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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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Search results for , issue "Vol 9, No 5 (2025)" : 50 Documents clear
Integrative Geospatial Analysis of Agricultural Land Resilience Using NDVI-Based Remote Sensing and GIS: Spatio-Temporal Impacts of Urbanization in Sleman Regency (2017–2022) Pamungkas, Guntur Bagus; Firmansyah, Muhammad Reffi; Tamara, Anindya Putri; Zainul, Rahadian
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.9.5.2127

Abstract

This study delves into the dynamics of agricultural health in Sleman Regency, Yogyakarta Special Region Province, Indonesia, spanning the years 2017 to 2022. By integrating Geographic Information System (GIS) techniques with NDVI-based remote sensing using Landsat imagery from the USGS, our research aims to comprehend the spatio-temporal patterns and transformations in agricultural land health over the study period. The primary objectives encompass understanding these alterations and assessing how urbanization and land utilization impact the well-being of agricultural lands in Sleman Regency. The analytical framework incorporates geospatial processing using QGIS to classify and visualize vegetation health changes, enabling spatially explicit interpretation of land degradation trends. Throughout the 2017–2022 analysis period, a concerning and consistent decline in healthy agricultural lands was observed. By 2022, only 4581.56 hectares of agricultural land remained in a healthy state, constituting a mere 0.011% of the total region, while the expanse of unhealthy land surged from 1109.48 hectares in 2017 to 1160.8 hectares in 2022. This shift underscores a distressing deterioration in the health of agricultural plants due to diminished agricultural land. The geospatial analysis reveals a notable encroachment pattern from urban expansion zones into previously fertile areas, highlighting the urgency for integrated spatial planning. To counter this trend, proactive protection and effective regulation of designated agricultural zones by the Sleman Regency Government are imperative to ensure sustainable cultivation of essential food crops within the region and maintain the overall well-being of the agricultural landscape. The study contributes to advancing GIS-based land monitoring approaches and offers actionable insights for sustainable land use policy formulation in rapidly urbanizing regions. Strengthening policies for sustainable urban development in harmony with agricultural interests is pivotal to securing prosperous and balanced socio-economic growth.
An Evaluation of Persuasive Systems on Private Music School Websites Using the Persuasive System Design Model Hasugian, Leonardi Paris; Zeko, Chubo; Ahmadimaldeh, Ashkan; Carolina, Anita; Muqtadiroh, Feby Artwodini; Rakhmawati, Nur Aini
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.9.5.3325

Abstract

Music is one of the business opportunities within the creative industry, and the number of music schools is rapidly increasing in major urban areas. The City of Oulu in Finland has various music school providers that use websites as digital portals of information about their schools. Given the critical role of persuasive websites in marketing, this study aims to evaluate the websites of private music school providers in Oulu, with a persuasive system approach. To achieve this objective, a content analysis was conducted on fifteen websites and studied how they influence their users persuasively. Our method analyzes the website using a checklist-based approach grounded in the Persuasive System Design model, which comprises 28 principles categorized into four groups, namely: Primary Task Support, Dialogue Support, System Credibility Support, and Social Support. The analysis revealed that implementation of persuasive principles was uneven across the website, with the following average application rates per category: 28.57% (Primary Task Support), 28.57% (Dialogue Support), 38.10% (System Credibility Support), and 9.52% (Social Support). Several providers have applied the Persuasive System Design model according to each category, but not optimally. These findings suggest that there is still considerable room for improvement in the websites of private music schools through an optimally persuasive approach to support and motivate users to access information. The implication can encourage providers to optimize their website persuasively, engaging a broader audience and serving users properly.
A Cyber Security Model Using Gaussian Noise for Text Encryption and Decryption Algorithm Majeed, Sundus Hatem
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.9.5.2925

Abstract

Cybersecurity has become a critical issue in protecting personal information for all internet users, so the need to protect such information through different techniques has become necessary. One of these techniques is cryptography, which is used to protect data transferred over the internet by converting readable messages into unreadable messages. This paper introduces an innovative model for encryption and decryption utilizing Gaussian noise incorporation and denoising, exploiting the statistical characteristics of Gaussian distributions as the basis for encryption. The model proposed in this study encodes text through the transformation of characters into their corresponding ASCII values, followed by the addition of Gaussian noise characterized by specific mean (μ) and standard deviation (σ) parameters. The decryption process involves accurately subtracting the identical Gaussian noise, consequently retrieving the original text. The effectiveness of this technique heavily depends on maintaining the confidentiality of the Gaussian noise parameters. The results of our studies showcase specific decryption with a confined computational burden, rendering it suitable for applications with low processing capabilities, together with Internet of Things (IoT) gadgets and embedded systems. A side-by-side comparison of this approach with traditional encryption methods such as AES, RSA, and lightweight block ciphers is conducted, highlighting its advantages and possible application in resource-sensitive contexts. Analysis reveals that while the Gaussian noise-focused approach is straightforward and effective, more cryptographic verification is required to provide a strong defense against more complex cyberattacks. This test opens up the possibility of investigating noise-based fully cryptographic frameworks and their application in establishing consistent communication channels.
Adopt E-Mental Health Services: Factors Shaping Intention to Engage Samsudin, Rahimah; Khan, Nasreen; Subbarao, Anusuyah; Chen, Tan Booi Chen Booi; Obreja, Serban Georgică
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.9.5.3374

Abstract

E-Mental Health (eMH) tools are increasingly vital in providing scalable mental health support. This study aims to examine the factors influencing users’ intention to engage with eMH services. Specifically, it investigates the effects of six reflective first-order dimensions: Accessibility, Communication, Affordability, Flexibility, Custom Belief, and Government Support on the reflective second-order construct: Intention to Engage in E-Mental Health. Using data from 100 respondents, Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to test the hypothesized relationships and assess the strength of each contributing factor. The results show that Accessibility, Communication, and Flexibility are the most influential predictors of engagement. Affordability and Custom Belief demonstrate moderate but positive impacts, while Government Support plays a complementary role. These findings provide critical insight into user-centered design priorities for eMH platforms, particularly in enhancing user retention and accessibility. Practical applications include the development of multilingual mobile applications, culturally adaptive cognitive-behavioral therapy tools, and enhanced digital communication pathways. This study contributes to the understanding of how both infrastructural and personal belief factors can drive engagement. It also highlights the importance of holistic system support in digital mental health ecosystems. For future research, it is recommended to explore user engagement across diverse demographic and cultural settings and to examine the effectiveness of emerging technologies such as AI-driven chatbots and virtual reality therapies. Additionally, policy-level interventions should be further evaluated to strengthen the implementation and sustainability of digital mental health services.
Visualization of Data Inventory Using Visual Data Mining (VDM) and Exploratory Data Analysis (EDA) Methods Yanto, Iwan Tri Riyadi; Handayani, Ossie Purwanti
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.9.5.4075

Abstract

Naavagreen Sriwijaya Skincare Clinic in Semarang encountered difficulties in interpreting inventory data, which led to operational inefficiencies, stock imbalances, and potential sales losses. To address this issue, we aim to transform raw data into comprehensible visual insights for better decision-making. The study employed Visual Data Mining (VDM) and Exploratory Data Analysis (EDA) methods using Tableau software to visualize and analyze inventory records from January 2019 to December 2020. The methods were implemented in three main phases, consisting of project planning, data preparation, and data analysis. In the project planning phase, we conducted justification and a project plan, and identified the top business question. In the data preparation phase, we choose, transform, and verify the dataset. In the data analysis phase, we chose visualization or mining tools, analyzed the visualization or mining model, and verified and presented the visualization or mining model. The results indicated that among ninety-eight products, three were identified as efficient and three as inefficient based on their stock and sales behavior. Product visualizations showed distinct inventory patterns, while sales turnover lacked consistent trends, with the highest increase occurring in January 2020 at 12.86%. The visualizations were reviewed and validated by the clinic’s administrative team, demonstrating their practical value in supporting inventory management improvements. The efficiency dashboard indicates that Ng Facial Wash, Ng Skin Toner, and Ng Moisturizing Sunscreen 1 are deemed inefficient due to the imbalance between sales and incoming stock. Conversely, the top three most efficient products are Ng-Neher Pagi, Ng Badan Pagi, and Naavagreen Moist Aha Cream. This analysis aids in making informed decisions regarding stock management and future sales strategies.
Comparison Architecture of Convolutional Neural Network for Fertility Level of Paddy Soil Detection Natsir, Muh. Syahlan; Suyuti, Ansar; Nurtanio, Ingrid; Palantei, Elyas
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.9.5.3342

Abstract

This study proposes to detect the fertility of paddy soil based on texture, the power of Hydrogen (pH), and the amount of production. Fertile paddy soil provides essential nutrients and supports optimal plant growth. Therefore, monitoring and analyzing soil fertility is crucial in agricultural land management, which significantly increases rice yields. Paddy soil is categorized into three parts: very fertile soil, fertile soil, and reasonably fertile soil. This research proposes a new approach to detecting soil fertility levels based on factors that influence soil fertility using the Convolutional Neural Network (CNN) algorithm. There are 558 paddy soil datasets of 178 very fertile datasets, 135 fertile datasets, and 245 quite fertile datasets. In this research, we conducted trials using the CNN, Resnet, Enet, and VGG19 models. According to the test results, the CNN model using the Adam optimizer and a learning rate of 0.001 achieves the highest training accuracy of 0.9687 and validation accuracy of 0.8333. This suggests that this model can accurately identify the fertility of paddy soil, making it easier to calculate the fertility of paddy soil through its use. Future research can expand this study by integrating additional soil parameters, such as nitrogen, phosphorus, potassium levels, and organic matter content, to improve classification accuracy further. Additionally, employing multimodal data sources, such as remote sensing and hyperspectral imaging, could enhance the model's robustness in various environmental conditions. Further optimization of deep learning architectures and Artificial Intelligence (AI) techniques can also provide better interpretability and usability for agricultural stakeholders.
Hyperellipsoid Cluster Merging using Hierarchical Analysis of Hyperellipsoid Cluster for Image Segmentation Kaswar, Andi Baso; Nurjannah, Nurjannah; Djawad, Yasser Abd
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.9.5.2815

Abstract

Segmentation is one of the critical stages in digital image processing and computer vision. However, conventional clustering-based segmentation methods, such as K-means and Fuzzy C-means (FCM), are still unable to accurately segment images whose pixels form hyperellipsoid clusters in the feature space. In addition, previous clustering methods based on Mahalanobis distance measurement require a long computational time and still have the potential to fall into local optima. Therefore, in this paper, we propose a new method for segmenting images whose pixels form hyperellipsoid clusters in the feature space, utilizing hyperellipsoid clusters merging through hierarchical analysis of hyperellipsoid clusters. The proposed method comprises eight main steps: histogram extraction, peak and valley identification, elimination of low peaks and valleys, peak combination for centroid initialization, initialization of cluster pixel members, elimination of ineffective clusters, hyperellipsoid cluster merging, and finalization of cluster members. This paper presents a novel approach to segmenting color images by employing an initial centroid discovery process and cluster analysis that considers cluster covariance for cluster merging. Based on the tests conducted using various image characteristics, the proposed method can provide 97.42% accuracy, 98.02% precision, 97.15% recall, 2.58 misclassification error, 97.54 F1-score, 95.29% intersection over union, 97.52% dice coefficient, and 15.37 seconds of computation time. The test results are superior to those of conventional methods, such as K-means and FCM. Based on these results, it can be concluded that the proposed method can effectively segment images with high accuracy. The proposed method can serve as an alternative approach to image segmentation.
An Eccentricity for Improvement in Rice Stem Borer Detection Using Sensed Drone Imaging Indrabayu, -; Basri, -; Achmad, Andani
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.9.5.2864

Abstract

Rice stem borers are severe pests that cause significant crop losses. This research aimed to tackle this problem by using a drone equipped with a high-resolution camera to capture detailed images of paddy fields. These images were then processed to estimate the early potential attacks of stem borer pests through color segmentation computing. The detection process relied on analyzing color variations, particularly focusing on symptoms indicative of stem borer presence. The system utilized Hue, Saturation, Value (HSV) color segmentation and advanced image processing algorithms on numerous rice field videos collected from drone flights conducted at altitudes ranging from 5 to 40 meters above the ground. To improve detection accuracy, the study tested the system with and without the eccentricity parameter, which is crucial in eliminating false positives caused by the misidentification of field embankments as stem borers. This research's primary contribution is the implementation of eccentricity, which significantly reduces the false-positive rate. The results demonstrated that the accuracy of the system with the eccentricity parameter included was 75%, compared to a significantly lower accuracy rate of 17.19% when the eccentricity parameter was not used. Overall, this study highlights the effectiveness of using drones for remote sensing and the importance of incorporating eccentricity in image processing algorithms to enhance the precision of early stem borer detection in rice fields. This approach not only improves the reliability of pest detection but also offers a promising method for protecting rice crops from severe pest damage.
Predictive Performance of Machine Learning on Low-Birth-Weight Classification: A Study from Asia Developing Countries Putera, Muhammad Luthfi Setiarno; Adawiyah, Rabiatul; Ahmidi, Ahmidi
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.9.5.3384

Abstract

This study is aimed to evaluate the predictive performance of several machine learning models in classifying low birth weight (LBW) infants. This classification is necessary, as low birth weight is linked to many health hazards for newborns. This study conducted machine learning to examine socio-economic variables, maternal health, and additional pertinent aspects that influence low birth weight (LBW) in developing countries, such as India, Indonesia, Jordan, and the Philippines. The independent variables were type of residence, number of household members, mother's education level, mother's occupation, father's occupation, welfare status, number of births for the last 5 years, mother's age at first birth, mother's smoking status, birth order, infant's alive status, number of antenatal care visit, and type of antenatal care. The total eligible sample included 12,393 respondents of Indonesia, 21,681 of India, 6,365 of Jordan, and 5,704 of the Philippines. The findings demonstrate that several machine learning models, including Support Vector Machines (SVM), Random Forest, and Decision Trees, exhibit differing degrees of accuracy in predicting low birth weight (LBW) across India, Indonesia, Jordan, and the Philippines. For example, SVMs exhibited superior performance, although Naive Bayes attained elevated sensitivity. The results indicate that customized strategies reflecting regional attributes are necessary for enhancing prediction precision in LBW classification. This underscores the need of accounting for local socio-demographic variables when using machine learning models in healthcare study.
Classification and Visualization Model of Stunting Zone Distribution Using Artificial Intelligence and Streamlit Approaches Zuraiyah, Tjut Awaliyah; Widanti, Nurdina; Yamato, Yamato; Chairunnas, Andi; Mauludin, Kriti; Setha, Bira Arya
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.9.5.3174

Abstract

Time series datasets enable automated classification processes. Machine Learning (ML) and Deep Learning (DL) models are Artificial Intelligence (AI) models that allow systems to make intelligent decisions automatically. Stunting is a significant public health issue that warrants serious attention. Decision-making requires accurate, data-driven information that is easily understandable. However, many classification results have not been visualized in a way that allows users to understand them easily. This study aims to evaluate the performance of the classification model and visualize the distribution of areas using the Streamlit framework. The ML classification models used are Decision Tree and Extreme Gradient Boosting (XGBoost), while the DL classification models used are LSTM and Bi-LSTM models. The visualization tool was developed using the Python programming language integrated with the web-based Streamlit framework. SMOTE is used to balance the dataset, thereby improving accuracy. Stunting data were obtained from the Bogor City Health Office in the form of By Name By Address (BNBA) stunting data for 2022 - 2024, totaling 6023 data. The model performance is evaluated by assessing accuracy, precision, recall, and F1 score. The results show that the BiLSTM model performs better after data matching with SMOTE, achieving an accuracy of 99.43%. Bi-LSTM has two directions: forward (from past to future) and backward (from future to past). This intelligent system uses the BiLSTM model and is dynamic, providing an automatic display of stunting classification and distribution zones. So, stakeholders can use it to get recommendations for stunting decision-making and further research.

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