoring. The findings suggest that depth information provides valuable cues for accurate human detection, enabling the Haar-Cascade algorithm to overcome challenges faced in color image analysis. 
Community Blockchain Record-Keeping Method for Agricultural Land Leases using Design Science Research Approach Loh, Yin Xia; Huspi, Sharin Hazlin; Amerhaider Nuar, Ahmad Najmi
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.9.3.3619

Abstract

In Malaysia, agricultural land lease records are still predominantly maintained on paper, making them vulnerable to loss, damage, and tampering. The study presents a novel, community-based blockchain record-keeping system designed explicitly for agricultural land leasing. Its primary objective is to enhance the transparency, trust, and efficiency of lease transactions between landowners and small-scale farmers. The system leverages Hyperledger Fabric in combination with the Interplanetary File System (IPFS) to ensure lease agreements are stored securely and immutably. By using decentralized storage, the documents remain accessible when needed while reducing the risk of unauthorized modifications. The design of this system is grounded in Work System Theory (WST), which emphasizes the integration of technology with the people, processes, and environmental factors involved in land leasing. To ensure the development approach aligns with the complexities of the real-world context, the study employs Situational Method Engineering (SME). This methodology involves selecting and tailoring components from existing methods to create a solution customized for agricultural land leasing. By combining a robust technical foundation with a design that accounts for community dynamics and legal considerations, the study demonstrates how blockchain can serve not only as a data management tool but also as a means of promoting fairness and transparency in rural land governance. The artefact marks a significant step toward building digital trust in the management and documentation of agricultural land. 
Audio Signal Classification using Mel-Frequency Cepstrum Coefficients and Deep Neural Network for Noon Saakin or Tanween Tajweed Rule Dataset Irawan, Genta Hayindra; Mubarok, Husni; Hoeronis, Irani
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.9.3.2875

Abstract

The Al-Qur’an serves as a fundamental guide for Muslims, requiring both comprehension and practice. Accurate recitation according to tajweed rules is essential for a deeper understanding of its meaning. Despite the growing focus on classification across various modalities, studies specifically targeting audio objects remain relatively limited, motivating further exploration in this area. This study focused on the classification of the tajweed rule as the decided audio object, leveraging the potential of Natural Language Processing (NLP) to support Qur’an research and studies, as well as developing applications that may help learners understand the Qur’an, so further study is needed on the recognition of tajweed reading rules, one of which is the noon saakin or tanween tajweed rule. Audio features were extracted using Mel-Frequency Cepstrum Coefficients (MFCC) technique, which has been widely adopted in various study within the scope of audio processing tasks. These features were subsequently used to train a classification model based on Deep Neural Network (DNN) algorithm. Experiment results demonstrate that the DNN classification model produces an accuracy of 71% and f1 score respectively for iqlab of 0.8, idgham of 0.46, idzhar of 0.77, and ikhfa of 0.72. The results of testing the model with new foreign data, each class tested with one data has successful rate of 50%. These findings indicate that the classification model needs to be further improved in terms of its design or diversity of the audio data, especially model improvements in the recognizing idgham, idzhar, and ikhfa laws.
Assessment of Post-Disaster Building Damage Levels Using Back-Propagation Neural Network Prediction Techniques Wibowo Almais, Agung Teguh; Fajrin, Rahma Annisa; Naba, Agus; Sarosa, Moechammad; Juhari, Juhari; Susilo, Adi
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.9.3.2711

Abstract

Indonesia is susceptible to natural disasters, with its geographical location being one of the contributing factors. To mitigate the harmful effects of natural catastrophes, a disaster emergency response must be undertaken, consisting of steps taken immediately following the event. These operations include rescuing and evacuating victims and property, addressing basic needs, providing protection, and restoring buildings and infrastructure. Accurate data is required for adequate recovery after a disaster. The Badan Penanggulangan Bencana Daerah (BPBD) oversaw disaster relief efforts, but faulty damage assessments slowed restoration. Surveyor subjectivity and differing criteria result in discrepancies between reported damage and reality, generating issues during the post-disaster reconstruction. The objective of this study is to develop a prediction system to measure the extent of damage caused by natural disasters to buildings. The five criteria that decide the level of building damage after a disaster are building conditions, building structure condition, physical condition of severely damaged buildings, building function, and other supporting conditions. The data used are from the BPBD of Malang city from 2019 to 2023. This system would allow surveyors to make speedy and objective evaluations. Five different models were tested using the Neural Network Backpropagation approach. Model A2 produces the highest accuracy of 93.81%. A2 uses a 40-38-36-34 hidden layer pattern, 1000 epochs, and a learning rate 0.1. These findings can lay the groundwork for advanced prediction models in post-disaster building damage evaluation research.
The Development of a Hybrid Learning Management System with Learning Styles for Creative Learning to Undergraduate Students Anugrah, Septriyan; Supendra, Dedi; Jalinus, Nizwardi; Syahril, Syahril; Austin, Diah Anggraini
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.9.3.2670

Abstract

Distance learning often poses challenges for teachers, particularly in understanding the diverse learning styles of their students. The inability to recognize and accommodate these differences can lead to monotonous teaching approaches, which hinder students' comprehension and creativity. This study aims to evaluate the application of learning style concepts developed by Peter Honey and Alan Mumford, implemented in a Learning Management System (LMS) designed by the researcher to support creative learning practices. The research method used is research and development with the Plomp model. This study involved 219 students enrolled in an introductory programming course at Universitas Negeri Padang. The results indicate that respondents' experiences in six aspects—attractiveness, clarity, efficiency, reliability, stimulation, and novelty—received favorable evaluations. This study emphasizes the importance of understanding students' learning styles and using appropriate media to create an environment that supports exploring ideas and innovation, enabling students to develop their creativity optimally while ensuring equal opportunities for all students to grow and succeed.
Ship Trajectory Prediction Based on Spatial-temporal Data Using Long Short-Term Memory Setiawan, Widyadi; Linawati, Linawati; Widyantara, I Made Oka; Wiharta, Dewa Made; Asri, Sri Andriati; Pawana, I Wayan Adi Juliawan
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.9.3.3353

Abstract

The frequent exploitation of shipping lines by passengers increased traffic and exposed it to more significant dangers. Precise predictions for ship trajectory conditions at sea must be available to ensure safe navigation across the oceans. This article presents a trajectory prediction approach based on Long Short-Term Memory (LSTM) neural networks applied to time series Automatic Identification System (AIS) position data, expressed in spatial-temporal form. LSTM is highly suitable for ship trajectory predictions as it can capture long-term dependencies and spatial-temporal patterns existing in AIS data, since LSTM is targeted toward sequential data. The proposed model extracts ship trajectories from AIS data and utilizes an LSTM (Long Short-Term Memory) model to predict future ship movements based on historical patterns. The experiments demonstrate that it is effective in predicting where ships to navigate next, providing a valuable tool for enhancing traffic flow and improving navigation safety. The model with LSTM unit 500, tested on 3,478 ship trajectories, showed a median RMSE prediction error ranging from 0.0720 to 0.0841, with prediction M=8 coordinate a head having the highest error (0.0841) and M=2 and M=9 having the lowest (0.0720); the interquartile range (IQR) spanned from 0.0571 to 0.1006, and M=2 had the most outliers (302) while M=8 had the fewest (171), indicating varying prediction stability across different points. Despite these results, challenges remain in maintaining prediction stability across all points. Further optimization could enhance the model's performance and address these limitations by incorporating more complex spatial-temporal features or hybrid techniques.
Enhancing Low-Resolution Images of Mustard Leaves Affected by Pests with Thermal Sensor using Super-Resolution Convolutional Neural Network Optimization Susanto, Fredy; Nurtantio, Pulung; Soeleman, Arief; Pujiono, Pujiono; Noersasongko, Edi; Dedi, Dedi
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.9.3.2841

Abstract

With urban areas facing limited agricultural land, hydroponic systems offer a solution to increase food storage and variety. Hydroponics, a farming technique that relies on water as a growing medium rather than soil, provides essential nutrients and oxygen for plants. This paper explores the use of thermal sensors to capture images of mustard leaves in a hydroponic system. In addition, it also explores thermal sensor images. These images are analyzed to detect pest attacks, with red leaves indicating the presence of pests and green/blue leaves unaffected by pests. These pests emit hot air; consequently, they turn red. The method of increasing resolution is to compare the Long Short-Term Memory (LSTM) algorithm with the Super-Resolution Convolutional Neural Network (SR-CNN) to improve the quality of images obtained from low-resolution sensors (AMG8833/Grid-EYE). The results show that the SR-CNN method is better than the LSTM (Long Short-Term Memory) method, although limitations remain due to the sensor resolution. After conducting the research, it could be observed that using LSTM resulted in a Mean Square Error (MSE) value of 0.001551685, while SR-CNN indicated an MSE value of 8.873. Furthermore, LSTM produces a Peak Signal-to-Noise Ratio (PSNR) value of 37.10797726, whereas SR-CNN achieves a PSNR of 39.199. The accuracy rates (SSIM) for LSTM and SR-CNN are 0.991538522961364 and 0.997747, respectively. These findings show that using the SR-CNN algorithm can effectively improve the quality of images produced by thermal sensors, even though the sensor pixel capacity is limited.
Optimizing Quadrotor Stability: RBF Neural Network Control with Performance Bound for Center of Gravity Uncertainty Yani, Mohamad; Ardilla, Fernando; Anom Besari, Adnan Rahmat; Saputra, Azhar Aulia; Kubota, Naoyuki; Ismail, Zool H
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.9.3.2918

Abstract

The Radial Basis Function (RBF) neural network has been widely applied for approximating nonlinear systems and improving control robustness, particularly in uncertain conditions such as dynamic shifts in the quadrotor’s Center of Gravity (COG). However, initial weight estimation errors can degrade transient responses, reducing tracking performance. This study proposes a novel RBF-based control scheme integrated with a performance-bound mechanism to enhance quadrotor stability under COG uncertainty. The performance bound ensures that the quadrotor’s motion remains within a defined region around the reference trajectory, thereby minimizing steady-state and transient errors. The RBF network is trained online to estimate the system’s dynamic changes, and the controller is designed using a Lyapunov-like function to ensure stability. Simulation results show that the proposed controller achieves better tracking accuracy and significantly lower energy usage, with total force and moment values reduced compared to the standard RBF controller. Specifically, the proposed controller uses 3010.7 N of force and 2.2427 Nm of moment, while the standard controller requires 3150.2 N and 15.197 Nm. These results confirm that the proposed method provides improved performance and energy efficiency. This research highlights the potential of integrating performance bounds in neural network control for robust quadrotor navigation. Future work includes real-world experiments to validate performance under varying COG perturbations.

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