JOIV : International Journal on Informatics Visualization
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Wireless Data Communications in WSN Networks Using UAV
Get PDFThis research explores the impact of environmental and technical factors on air-to-ground (A2G) wireless communications using drones, specifically tackling challenges like multipath propagation, Doppler effects, and geographical variability. The study aims to analyze performance determinants of A2G communications, develop a simulation model to predict communication issues and offer recommendations for optimizing interactions between drones and ground stations. The methodology includes data collection from field tests and simulations, focusing on various environmental and weather conditions. Statistical data analysis, including regression and hypothesis testing, is employed to interpret the results. Key findings reveal that factors such as operational altitude, drone speed, and weather conditions—mainly rain—significantly affect throughput, latency, and packet loss. Optimal communication performance is achieved at a drone height of 120 meters, with rural environments offering the best conditions for data transmission. Conversely, urban settings experience decreased throughput and increased latency due to physical obstructions like buildings and infrastructure. These insights highlight the need for adaptive communication technologies and comprehensive testing across diverse conditions. The research advocates further exploring advanced antenna technologies, dynamic operational adjustments informed by real-time environmental data, and robust security measures to enhance communication reliability. In conclusion, this study establishes a strong foundation for future advancements in drone communication technologies, aiming to improve the safety and efficiency of drone operations across various applications. The findings serve as a roadmap for developing innovative solutions to address the inherent challenges of A2G communications in varying operational environments
A Framework for Integrated E-notary Services Based on Blockchain for Civil Law Notaries: The Case of Indonesia
Get PDFThe trend of digitalization has called for electronic notary services that are both efficient and secure. This study proposes a framework for an integrated e-notary system using blockchain technology based on Indonesia's civil laws and regulations. To accomplish this objective, this study uses a methodology combining both normative legal and information systems methods. This study starts with a search of existing laws and regulations conducted on the Indonesian government regulation database (peraturan.go.id). Subsequently, laws and regulations are analyzed to elicit system components and functional requirements. The findings are visualized using a rich picture, resulting in a framework for an integrated e-notary system. The system entails a blockchain network in which Indonesian registered notaries act as nodes. The proposed system is integrated with other e-government systems to facilitate notarial services as required by laws and regulations, such as document validity checks, electronic recording and storage of notarial deeds, document legalization, and notary protocol archiving. To support the proposed blockchain-based e-notary system, this study suggests several regulatory adjustments based on legal gaps identified using Kostruba’s approach. Regulatory adjustments include creating technical regulations on the establishment of the blockchain network operated by the Indonesian Notary Association (INI) and also the creation and storage of notarial deeds electronically. The findings imply that the proposed e-notary system has the potential to enhance notary services’ security and efficiency in Indonesia, though successful implementation of such a system may hinge upon the readiness of the stakeholders
Exploring Strategies to Improve Digital Literacy Assessment Using Log Data Analysis
Get PDFThe purpose of this study is to propose improvement strategies for national digital literacy assessment tools based on the analysis of log data from the first performance-based evaluation conducted in 2023. To achieve this, we analyzed log data from a total of 32,804 primary and middle school students. For the analysis, eleven types of data, including problem domain, problem type, and total number of logs, were utilized for the analysis. Students were assessed on their digital literacy level through 26 items, with primary school students given 40 minutes and middle school students given 45 minutes for the assessment. The key findings indicate that primary school learners generated 1.5 million log entries spanning four modules, whereas middle school participants produced 3.2 million log data points. Both primary and middle school students showed an increasing tendency to skip questions without answering as they progressed through the latter part of the assessment. Additionally, the tendency to skip questions increased when the minimum number of clicks required to solve a problem increased or when the problem length was longer. In the future, it is necessary to clearly define which parts of the log should be recorded in advance so that logs are consistently recorded. To accurately perform analyses such as student response type and pattern analysis, and error type analysis, a design for appropriate log data recording should be prioritized. This will enhance the reliability and validity of the tools and serve as a basis for future digital literacy policy development
Visualization of Data Inventory Using Visual Data Mining (VDM) and Exploratory Data Analysis (EDA) Methods
Get PDFNaavagreen 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
A Better Performance of GAN Fake Face Image Detection Using Error Level Analysis-CNN
Get PDFThe use of face images has been widely established in various fields, including security, finance, education, social security, and others. Meanwhile, modern scientific and technological advances make it easier for individuals to manipulate images, including those of faces. In one of these advancements, the Generative Adversarial Network method creates a fake image similar to the real one. An error-level analysis algorithm and a convolutional neural network are proposed to detect manipulated images generated by generative adversarial networks. There are two scenarios: a stand-alone convolutional neural network and a combination of error-level analysis and a convolutional neural network. Furthermore, the combined scenario has three sub-scenarios regarding the compression levels of the error-level analysis algorithm: 10%, 50%, and 90%. After training the data obtained from a public source, it becomes evident that using a convolutional neural network combined with compression of error level analysis can improve the model’s overall performance: accuracy, precision, recall, and other parameters. Based on the evaluation results, it was found that the highest quality convolutional neural network training was obtained when using 50% error level analysis compression because it could achieve 94% accuracy, 93.3% precision, 94.9% recall, 94.1% F1 Score, 98.7% ROC-AUC Score, and 98.8% AP Score. This research is expected to be a reference for implementing image detection processes between real and fake images from generative adversarial networks
Developing Augmented Reality as a Teaching Material to Enhance Cultural Awareness in Secondary Schools
Get PDFThis study investigates the development of Augmented Reality (AR)-based teaching materials to enhance students’ understanding of local wisdom and cultural diversity in secondary schools in South Sulawesi, Indonesia. Employing a quasi-experimental design, the research involved 304 students across four major ethnic regions, namely Makassar, Bone, Toraja, and Mandar, which were divided equally into experimental and control groups. The experimental group utilized AR-integrated materials, whereas the control group employed traditional methods. Data were collected through pre-tests, post-tests, and student perception surveys. Results revealed a significant increase in students' cultural awareness and understanding after the intervention, particularly among Bugis Makassar and Bugis Bone groups. The mean improvement in the experimental group was statistically significant (p < 0.001), confirming the pedagogical benefits of AR in promoting cultural literacy. Additionally, students expressed strong appreciation for AR’s interactive features, which enhanced their engagement and motivation. This study underscores the importance of integrating AR and local wisdom in educational content to foster inclusive, culturally responsive learning environments. The findings emphasize that augmented reality (AR)-based instructional resources serve as a powerful tool in enhancing students' understanding of indigenous knowledge and cultural diversity within secondary education. The incorporation of local wisdom into educational content not only enhances students’ learning experiences but also cultivates a profound appreciation of their cultural heritage. The results highlight the potential of AR as a transformative technology that can bridge the deficiencies inherent in conventional pedagogical approaches, particularly in fostering cultural awareness among learners
Exploring Digital Competency as a Fundamental Job Competency in Higher Education
Get PDFIn the era of artificial intelligence, emerging digital technologies have revolutionized the nature of workplaces, making digital competency (DC) an increasingly essential competency in the modern job market. However, there are discrepancies between the existing and required levels of DC among employees, highlighting the need for proper and early educational interventions to foster this competency in higher education. In response to this need, this study aims to explore the degree of commitment (DC) among university students in work contexts. This study first developed an instrument to assess the level of DC and conventionally stressed job competencies—cognitive, interpersonal, and self-leadership—and applied it to 4,297 first-year university students. The study first compared the students' DC levels with other job competencies and found that their DC levels were lower than those of other competencies. Additionally, the study investigated the relationship between DC and other job competencies, identifying the prerequisite role of DC in affecting other competencies. Finally, the study also explored factors that promote DC and found that students' interest in emerging information and communication technologies is the most prominent indicator of their DC level. We also examined the effect of experience and attitude toward learning programming on the DC level and found that they were also significant factors. In particular, learning both block-based and text-based programming languages was the most effective means to improve DC. Accordingly, the practical implications for future studies and stakeholders regarding students' DC in higher education were discussed
Lecopelese - a Novel Evaluation Model for Measuring Educational Aspects of Game-based Learning
No full textThis study aimed to establish a model for assessing the pedagogical quality of mobile game-based learning (GBL), which seeks to convey educational content to users. Evaluating the educational effectiveness of GBL necessitates a robust model tailored for this purpose. Current models can be improved to better address various educational challenges associated with mobile GBL. The LECOPELESE (LEarning COntent, PEdagogy and LEarning StyLE) model was developed by integrating relevant constructs identified in existing literature. To validate this model, a qualitative research approach was employed, drawing a sample from 270 undergraduate students. The analysis utilized Structural Equation Modeling (SEM) and resulted in a final model based on rigorous factor analysis. The findings indicated that the proposed model effectively measures educational quality in game-based learning. This new model includes more comprehensive constructs and items, addressing the educational aspects of game-based learning. Specifically, the model introduces a pedagogy construct to evaluate game-based learning quality, reflecting criteria for outstanding educational content and delivery through mobile applications. It assesses how effectively GBL provides real-world learning experiences. Additionally, the research highlights that the quality of pedagogy is influenced by two key factors: the GBL's ability to accommodate learners' unique characteristics (learning styles) and the quality of the learning content that adapts to learners' needs. Ultimately, the study demonstrates that both learning content and style significantly impact the pedagogy construct, suggesting that enhancing these areas can improve the overall pedagogical quality of game-based learning
Development of a Life Story-Based Digital Counseling Model to Detect Student Depression Using LSTM
Get PDFThis research aims to develop an LSTM-based model to help counselors analyze depressive symptoms in students based on their life stories. Depression often occurs among students, which can affect their lives. However, counselling can overcome these mental problems. In order to support the Indonesian government's programs in the field of mental health, concrete steps are needed. One concrete effort is to prevent children from experiencing depression. Depression can be recognized early through a counselling approach. Currently, counselling can be done using digital counselling technology. Therefore, a reliable model is needed to help counsellors. This research used 2,551 tweets about someone's life story from 2,581 datasets. ANN method with LSTM (Long Short-Term Memory) architecture. This counselling is effective in helping individuals resolve psychological and emotional problems, especially depression. The advantage of LSTM is that it can delete data that is no longer relevant. This method effectively processes, predicts, and classifies data based on a certain time sequence. The dataset was taken from Twitter(X) and then validated by experts before being trained with the model. As a result, the model can recognize the depression levels with a test accuracy of 86%. This research has implications in psychology regarding cases of student mental health in realizing the vision of Indonesia in 2045
Restricted Boltzmann Machine Approach for Diagnosing Respiratory Diseases
Get PDFRespiratory diseases remain a significant global health challenge, particularly in developing countries where high morbidity and mortality rates persist. This study aims to establish a diagnostic approach for respiratory diseases using the Restricted Boltzmann Machine (RBM) method to support early detection and improve clinical decision-making. The research utilizes 180 medical records from patients at I. A Moeis Samarinda Hospital, East Kalimantan, Indonesia, includes 22 symptom variables associated with six respiratory disease types: sinusitis, pharyngitis, bronchitis, pneumonia, tuberculosis, and asthma. The collected data were preprocessed into binary formats to represent symptomatic and asymptomatic conditions, facilitating practical training in the RBM model. Data splitting was conducted with 70:30, 80:20, and 90:10 ratios for training and testing sets. The RBM architecture was optimized to enhance model performance by tuning key parameters, including the number of epochs, learning rate, and hidden neurons. Experimental results demonstrate that the RBM model achieved high diagnostic accuracy, with an accuracy of 98%, sensitivity of 98%, and specificity of 99% under the configuration of 5000 epochs, a learning rate of 0.1, and 53 hidden neurons. These findings indicate the model’s capability to recognize patterns and accurately classify respiratory diseases based on clinical symptoms. The study highlights the potential of integrating AI-based diagnostic systems like RBM into healthcare services, particularly in resource-limited settings. Future research should explore larger, more diverse datasets and consider environmental and socioeconomic factors to improve the model’s generalizability and practical applicability