cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmat Hidayat
Contact Email
mr.rahmat@gmail.com
Phone
-
Journal Mail Official
rahmat@pnp.ac.id
Editorial Address
-
Location
Kota padang,
Sumatera barat
INDONESIA
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 50 Documents
 1   2   3   4   5 
Search results for , issue "Vol 9, No 5 (2025)" : 50 Documents clear
Preserving Indigenous Indonesian Batik Motif Using Machine Learning and Information Fusion Sumari, Arwin Datumaya Wahyudi; Aziza, Nadia Layra; Hani'ah, Mamluatul
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.9.5.3714

Abstract

Preserving Indonesia’s indigenous cultural heritage in the form of Batik with various motifs to maintain the nation’s continuity from generation to generation. Hundreds of Batik motifs are spread across multiple regions of Indonesia, along with their unique names and meanings, where each motif has a cultural and historical meaning behind it. The distinctive patterns of Batik motifs challenge the community to remember and distinguish them, so it is crucial to have an intelligent system. This study designed and implemented a Batik motif classification system based on machine learning’s Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. The primary key to classifier performance is features. An assessment was carried out on the performance of two feature models: single features and fused features. The Gray Level Co-occurrence Matrix (GLCM) produces the texture features of the Batik motif, and the Moment Invariant (MI) is used to create the shape features of Batik motifs. The Union Fusion and XOR operators produce a single fused feature of the two features. The proposed combination of techniques, namely SVM and GLCM, outperforms the combination scenario of Multi Texton Histogram (MTH), Multi Texton Co-Occurrence Descriptor (MTCD), Multi Texton Co-occurrence Histogram (MTCH) with SVM, and the combination of GLCM with 1-NN as well as the combination techniques that employed information fusion. The experiment results showed that the proposed combination technique achieved an accuracy of 97%. It can be concluded that SVM (RBF) with GLCM yields the best Batik motif recognition system.
Brain Tumor Classification based on Convolutional Neural Networks with an Ensemble Learning Approach through Soft Voting Puspita, Kartika; Ernawan, Ferda; Alkhalifi, Yuris; Kasim, Shahreen; Erianda, Aldo
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.9.5.4609

Abstract

The brain is a vital organ that serves various purposes in the human body. Processing sensory data, generating muscle movements, and performing complex cognitive tasks have all historically relied heavily on the brain. One of the most common conditions affecting the brain is the growth of abnormal tissue in brain cells, leading to the development of brain tumors. The most common forms of brain tumors are pituitary, glioma, and meningioma, which are major global health issues. From these issues, there is a need for appropriate and prompt handling before the brain tumor disease becomes more severe. Quick handling is through an early disease detection approach, and computer vision is one of the trending early disease detection methods that can predict diseases using images. This research proposes a model in computer vision, namely the Convolutional Neural Network (CNN), with a soft voting ensemble learning strategy to classify brain tumors. The dataset consists of 7,023 images without tumors and MRI brain tumors such as glioma, meningioma, and pituitary with a resolution of 512x512 pixels. This experiment investigates classifier models such as VGG16, MobileNet, ResNet50, and DenseNet121, each of which has been optimized to maximize performance. The proposed soft voting ensemble strategy outperformed existing methods, with an accuracy of 97.67% and a Cohen's Kappa value of 0.9688. The proposed soft voting ensemble method approach has proven effective in improving the accuracy.
A Hybrid Approach for Malicious URL Detection Using Ensemble Models and Adaptive Synthetic Sampling Sujon, Khaled Mahmud; Hassan, Rohayanti; Zainodin, Muhammad Edzuan; Salamat, Mohamad Aizi; Kasim, Shahreen; Alanda, Alde
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.9.5.4627

Abstract

Malicious URLs pose a significant cybersecurity threat, often leading to phishing attacks, malware infections, and data breaches. Early detection of these URLs is crucial for preventing security vulnerabilities and mitigating potential losses. In this paper, we propose a novel approach for malicious URL detection by combining ensemble learning methods with ADASYN-based oversampling to address the class imbalance typically found in malicious URL datasets. We evaluated three popular machine learning classifiers, including XGBoost, Random Forest, and Decision Tree, and incorporated ADASYN (Adaptive Synthetic Sampling) to handle the class-imbalanced nature of our selected dataset. Our detailed experiments demonstrate that the application of ADASYN can significantly increase the performance of the predictive model across all metrics. For instance, XGBoost saw a 2.2% improvement in accuracy, Random Forest achieved a 1.0% improvement in recall, and Decision Tree displayed a 3.0% improvement in F1-score. The Decision Tree model, in particular, showed the most substantial improvements, particularly in recall and F1-score, indicating better detection of malicious URLs. Finally, our findings in this research highlighted the potential of ensemble learning, enhanced by ADASYN, for improving malicious URL detection and demonstrated its applicability in real-world cybersecurity applications.
Yolo-Drone: Detection Paddy Crop Infected Using Object Detection Algorithm Yolo and Drone Image Masykur, Fauzan; Prasetyo, Angga; Zulkarnain, Ismail Abdurrozaq; Kumalasari, Ellisia; Utomo, Pradityo
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.9.5.3472

Abstract

Crop failure is an undesirable result of rice planting for every farmer because it disrupts the economic stability of the family. One of the factors of crop failure in the rice planting process is the disease attack factor, which causes infection. Infected plants will interfere with the growth of rice, not optimally, because the green leaf substance, which is key to processing sunlight's nutrients, is unable to function. After all, it is covered by infection. Infection in the leaves covers the green leaf substance, or chlorophyll, so that the leaves are unable to absorb nutrients from sunlight. This problem is a separate concern in overcoming rice plant infections, which will result in crop failure. This paper discusses the detection of infected rice plants, particularly leaf infections, using drone camera images. Unmanned aircraft, also known as drones, fly above rice fields to capture images of rice plants, which are then used as datasets in training models to detect infected and healthy rice plants. The detection of disease presence in rice leaves is carried out using the You Only Look Once version 8 (YOLOv8) object detection algorithm, with a model trained using Google Colab Pro+. The results of training the model to detect healthy and infected plant leaves are the primary objectives of this study. The YOLOv8 object detection model, when applied to detect rice plants with two classes (healthy and infected), shows quite good results. This is indicated by the recall, precision, and F1-score values (0.99, 0.814, 0.90) approaching 1 in all classes.
Efficient Broker-Driven Request Packet Size Sekhi, Ihab; Nehéz, Károly
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.9.5.3131

Abstract

Efficient virtual machine (VM) allocation is fundamental in cloud computing to optimize resource utilization and ensure high performance. Traditional methods often fail to account for the variability in request packet sizes, resulting in inefficiencies and performance bottlenecks. This study introduces a novel broker-driven VM allocation approach integrated with fuzzy logic to optimize resource distribution and address these limitations dynamically. The proposed methodology employs a broker system for real-time monitoring and analysis of request packet sizes, leveraging fuzzy logic to adjust VM allocations based on fluctuating workload demands dynamically. Validation of the approach was conducted using real-world data from the Google Cloud Platform's Europe West3 region and t2d-standard machine types. Simulations executed with the Cloud Analyst tool across five scenarios demonstrated the method's efficacy compared to traditional techniques. The results from the third scenario were used as a representative example. Its findings include a 67.62% reduction in response time, a 26.64% decrease in data center processing time, a 26.65% improvement in request serving time, and a 70.65% reduction in total data transfer costs. The results of the other scenarios demonstrated comparable levels of improvement. The study highlights the effectiveness of a broker-driven, fuzzy logic-enhanced system in modern cloud computing, highlighting its adaptability and scalability. Future research should include incorporating energy consumption and fault tolerance parameters, applying the method to hybrid and multi-cloud environments, and integrating machine learning techniques.
Ad-hoc Networks and Cloud Databases for Renewable Energy Systems Saad Ahmed, Omar; Shuker Mahmoud, Mahmoud; Waleed Khalid, Rafal; Mohammed Khaleel, Basma; Waleed, Ghufran
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.9.5.4628

Abstract

The growing heterogeneity and decentralization in renewable energy infrastructures have resulted in a need for adaptive, scalable, and intelligent communication and data management solutions. On the one hand, centralized systems have limitations in terms of latency, scalability, and fault resilience, whereas purely decentralized systems can encounter challenges with data integration and long-term analytics. In this article, a hybrid architecture using mobile ad hoc networks and cloud databases to improve the collaborative operation of distributed renewable energy systems is introduced. The architecture leverages latency-aware routing protocols for hard real-time communication among edge devices, solar panels, wind turbines, and battery storage. At the same time, it uses cloud-based predictive analytics to enable more powerful capabilities, such as failure diagnostics and power scheduling. In extensive simulations, we demonstrated improvements of several orders of magnitude across key operational metrics, including latency reduction, throughput gains, energy efficiency, and scaling. Furthermore, introduced machine learning applications, a BiLSTM-CNN hybrid for fault prediction, and a reinforcement learning agent for energy dispatch, improving system flexibility and the ability to make informed decisions. The results demonstrate the potential of hybrid communication and analytics systems to enable next-generation smart grid applications by improving reliability, responsiveness, and resource allocation. This study adds to the existing knowledge base on intelligent energy by providing a design that can be easily replicated and scaled, while accounting for operational and long-term sustainability performance.
Drones and IoT for Enhancing Renewable Energy Integration Hameed, Maan; Abdulkareem Hameed, Nada; Natiq Abdulwahab, Imad; Hashim Qasim, Nameer; S. Alani, Saad
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.9.5.4630

Abstract

Ranging from monitoring in real-time to predictive maintenance and operational optimization, the increasing complexity of renewable energy systems requires sophisticated solutions. The article proposes a holistic solution that combines drones and IoT to improve installation efficiency and reduce incidents in wind, solar, and hydropower energy production. The study uses a hybrid approach that combines sensor analytics, drone-assisted infrastructure inspection, edge computing for latency minimization, and multivariate modeling to quantify the system's enhancement. Field trials involved three renewable power plants over the course of six months and included the acquisition of more than 10,000 data related to power plant operations. It was shown that integrating a thermal, an RGB, and a LiDAR sensor on a drone resulted in a significant increase in inspection efficiency, fault coverage, and spatial resolution. At the same time, deployed IoT sensors continuously monitor inverter temperature, vibration frequency, and energy output. Statistical regression models revealed highly significant relationships among the frequency of UAV inspection, IoT latency, and energy efficiency, and algorithmic modules, such as support vector machines, Kalman filters, and ant colony optimization, further improved fault diagnosis, data fusion, and pathfinding. The results validate the applicability of drones and IoT for enhancing system uptime, dependability, and predictability without introducing extra operational load. This work lays out a scalable, modular approach, feasible for deployment in smart grid scenarios, which enables sustainable, intelligent energy management.
Semantic Multi-Query Model for Cultural Computing of Image Search System Barakbah, Ali Ridho; Suryani, Indah Yudi; Kusumaningtyas, Entin Martiana
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.9.5.4294

Abstract

The proliferation of digital images on the internet has increased the need for image search systems, especially for culturally significant images that contain a collection of impressions. However, traditional image search systems typically rely on a single query, making it difficult to discern user intent accurately. This paper introduces a novel model for describing user impressions using a semantic multi-query function for cultural computing in image search systems.  This model provides a culture-centric semantic multi-image query system to generate representative query impressions.  The proposed multi-query model provides an analytical tool to semantically construct representative query color attributes, involving four stages: (1) Local normalization of 3D-Color Vector Quantization, (2) Color distribution measurement, (3) Adaptive representative color adjustment, and (4) Representative color identification. For the experimental study, we evaluate our system with two types of experiments: (1) Multi-query image for image search to ensure that our multi-query model enhances the accuracy of the retrieval outcomes, and (2) Multi-query image for semantic image search of cultural paintings. In the first experiment using the SIMLIcity dataset, our proposed multi-query model achieved better retrieval performance across most categories, reducing the single-query error from 26.67% to 20%. In the second experiment using the Indonesian cultural painting dataset, our proposed multi-query model achieved better retrieval performance across most categories, improving the single-query average similarity from 46.6% to 72%.
Gamification Project-Based E-learning in Character Education: A Study in Senior High School Merliana, Ni Putu Eka; Widyantara, I Made Oka; Wirastuti, Ni Made Ary Esta Dewi; Saputra, Komang Oka; Setyohadi, Djoko Budiyanto
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.9.5.4033

Abstract

E-learning in education faces challenges in improving students' engagement, specifically regarding character education effectiveness. Gamification is among the strategies that can be applied to increase student engagement in the project-based learning process. Therefore, this study aimed to develop a Gamification Framework for Project-Based E-learning in Character Education (GaPolCE) as an innovative solution to improve engagement and character education at the senior high school level. A quantitative study was carried out using a quasi-experimental method, where data collection was carried out through a pre-post test and log data analysis to measure the effectiveness of gamification in achieving character education and student engagement. The results showed that the implementation of GaPolCE improved aspects of character education measured using the N-Gain score, where moral knowledge was in the high category (0.70) while moral feeling (0.49) and moral action (0.51) were in the moderate category. Student engagement increased significantly by 67%, 8%, and 25% for behavioral, emotional, and cognitive engagement. However, the effectiveness of in-depth character formation requires long-term evaluation. In addition, the assessment of the application of gamification in project learning for character education is still done manually, thus increasing teachers' workload. In this regard, further research is needed with a longitudinal approach to ensure the sustainability of its influence. In addition, it is necessary to develop an automatic assessment system based on artificial intelligence to increase the efficiency of character education evaluation. 
Assessing the ReCODE (Reading, Connecting, Observing, Discussing, and Evaluating) Instructional Model with ICT Assistance: Its Effects on Collaborative Skills and Academic Resilience of Students Saenab, Sitti; Yunus, Sitti Rahma; Saleh, Andi Rahmat; Wulandari, -; Muhiddin, Nurhayani H.
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.9.5.3277

Abstract

Education in the 21st century requires students to have collaborative skills. Academic resilience is essential for facing various challenges in education. However, several studies have shown that students' collaborative skills and academic resilience are still low. This study aims to determine the effect of the ICT-assisted ReCODE instructional model on students' collaborative skills and academic resilience. This study is a quasi-experimental study with a posttest-only non-equivalent Control Group design. The population in this study consisted of all students in class VIII at SMPN 18 Makassar. The sample in this study was selected using a purposive sampling technique consisting of an experimental class and a control class. The data obtained were analyzed using descriptive and inferential statistics, including an independent t-test with a significance level of 0.05. The results of the inferential statistical analysis of collaborative skills obtained tcount = 1.75 > ttable = 1.67, which means H0 is rejected and H1 is accepted. The inferential statistical analysis of academic resilience yielded tcount = 2.04 > ttable = 1.67, indicating that H0 is rejected and H1 is accepted. Based on this analysis, it can be concluded that the ICT-assisted ReCODE instructional model affects the collaborative skills and academic resilience of class VIII students at SMPN 18 Makassar in the primary material on the human digestive system. The implications of this study suggest the need for further research on the broader application of the ICT-assisted ReCODE learning model to enhance students' collaborative skills and academic resilience.

Page 5 of 5 | Total Record : 50

 1   2   3   4   5 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 9, No 6 (2025) Vol 9, No 5 (2025) Vol 9, No 4 (2025) Vol 9, No 3 (2025) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow Vol 8, No 4 (2024) Vol 8, No 3 (2024) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien Vol 7, No 4 (2023) Vol 7, No 3 (2023) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2-2 (2022): A New Frontier in Informatics Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation Vol 6, No 4 (2022) Vol 6, No 3 (2022) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 4 (2021) Vol 5, No 3 (2021) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 4 (2020) Vol 4, No 3 (2020) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2-2 (2019): Internet of Things and Smart Environments Vol 3, No 4 (2019) Vol 3, No 3 (2019) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 4-2 (2018): Cyber Security and Information Assurance Vol 2, No 3-2 (2018): The Diversity in Information Systems Vol 2, No 4 (2018) Vol 2, No 3 (2018) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 4-2 (2017): The Advancement of System and Applications Vol 1, No 4 (2017) Vol 1, No 3 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue