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 51 Documents
 1   2   3   4   5 
Search results for , issue "Vol 9, No 4 (2025)" : 51 Documents clear
Improved Content-based Image Retrieval by Improving Low-Level Features Detection with Artificial Neural Networks Ahmed, Asraa Safaa; Ibraheem, Ibraheem Nadher
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3311

Abstract

With the rapidly growing number of digital photos being taken using different devices in recent years, significant attention has been brought to improving the ability to match these images. However, the reliance on traditional Content-Based Image Retrieval (CBIR) techniques on certain features, e.g., objects, low-level features, or colors, in these images has caused a semantic gap in the matching results. Recent techniques rely on multiple features to reduce this gap and employ artificial neural networks (ANNs) to produce a single similarity measure that represents the overall similarity between two images. Additionally, several studies have suggested that these networks better detect low-level features when processing the input image in grayscale rather than separate color channels. In this study, we propose a new methodology that allows ANNs to process colored and grayscale versions of images simultaneously, producing a more accurate similarity measure by accurately considering the high-level, low-level, and colors in the input images. The model implementation is based on the Yolo V8 neural network architecture. It is evaluated against recent state-of-the-art methods using several datasets, including MIRFLICKR-25K, NUS-WIDE, MS-COCO, Pascal VOC2007, and Pascal VOC2012. We assess the model's performance using three well-established metrics: NDCG, ACG, and wMAP. The proposed technique outperformed all existing methods in terms of NDCG and wMAP. Experimental results demonstrate that this method has also achieved high-performance measures with significant improvements and more stable results at different datasets of different images and classes, especially when the quality of the results is measured using the NDCG. Such an improvement illustrates the importance of using the grayscale version of the image as an input to the neural network to improve its ability to recognize local features better than only providing the image in RGB.
Optimizing Private Museum Management and Exploration Routes through Sustainable Legendary Folklore Using Machine Learning, Statistics, and Particle Swarm Optimization: A Case Study in Bandung Municipality Safriana, Luki; Nurhayati, Nurhayati; Widiyani, Widiyani; Suharjito, Didik
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3184

Abstract

Bandung municipality, home to more than 2.5 million residents, ranks as Indonesia's third most populous city and functions as the administrative center of West Java Province. The city is a notable tourist destination and has experienced significant development, highlighted by important events such as the Smart City Declaration in 2015 and the inaugural Asian African Conference in 1955. Among its 21 museums, seven are under private management and play a crucial role in preserving cultural heritage and boosting urban tourism. This study aimed to improve the management and operational efficiency of these seven private museums in Bandung. Machine learning techniques, statistical methods, and the particle swarm optimization (PSO) algorithm were utilized to improve visitor experience, lower operational costs through decarbonization, and promote environmentally sustainable practices. The research employed K-means clustering, scatter plots, and multidimensional scaling (MDS) to categorize data and pinpoint the most effective museum exploration routes. At the same time, the Orange software package facilitated the machine learning application in this study. These techniques not only contribute to the preservation of Bandung's cultural folklore, such as the stories of Sangkuriang and Lutung Kasarung, but also create a framework for urban tourism management. The findings enhance the discussion on the integration of technology in heritage and tourism by providing valuable insights for improving museum operations, reducing costs, decarbonizing, and safeguarding cultural assets. The findings carry important implications for both national and international contexts and foster the sustainable development of cultural tourism. 
Implementation and Empirical Analysis of ACCA Model Optimized by Learning Management System to Enhance the Students’ Creative Thinking Skills Muharni, Andi; Mahanal, Susriyati; Zubaidah, Siti; Susanto, Hendra
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3417

Abstract

The ability to think creatively is one of the essential competencies that must be developed in the education process in the modern era.Creative thinking skills can be stimulated through the application of the right learning model. One of the learning models that can help educators stimulate creative thinking skills is the ACCA learning model. The purpose of this research is to analyze and describe the effect of ACCA learning model on improving creative thinking skills. This study used an experimental research design with pre-test-post-three-treatment conducted at one of the State Universities in Makassar, Indonesia. The implementation of ACCA learning model assisted by the Learning Management System (LMS) to optimize the use of technology and improve creative thinking skills. The research instrument used in this study was an integrated essay test of creative thinking skills that had gone through a validity and reliability process. Data collection was based on the results of students' answers at the time of the pre-test and post-test. The statistical analysis used in this study was Ancova test with a significance level of 0.05. The results of the data analysis showed that the ACCA learning model assisted by LMS can improve creative thinking skills. The flexibility offered by this model provides opportunities for students to express themselves and be active in learning and solving problems faced both independently and in groups. Based on these findings, this research can be used as one of the innovative models in empowering creative thinking skills and other skills of students.
An Optimized Hybrid Deep Learning Approach for Accurate Fruit Image Classification H. Razzaq, Hasanain; H. Al-Rammahi, Laith F. M.; Almousawy, Asraa Mounaf; Zulqarnain, Muhammad
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3732

Abstract

The field of fruit classification in computer and machine vision is growing rapidly. However, numerous deep learning approaches have been introduced for image classification, but they often encounter challenges that must be addressed. The effectiveness of the classification system relies on several factors and the selection of relevant features. In this paper, we propose an innovative hybrid deep learning framework integrating Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU) to classify fruit images accurately. The extraction and selection of optimal features are the key to achieving high classification accuracy. To achieve this, we leverage the power of CNN, RNN, and GRU, which are jointly employed in the automatic fruit classification process. The CNN was applied to extract spatial features from images. Then, an RNN was utilized to identify the most discriminative features, and finally, GRU performed final classification using the refined feature set from both CNN and RNN. Moreover, hyperparameters of the proposed model are optimized using TLBO-MCET. Empirical evaluations highlight the proposed method’s superiority over traditional methods such as Support Vector Machine (SVM), Feed-forward Neural Network (FFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for fruit classification tasks. The accuracy rate of the proposed technique surpasses that of SVM, FFNN, ANFIS, and CNN-LSTM. The results of the experiment showed that the developed model achieved an accuracy of 97.35%, F1-score of 94.91%, and Coefficient of Correlation (CoC) of 96.50 and RMSE of 11.50 respectively. Furthermore, the proposed approach contains relatively high computational momentum that will be further enhanced in the future.
Cloud Computing-based Shrimp Pond Water Quality Prediction Intelligent Service System Suasono, Zaikhul Sulthon; Setiawardhana, Setiawardhana; Winarno, Idris; Gunawan, Agus Indra
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.2862

Abstract

Maintaining water quality is an essential factor in the success of shrimp farming, particularly in conventional and semi-intensive methods in Indonesian. Poor water quality will affect shrimp's survival, reproduction, development, and harvest yield. In order to furnish data regarding future water quality conditions, This research aims to create an intelligent cloud-based water quality prediction system for shrimp ponds that can provide accurate predictions regarding future water quality conditions. The system utilizes the WQI dataset gathered from four different shrimp farming sites, totaling 408 samples, each location exhibiting a different set of values. The model will be trained using four parameters: pH, DO, salinity, and temperature. The WQI dataset will be pre-processed to address missing data, outliers, and standardization. The water quality prediction model uses three machine learning algorithms: SVM, ANN, and MLR. The model's performance results are evaluated using MAE, RMSE, and R². The results indicate that the ANN model is the most effective, achieving an MAE: 0.4023, RMSE: 0.5336, and R²: 0.7178 for temperature predictions, and an MAE: 0.4080, RMSE: 0.5942, and R²: 0.5997 for salinity. The SVM model had mixed results for temperature, with an MAE: 0.3645 and RMSE: 0.4823, but it performed poorly for DO, as evidenced by a negative R² of -0.2428. The MLR model provided reasonable temperature predictions MAE: 0.4953, RMSE: 0.6370, R²: 0.5602. Subsequent research endeavors should prioritize the augmentation of the dataset size and the incorporation of temporal dimensions in order to enhance the precision of predictive outcomes.
Development Extraction of Regional Features of Pleural Cavity Objects in Pneumothorax Lung X-ray Images by Dilation and Erosion Morphology Marfalino, Hari; Defit, Sarjon; Nurcahyo, Gunadi Widi
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3387

Abstract

Image processing is a solution in the development of chest X-ray technology, starting from the image segmentation process as a preprocessing stage to separate the image object from the original background. Spontaneous pneumothorax (SP) is a type of air collection in the pleural cavity that develops without trauma. The diagnosis of pneumothorax has a sensitivity of approximately 25 to 75% using an anteroposterior chest x-ray, which still provides a dubious picture of pneumothorax. However, the development of the Region Feature algorithm with a new algorithm, namely RM Multy, has improved the accuracy. The RM Multy algorithm can calculate the area of the object, allowing it to produce the area of infiltration in the right lung, left lung, and the lung as a whole. The Region Feature results of the Pneumothorax obtained with the detected image area as many as 19 areas, for the pixel size of each area are 145, 355, 110, 31, 31, 52, 30, 36, 54, 122, 58, 23, 476, 77, 192, 24, 168, 263, 41 and 44. So the total pixels for 19 areas is 2301. The area converted to mm2 is 2301 x 0.04 mm2 = 92.04 mm2. Classification results on lungs with Pneumothorax and Normal by detection process with RM Multy using the CNN algorithm with an accuracy of 96.43%. This accuracy confirms the success of the system, which has been processed using a new algorithm. Therefore, further development is needed to improve detection accuracy in pneumothorax cases with smaller area sizes.
Database Optimization for Sustainable Energy Analytic Alshareefi, Hamid; H. Habib, Bassam; Jabbar Obaid, Ezzeddin; Kassim Ahmad, Hussain; Alsaidi, Jamal
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.4381

Abstract

In recent years, the burgeoning amount, speed, and variety of data in multiple energy fields have generated new requirements that far exceed those of traditional database systems and especially pose new challenges related to sustainable energy analytics. Classical relational database architectures are usually not capable of satisfying the needs of performance, scalability, and energy efficiency of emerging energy platforms for smart grids, renewable energy forecasting, and real-time monitoring. The study proposes a high-level database optimization framework targeted at improving the performance of large-scale energy analytics infrastructures. Specifically, the system combines the Adaptive Query Optimization (AQO) and Partition-Aware Load Balancing (PALB) to address the issues of query delay, storage limit, and energy consumption. AQO dynamically optimizes execution plans by considering workload statistics and planner feedback, while PALB balances query distribution in concurrent scenarios based on system resource metrics. We demonstrate via experiments on a high-performance computing platform that our DWPRF achieves significant improvements in various aspects, such as query execution time reduction, advanced compression to realize higher storage efficiency, reduced energy consumption, and better scalability of the system under high concurrency. The experimental results indicate the feasibility of scheduling and allocating plans when addressing these challenges and show the power of using intelligent planning with resource-aware execution to optimize databases for energy informatics. Moreover, the study outlines potential avenues for future extensions, including machine learning-driven optimizers and distributed deployment at the edge and cloud. The proposed approach provides a solid basis for constructing high-performance, energy-efficient data management systems, which are a fundamental requirement for sustainable energy systems.
Using IT2FS, DEMATEL, and TOPSIS to Build Sustainable Solutions for Vietnamese Coffee Nguyen, Hoang Phuong; Bui, Thuy Chi; Nguyen, Mai Hoang Dieu; Trang Bui, Dang Thien; Nguyen, Thi Quynh Nhu; Nguyen, Hoang Truc Khanh; Le, Thanh Tam; Nguyen, Thi Lieu; Bui, Viet Duc
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.4358

Abstract

Ensuring the sustainability of coffee supply chains in emerging economies is a growing concern due to complex systemic barriers and limited strategic direction. The Vietnamese coffee industry is ranked second in global coffee exports and plays a vital role in Vietnam's economy. The Vietnamese coffee supply chain encompasses a vast network of smallholder farmers, local processors, and exporters, presenting challenges related to sustainability, price volatility, and quality control. This study proposes the use of Interval Type-2 Fuzzy Sets (IT2FS), Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to analyze and rank the barriers and strategic interventions in Vietnam's coffee sector. Using IT2FS-DEMATEL, we looked at the driving and dependent relationships between six main barriers and six solutions that focus on sustainability. The results showed that the DEMATEL-based structural analysis revealed that Unstable Market and Trade Conditions had the most substantial driving influence. At the same time, the Lack of Best Cultivation Quality Standards was the most dependent factor. The TOPSIS analysis ranked Establishing National Coffee Cultivation Standards as the top solution, which was remarkably close to the optimal solution vector. These results provide a thorough, evidence-based plan for determining the initial actions to take in stabilizing Vietnam's coffee supply chain during times of volatility. It gives policymakers and industry stakeholders a clear framework for developing targeted actions to enhance the sustainability and resilience of coffee supply chains.
UAV-Based Segmentation and Correlation Analysis of Vegetation Indices for Cassava Crop Health Assessment Maryana, Sufiatul; Herdiyeni, Yeni; Wahjuni, Sri; Santosa, Edi
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3078

Abstract

Cassava, an essential staple food with diverse applications, has been relatively underexplored in terms of health analysis using vegetation indices. Conventional field surveys face challenges in covering large areas due to resource constraints. Recent advancements in remote monitoring techniques, such as satellite imagery and Unmanned Aerial Vehicles (UAVs), offer a promising alternative. While satellite imagery enables broad-scale surveys, its limited spatial resolution restricts detailed analyses of individual plants or smaller ecosystems. UAV-based vegetation surveys commonly utilize Vegetation Indices (VI) to assess unique spectral information. This study investigated UAV-based methods for mapping cassava distribution in the Telaga Kahuripan smallholder plantation in Bogor, Indonesia, focusing on UAV imagery, segmentation, and vegetation indices to evaluate cassava plant health at 2, 5, and 8 months of age. The results revealed significant variations in vegetation indices across different cassava plant ages. Particularly, the highest values observed at 5 months of age indicated substantial growth, with NDVI and GNDVI values exhibiting R2 ranging from 0.95 to 0.98, indicating a strong correlation. The robust correlation between NDVI and GNDVI implies that both indices can effectively predict plant health using UAV-based monitoring. Comparisons with existing studies suggest potential variations attributable to factors such as geographical location, environmental conditions, and cultivation practices. Understanding these variations is crucial for refining monitoring techniques and informing agricultural practices. Consequently, the findings have implications for enhancing cassava health monitoring and optimizing agricultural practices to ensure sustainable crop production.
Multivariate Time Series Forecasting using Hybrid Vector Autoregressive and Neural Network for Coupled Roll-Sway-Yaw Motions Prediction Suhermi, Novri; Suhartono, -; Rahayu, Santi Puteri; Ali, Baharuddin; Dahlila, Dea; Aisy, Rahida Rihhadatul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3077

Abstract

There are six types of motion referred to as the six degrees of freedom, which define the motion of a ship. For a ship to remain stable, it must be in a symmetrical position. Therefore, a ship's stability can be determined based on its motion. Ship motions can be analyzed either in an uncoupled system or a coupled system. One of the coupled motion systems that is often studied is the roll-sway-yaw motion. In this study, we apply the Hybrid Vector Autoregressive–Neural Network (VAR-NN) model to build a multivariate time series model for predicting the roll-sway-yaw motions of a prototype ship. The Hybrid VAR-NN is a data analysis technique that integrates the linear capabilities of the VAR model with the nonlinear capabilities of the NN model to capture both linear and nonlinear trends simultaneously. The dataset for this study was generated from waves in a prototype ship experiment and divided into in-sample and out-of-sample data. The model was trained using the in-sample data, and predictions were made on the out-of-sample data using the trained model. The forecast results of the VAR-NN model were compared with those from the pure VAR and pure NN models. Model selection was based on out-of-sample performance criteria, with the Root Mean Square Error (RMSE) employed as the prediction performance metric. According to the experimental results, the Hybrid VAR-NN model outperformed the other models, demonstrating its ability to improve the prediction performance of the pure models through its hybrid approach.

Page 2 of 6 | Total Record : 51

 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