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
Analysis of Pneumonia from Chest X-Ray Images Using an Optimized Ensemble Machine Learning Models with Voting Classifier Monita, Vivi; Hanan Lutfianto, Naufal; Dyah Irawati, Indrarini
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.9.5.3689

Abstract

Pneumonia is a pulmonary disease resulting from infections caused by bacteria, viruses, or fungi that invade the lungs. This condition leads to inflammation due to the accumulation of fluids, blood cells, and other substances in the alveoli. Common symptoms experienced by patients include fever, coughing, and production of phlegm. Although pneumonia can affect individuals of any age, those with weakened immune systems are particularly vulnerable. Children and elderly individuals are especially prone to contracting this illness. The present research employs an ensemble learning approach for pneumonia detection using chest x-ray images to address this issue, specifically integrating support vector machines (SVMs) and random forests (RFs). The primary aim is to evaluate the effectiveness of ensemble learning through a voting classifier in improving pneumonia detection accuracy compared to individual machine learning models. The methodology includes preprocessing the data with contrast-limited adaptive histogram equalization (CLAHE), which minimizes noise by defining a kernel matrix and substituting each pixel's intensity with the weighted average of its neighboring pixels and itself. The research also involves training models using SVM and RF algorithms with hyperparameter optimization. These individual models are then assessed and compared using performance metrics such as accuracy, area under the curve (AUC), specificity, sensitivity, confusion matrix, and computational efficiency. By harnessing the strengths of ensemble learning, this research aims to contribute to the development of reliable pneumonia detection systems, with potential applications in clinical environments where timely and accurate diagnosis is essential for patient management. This research achieved 99.40% and 96.32% accuracy, 99.97% and 96.52% AUC, and 0.0436% and 0.0451% loss. This method tackles others that use deep learning and single machine learning with all balanced datasets.
Real-Time Embedded Vision System for Road Damage Detection Utilizing Deep Learning Putri, Ambarwati Rizkia; Irwansyah, Arif; Arifin, Firman; Purwantini, Elly; Wijaya, Candra Kusuma
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.9.5.3661

Abstract

Accidents resulting from road damage are becoming a serious concern, emphasizing the need for efficient monitoring systems and timely government intervention. This research highlights the potential of advanced AI-driven solutions in road safety management, providing a practical approach to efficiently monitoring and maintaining road conditions. It presents a real-time embedded vision system for automatic road damage detection using deep learning techniques. The system is designed to classify six types of road damage and has been implemented on two platforms: Jetson Nano and a personal computer or laptop. A comparative analysis was conducted to evaluate accuracy, computational performance, and power efficiency. The study employs YOLO (v5, v7, v8) and EfficientDet algorithms for detecting road damage. Experimental results indicate that EfficientDet achieves the highest accuracy at 88%, while YOLO attains 63%. In terms of computational performance, YOLOv8 delivers the highest frame rate, reaching 25 FPS on the Jetson Nano. Power efficiency analysis reveals that YOLOv8 on the Jetson Nano is six times more energy-efficient compared to its implementation on a laptop. Likewise, EfficientDet on Jetson Nano demonstrates three times better energy efficiency than on a laptop. These findings underscore the feasibility of deploying AI-powered embedded vision systems for detecting road damage. The use of deep learning models on energy-efficient platforms, such as Jetson Nano, enhances real-time performance while minimizing power consumption. Future research should focus on optimizing these models to enhance performance on edge devices while further assessing their practical applications in real-world environments.
Deep Learning-based Models with YOLOv7 and Convolutional Neural Networks for Vehicle Detection and Recognition Nugroho, Wahyu Adi; Supriyanto, Catur; Safar, Noor Zuraidin Mohd
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.9.5.3584

Abstract

The application of artificial intelligence (AI) technology has become prevalent across various sectors, including transportation and smart city. A key implementation of AI in this domain is traffic monitoring, often relying on license plate recognition to identify vehicles. However, this approach faces limitations when plates are obscured. To address this issue, this research explores a broader approach by recognizing general vehicle attributes, ensuring more accurate identification and comprehensive traffic statistics. The proposed solution integrates the You Only Look Once (YOLO) object detection algorithm and convolutional neural networks (CNN) pretrained models for vehicle attributes recognition. This study utilizes multiple datasets, including Roboflow Vehicle, Stanford Cars, VehicleID, and VCoR, to ensure comprehensive model evaluation. Experimental results indicate that YOLOv7 achieved a mean average precision (mAP) score of 86.1% for vehicle detection, with an average precision (AP) score of 91.5% for the car class. For vehicle make and model recognition, the lightweight EfficientNetV2S model demonstrated the highest accuracy score, achieving 89.8% and 99.2% on the Stanford Cars and VehicleID dataset, respectively. For vehicle color recognition, DenseNet201 models achieved the highest accuracy score of 87% on the VCoR dataset. These findings underscore the effectiveness of integrating YOLOv7 and CNN models for robust vehicle detection and recognition. This research provides a practical solution to the limitations of traditional license plate recognition methods, contributing to the development of more accurate and efficient traffic monitoring systems. Future studies may further optimize the framework for real-time applications and diverse traffic scenarios.
Optimization and Collaboration of Fuzzy C-Mean, K-Mean, and Naïve Bayes Algorithms Using the Elbow Method for Micro, Small, and Medium Enterprises Norhikmah, -; Nurastuti, Wiji; Aminuddin, Afrig; Sidauruk, Acihmah; Gunawan, Puguh Hasta
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.9.5.3292

Abstract

Micro, Small, and Medium Enterprises (SMEs) have a vital role in Indonesia’s economy. However, IT-based marketing strategies among SMEs receive limited support from the government due to the lack of sufficient data to inform policy. This study aims to (1) identify the needs of SMEs for social media promotion training as part of their digital capacity building, (2) develop and compare the effectiveness of classification models that combine Fuzzy C-Means and K-Means clustering algorithms with the Naïve Bayes algorithm to group SMEs based on business characteristics, (3) analyze the relationships between business variables—such as business type, marketing media, funding sources, and financial aspects—and SME performance through regression analysis, and (4) provide data-driven foundations for designing targeted digital interventions and policy strategies to support SME development in Indonesia. This study used UPPKS data from 133 SMEs in seven districts in the Special Region of Yogyakarta. Data analysis covered business types, marketing platforms used, funding sources, and financial performance indicators. Data pre-processing involved cleaning, normalization, and integration to ensure consistency and readiness for analysis. The researcher used the Elbow method to determine the optimal number of clusters. Then, it also used Fuzzy C-Means (FCM) and K-Means to categorize SMEs into three groups: high, medium, and low. The classification was based on the Naïve Bayes algorithm. The evaluation of the model performance used a confusion matrix, cross-validation, and regression analysis to examine inter-variable relationships. The results showed that the combination of FCM and Naïve Bayes achieved an accuracy of 85% based on the confusion matrix and 97% based on cross-validation. Meanwhile, the combination of K-Means and Naïve Bayes respectively achieved an accuracy of 96% and 94.7%. These findings demonstrate the effectiveness of the proposed approaches in classifying SMEs based on their characteristics and performance. This research provides important insights for policymakers and SME development agencies in designing more targeted digital training and support programs. Future studies should explore the integration of other algorithms, such as Support Vector Machines (SVM) and Decision Trees, while incorporating market trends and customer engagement to enhance SME classification and provide ongoing support.
Comparison of Salp Swarm Algorithm and Particle Swarm Optimization as Feature Selection Techniques for Recession Sentiment Analysis in Indonesia Kristiyanti, Dinar Ajeng; Sanjaya, Samuel Ady; Irmawati, Irmawati; Ekachandra, Kristian; Suhali, Jason; Hairul Umam, Akhmad
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.9.5.3102

Abstract

Amidst global economic uncertainty, this study focuses on Twitter sentiment during the global recession issue on social media, especially in Indonesia. By utilizing sentiment analysis, this study uses machine learning algorithms such as Naïve Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) which are still less than optimal on high-dimensional Twitter data. The purpose of this study is to improve the accuracy of conventional machine learning using basic metaheuristic algorithms, namely the Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO) as feature selection. From January to May 2023, this study captures the evolving sentiment in response to economic conditions. Data preprocessing, including labeling through the TextBlob and VADER libraries, sets the stage for the analysis. Performance is compared based on labeling techniques, feature selection, and classification algorithms. Specifically, when applied to VADER labeled data without feature selection, the SVM model achieves an outstanding accuracy of 83% and an F1 score of 67%—notably, the application of SSA and PSO results in a reduction in model accuracy by 1%. However, the application of SSA and PSO slightly reduced the model accuracy performance by 1%. On the TextBlob labeled data, SVM showed an outstanding performance (80% accuracy, 77% F1 score). Interestingly, PSO on TextBlob data with SVM significantly decreased the model's performance. These findings contribute significantly to understanding the intricacies of sentiment dynamics during economic uncertainty on social media platforms, with SVM emerging as a strong choice for practical sentiment analysis.
A Slab Multi-Fold Classification Technique on A Mixed Pixel Hyperspectral Image Purwadi, -; Abu, Nor Azman; Mohd, Othman; Kusuma, Bagus Adhi; Ahmad, Asmala
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.9.5.3432

Abstract

Hyperspectral imaging offers a significant edge over standard RGB and multispectral images for land classification. It captures a wider range of electromagnetic waves, producing more detailed images than previous methods. This allows objects to be identified and distinguished with high certainty due to hyperspectral capabilities. However, the large data volume makes reducing the computational workload challenging. Imbalanced data and suboptimal hyperparameter settings can reduce classification accuracy. Hyperspectral image classification is computationally demanding, especially with mixed-pixel issues in high-resolution images. This study uses EO-1 satellite imagery with a 30-meter resolution affected by mixed pixels. It introduces a new classification approach to effectively use hyperspectral remote sensing at this resolution. The process includes satellite image preprocessing—geometric correction, image enhancement with FLAASH, and geometric and atmospheric corrections. To lessen the computational burden, a slab approach partitions the 242 spectral bands into segments, extracting features from each, resulting in fewer total features. These features are then input into a support vector machine (SVM) for five-class classification. Parameters like polynomial order, kernel scale, and kernel type are tuned for optimal accuracy. A novel SLAB Multi-Fold technique is proposed. Results indicate that the slab method combined with SVM achieves a maximum accuracy of 51.39%. The best results came from slab 2, with a polynomial order of 8 and k=4, using both linear and Gaussian kernels. These findings offer valuable insights for future research on satellite image classification, especially when tuning multiple hyperparameters within this SLAB approach. Future work could compare these results with higher-resolution images and different datasets to better evaluate the technique's accuracy.
Leveraging Machine Learning to Predict Future Human Development Gsim, Jamal; Zeriab Es-sadek, Mohamed; Sonatha, Yance
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.9.5.2543

Abstract

This study utilizes a rich repository of global development data to forecast the Human Development Index, harnessing the World Bank's World Development Indicators (WDI) database and the United Nations Development Program 's extensive human development metrics as primary data sources. Employing R as the driving force, this research unfolds through a meticulously structured four-phase methodology. The initial phase encompasses data pre-processing tasks, including web scraping, merging, cleansing, and transforming datasets. Subsequently, exploratory data analysis is conducted to unravel correlations and regression patterns among variables, culminating in the creation of refined data frames. The crux of this study revolves around machine learning, where two distinct random forest models are crafted: one for regression and another for classification purposes. Additionally, authentic development indicators are used to predict the Human Development Index accurately. Beyond merely deploying machine learning techniques, this research highlights the importance of adopting a multifaceted approach to assess and address global development challenges. This study not only aims to predict the Human Development Index but also lays a foundation for future research endeavors in this domain. It opens up avenues for exploring novel methodologies and datasets to make more precise and comprehensive predictions of human development indices. The findings of this research are poised to make a significant contribution to understanding the dynamics of global development and devising effective strategies for promoting human well-being worldwide.
Real-Time Tuberculosis Bacteria Detection Using YOLOv8 Sigit, Riyanto; Yuniarti, Heny; Karlita, Tita; Kusumawati, Ratna; Maulana, Firja Hanif
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.9.5.3147

Abstract

Tuberculosis (TB) is a contagious disease caused by the bacterium Mycobacterium tuberculosis. If not adequately managed, TB can become a fatal, life-threatening condition. In Indonesia, TB remains a critical public health issue, with millions affected and the country ranking third globally in TB cases, following India and China. Symptoms of TB include persistent cough lasting more than three weeks, hemoptysis (bloody sputum), fever, chest pain, and night sweats. The widely used diagnostic method in Indonesia is the Ziehl-Neelsen stained sputum smear technique, which processes sputum samples with specific reagents, allowing acid-fast bacilli to be visualized through microscopic examination. However, this process is labor-intensive and time-consuming, often requiring between half an hour and several hours for an accurate diagnosis. To address these challenges, there is a crucial need to develop technology that accelerates the TB diagnosis process, facilitating easier labor for healthcare workers. This study focuses on employing YOLOv8 to automate the detection of acid-fast bacilli. The system acquires sputum sample images from a microscope, and the acquired data is then used to train the model for detecting tuberculosis bacteria. The proposed real-time approach, employing the YOLOv8 algorithm, has demonstrated adequate performance for one of our specialized models, achieving a precision score of 0.88, a recall score of 0.77, and an F1 score of 0.82. This research aims to enhance TB case detection and increase treatment coverage, thereby improving overall public health outcomes in Indonesia.
A Machine Learning Approach to Spatial Analysis of Paddy Field Conversion Using Multispectral Sentinel-2A Imagery Fauzan, Achmad; Kurnia, Anang
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.9.5.3617

Abstract

The expanse of rice fields is a critical metric as it is intimately linked to agricultural productivity in a given locale. This study investigates the application of satellite imagery to quantify trice fields' acreage and temporal variations. The data utilized was acquired by the Sentinel-2A multispectral satellite. The variables employed are the image's baseband and spectral index. The research area encompasses the Sukamakmur sub-district in Bogor Regency, Indonesia. The types of machine learning models include Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and k-Nearest Neighbor (kNN). The simulation of class numbers is conducted to achieve the most stable and precise evaluation metric values. The XGBoost algorithm is used for the overall classification process of the region based on the optimal metric score. The model's accuracy, precision, recall, and F1-score are 92.37%, 92.3%, 92.38%, and 92.33%, respectively, indicating a very good performance. The model successfully captures a decline in rice field area between 2020 and 2023. Using the Modified Moran’s Index (MMI), the study reveals a positive spatial autocorrelation, indicating a clustered pattern in land-use change. Regions that experience either substantial or minor changes in land use are commonly situated near areas exhibiting similar characteristics. This study presents a spatially aware machine learning framework that enables the effective monitoring of agricultural land-use dynamics. In the future, this framework can be enhanced by integrating time-series forecasting and socio-economic data, supporting more informed decision-making in food security planning and agricultural policy development.
Deep Learning Models Performance for Classifying Dried Chili Based on Digital Image Analysis Yudantoro, Tri; Prayitno, Prayitno; Rizal Isnanto, Rizal; Djaeni, Moh
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.9.5.2919

Abstract

Artificial Intelligence and agriculture have been combined to create significant advancements in smart agricultural analysis that have improved both output and quality. This approach has completely changed conventional farming operations by utilizing image processing technologies. To assess the dryness levels of red chili peppers—a crucial component of crop quality and market value—the study set out to compare the efficacy of several CNN architectures. A dataset with 600 training images and 150 testing images spread over three classes was used to evaluate four CNN models (MobileNetV2, DenseNet121, InceptionV3, NASNetMobile). With a validation accuracy of 99%, DenseNet121 outperformed MobileNetV2 (which had a validation accuracy of 97%). The findings demonstrate how deep learning models can improve sorting procedures for agriculture by increasing accuracy and productivity. A scalable, impartial, and economical way to uphold crop standards and promote industry sustainability is by incorporating CNNs into the classification of agricultural products. The results of this study represent a breakthrough in the application of deep learning to agriculture, opening the door to automated systems that guarantee constant product quality. By optimizing yield and quality through image processing technology, the findings highlight the revolutionary influence of AI in smart agriculture. To increase production and improve competitiveness in the market, future research efforts may focus on developing automated sorting systems and further enhancing CNN models for agricultural applications. The research adds to the increasing corpus of work using AI in agriculture to enhance crop management and quality control.  

Page 4 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