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Metaverse Adoption in Public Sector: A Bibliometric Analysis
Get PDFThe objective of this study is to map the landscape and knowledge structure of the public sector metaverse through bibliometric analysis. Following this, a descriptive overview of the current state of public sector research related to the metaverse, including the leading ten authors and sources, most relevant keywords, and primary research topics, will be presented. The selection of the Scopus database was based on its status as the largest repository of its kind and its provision of an extensive compilation of abstracts and citations for peer-reviewed articles, proceedings, and journals. The initial search yielded 369 documents from Scopus; after eliminating duplicate and irrelevant articles, 354 documents remained. The results demonstrate the substantial development of the metaverse in the public sector, with an annual growth rate of 14.89%. This indicates that the adoption of virtual technology and the metaverse is gaining importance in the public sector. Castronova ranks first in terms of the quantity of studies published, with five articles, followed by De Kool and Zhang. "Metaverse," "virtual reality," and "virtual world" are the most pertinent keywords. Due to the extensive research that has been conducted on these phrases, they represent fundamental concepts in the field. Additional frequently occurring terms include "social networking (online)," "interactive computer graphics," "government data processing," and "internet." These terms underscore the significance of governance, human factors, technology, and data in shaping the trajectory of digital government services. They also exemplify the interdisciplinary character of metaverse applications within the public sector
Service Quality Analysis of Unej Digital Library Using M-S-QUAL and Importance Performance Analysis Methods
Get PDFA lot of library services are impacted by the improvement of technologies. With this modification, the traditional library services are now entirely digital. To carry out this digitalization, the Unej Digital Library (UnejDigiLib) application was created by the University of Jember's library. The purpose of developing this application is to improve the effectiveness of library services, which were previously hindered by the COVID-19 epidemic. The UnejDigiLib developer has not yet evaluated the quality of its services since the application's release, so they are unsure of whether the current services satisfy the user needs. The goal of this study is to combine Mobile Service Quality (M-S-QUAL) and Importance Performance Analysis (IPA) in order to assess the UnejDigiLib service's quality based on users perceptions. The M-S-QUAL is used to determine the service quality indicators and examine the gap between their performance and importance. After that, the service indications are mapped using the IPA based on their priority level. The M-S-QUAL dimensions that used are: compensation, privacy fulfillment, content, system availability, efficiency, and privacy. Data collection was carried out through online surveys and interview. The respondents are Unej students who had used and borrowed e-book from UnejDigiLib. The sample was determined using simple random sampling and obtained 287 respondents. The findings indicate that the user expectations have not been met by the UnejDigiLib service's performance. Meanwhile, the IPA analysis's findings indicate that the following indicators are found in quadrant 1: C3 (completeness of the book collection), C5 (suitability of the book collection with the curriculum), C6 (updates to the book collection), and F3 (download speed). This quadrant's indicators are the primary focus for the improvement. The conclusion from these improvement suggestions is that application service providers must coordinate with stakeholders to complete the e-book collection according to customer needs and also require technical updates starting from the features and internal application system to minimize errors due to the system
Android-Based Wireless Single-Lead Electrocardiogram: Heart Rate Measurement and ECG Signal Visualization
Get PDFHeart rate (HR) is vital for medical and healthcare purposes. This study presents an Android-based heart rate measurement system utilizing a single-lead electrocardiogram (ECG). Three electrodes placed on the arm in lead I configuration capture the ECG signals. An AD8232 sensor amplifies the signal, which is then digitized by Arduino Nano and transmitted to an Android device via HC-05 Bluetooth. The Android application processes the ECG data using the Pan-Tompkins algorithm with an optimized threshold coefficient to extract HR information. The system displays the ECG waveform and the calculated HR on the user interface. Our evaluation demonstrates high accuracy with an error rate of only 0.042%, sensitivity of 99.84%, and positive predictive value of 97.06%. This research suggests the potential of this system for convenient and reliable HR monitoring using readily available smartphones
Security Analysis of Web-based Academic Information System using OWASP Framework
Get PDFThe Academic Information System plays a crucial role in efficiently managing student, faculty, and campus administration data. However, system security needs to be a primary concern as it is vulnerable to cyber attacks. This research aims to analyze the security of the Academic Information System at the Muhammadiyah Business Institute Bekasi. The research method used is a comprehensive security analysis based on the OWASP framework. The study includes identifying potential vulnerabilities, penetration testing, and system improvement recommendations. Testing is conducted through simulated attacks based on the OWASP-released security risk list (OWASP Top Ten Most Critical Web Application Security Risks). The analysis results indicate that the system is vulnerable to Broken Authentication due to weak passwords, Sensitive Data Exposure due to URLs pointing to direct directories, and Security Misconfiguration due to open protocols. Furthermore, in CVSS scoring, Broken Authentication scored 4.8 (Medium), Sensitive Data Exposure and Security Misconfiguration scored 5.3 (Medium), Cross-Site Scripting scored 2.0 (Low) and Using Component with Known Vulnerabilities scored 2.0 (Low), while SQL Injection, XXE, Broken Access Control, Insecure Deserialization, and Insufficient Logging and Monitoring scored 0.0 (No Vulnerability). Recommendations for future system improvements include regularly updating the system to prevent new security vulnerabilities, better server configurations, and routine system monitoring to promptly anticipate suspicious activities
Classification of Arrhythmic and Normal Signals using Continuous Wavelet Transform (CWT) and Long Short-Term Memory (LSTM)
Get PDFAn electrocardiogram (ECG) can detect heart abnormalities through signals from the rhythm of the human heartbeat. One of them is arrhythmia disease, which is caused by an improper heartbeat and causes failure of blood pumping. In reading ECG signals, a common problem encountered is the uncertainty of the prediction results. An accurate and efficient heart defect classification system is needed to help patients and healthcare providers carry out appropriate therapy or treatment. Several studies have developed algorithms that are more effective in Machine Learning (ML) in automatically providing initial screening of patients' heart conditions. This study proposed the Long Short-Term Memory (LSTM) method as a classifier of heart conditions experienced by humans and Continuous Wavelet Transform (CWT) as a feature extractor to eliminate noise during data collection. CWT and LSTM methods are believed to perform well in feature extraction and classification of ECG signals. The dataset used in this study was taken from the MIT-BIH Arrhythmia Database. The results of this study successfully extracted ECG signals using CWT, thus improving the understanding of ECG characteristics. This research also succeeded in classifying ECG signals using the LSTM method, which obtained an accuracy training value of 98.4% and an accuracy testing value of 94.42 %
Performance Comparison between Double Exponential Smoothing and Double Moving Average Methods in Seasonal Beef Demand
Get PDFBeef demand relies on seasonal patterns because it depends on feed supplies, especially in the rural areas, that still rely on natural feeds. Beef supply is regulated by the government as it is one of the highly demanded commodities. It is a livestock product containing nutritional value to meet the protein needs of the community. The supply is influenced by several factors such as beef production, beef consumption, and the people's income level. In order to anticipate the increasing demand for beef, it is necessary to conduct a forecast to estimate the demand for meat in the future. In forecasting, various methods were examined to choose the method with the lowest error rate. This research compared the Mean Absolute Percentage Error (MAPE) resulted from Double Exponential Smoothing (DES) and Double Moving Average (DMA) methods. Based on the test results and analysis on beef supplies in Madura, it can be concluded that the method with the lowest MAPE value is Double Exponential Smoothing, i.e. 9.50% with an alpha parameter of 0.5. Meanwhile, the test using the Double Moving Average method to determine the best MAPE value, resulted the best time order of 2 with a MAPE value of 29.8408%. After finding the parameter with the lowest MAPE value, that parameter was used for the data testing. In the measurement, the data used for the testing were the data of 1-year, 2-year, 3-year, and 4-year period. Each method has a level of error value that increases the same; the number of data entered can affect the MAPE value. Therefore, the more data entered, the lower the error value
Optimizing Android Program Malware Classification Using GridSearchCV Optimized Random Forest
Get PDFThe growing number of smartphones, particularly Android powered ones, has increased public awareness of the security concerns posed by malware and viruses. While machine learning models have been studied for malware prediction in this field, methods for precise identification and classification still require improvement for the perfect detection of malwares and minimizing the cracks on machine learning based classification. Detection accuracy that ranges from 93% to 95% has been observed in prior research, indicates room for improvement. In order to maximize the hyperparameters, this paper suggests improving the Random Forest method by introducing the grid search algorithm which isn’t present in previous studies. A significant increase in classification accuracy is the main aim of the research. We exhibit an outstanding 99% accuracy rate in detecting malware contaminated programs, demonstrating the significance of our technique. The proposed method can be seen as a huge improvement over existing models, achieving near perfection in detection, in contrast to which typically obtained by previous models with the accuracy rate of 95% max on the same dataset. Our approach achieves such high accuracy and provides a novel remedy for the limits of the Android based platforms, particularly when program processing resources are limited. This study confirms the effectiveness of our improved Random Forest algorithm, points to a paradigm shift in malware detection, and heightened cybersecurity measures for the rapidly growing smartphone market
A Super Encryption Approach for Enhancing Digital Security using Column Transposition - Hill Cipher for 3D Image Protection
Get PDFImage encryption is an indispensable technique in the realm of information security, serving as a pivotal mechanism to safeguard visual data against unauthorized access and potential breaches. This study scrutinizes the effectiveness of merging columnar transposition with the Hill Cipher methodologies, unveiling specific metrics from a curated set of sample images. Notably, employing column transposition with the key "JAYA" and the Hill Cipher with the key "UDINUSSMG," the encrypted images underwent rigorous evaluation. 'Lena.png' demonstrated an MSE of 513.32 with a PSNR of 7.89 dB, while 'Peppers.png' and 'Baboon.png' recorded MSE values of 466.67 and 423.92, respectively, with corresponding PSNR figures of 7.12 dB and 7.31 dB. Across all samples, a consistent BER of 50.00% indicated uniform error propagation, while entropy values settled uniformly at 7.9999, highlighting consistent data complexity. While the findings underscore a consistent error rate and complexity, there's a compelling need for further refinement to enhance image quality and security. Moreover, the study proposes future research avenues exploring a three-layer super encryption paradigm, amalgamating columnar transposition, Hill Cipher, and other robust algorithms. This approach aims to fortify encryption methodologies against evolving threats and challenges in data protection, offering heightened resilience and efficacy in safeguarding sensitive information
Exploring the Impact of Octalysis Gamification in Japanese M-learning Using the Technology Acceptance Model
Get PDFIndonesia is a country with the second highest number of Japanese language learners in the world. However, with the main language of Indonesia being derived from the roman alphabets, it makes Indonesian students hard to get used to learning Japanese alphabets, especially Kanji. This study aims to develop a gamified mobile learning application following the Octalysis gamification framework, and assess its impact in garnering student’s acceptance to enhance their Japanese Kanji learning experience. This study was conducted quantitatively using the Technology Acceptance Model, and analyzed through the Structural Equation Model. The data were collected via questionnaires from 194 members of the local Japanese learning community. The variables analyzed in this research are Perceived Usefulness, Perceived Ease of Use, Attitude Towards Using, and Behavioral Intention. All variables are tested for validity and reliability using SPSS Statistics, and structural equation model analysis using SPSS AMOS. The results showed positive significant correlations between Perceived Usefulness and Attitude Towards Using, Perceived Ease of Use and Attitude Towards Using, and Attitude Towards Using and Behavioral Intention. The result also noted a negative correlation between Perceived Usefulness and Behavioral Intention. Each variable contributes to the acceptance of the gamified mobile learning application with a strong emphasis on Perceived Ease of Use, and a mild emphasis on Perceived Usefulness
Multi-Label Classification of Indonesian Qur'an Translation using Long Short-Term Memory Model
Get PDFStudying the Quran is an integral act of worship in Islam, necessitating a nuanced comprehension of its verses to ease learning and referencing. Recognizing the diverse thematic elements within each verse, this research pioneers in applying Deep Learning techniques, specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM), coupled with Word Embedding methods like Word2Vec and FastText, to refine the multi-label classification of the Quran's translations into Indonesian. Targeting core thematic categories such as Tawheed, Worship, Akhlaq, and History, the study aims to elevate classification accuracy, thereby enhancing the textual understanding and educational utility of the Quran's teachings. The employment of Bi-LSTM in conjunction with FastText and meticulous hyperparameter optimization has yielded promising results, achieving an accuracy of 71.63%, precision of 64.06%, recall of 63.60%, and a hamming loss of 36.17%. These outcomes represent a significant advancement in the computational analysis of religious texts, offering novel insights into the complex domain of Quranic studies. Furthermore, the research accentuates the critical role of selecting suitable word embedding techniques and the necessity of precise parameter adjustments to amplify model performance, thereby contributing to the broader field of religious text analysis and understanding. Through such computational approaches, this study not only fosters a deeper appreciation of the Quran's multifaceted teachings but also sets a new precedent for the interdisciplinary integration of Islamic studies and artificial intelligence