JOIV : International Journal on Informatics Visualization
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Development of IoT Control System Prototype for Flood Prevention in Bandung Area
Get PDFBandung is one of the areas with high rainfall that can increase the volume of river water, which, if not handled properly, has the potential for significant floods that can cause material damage and loss of life. With this problem, the authors' rationale for designing a control system for flood prevention. This system develops prototypes using Internet of Things technology and fuzzy logic. For Internet of Things technology, the author uses Arduino, which controls sensors and actuators, while Raspberry Pi is used to process data. In addition, the author uses ultrasonic sensors to measure the water level and a water pump to control the water level. So, if the water level exceeds the specified limit, the pump will move the water to another place, in this prototype, using an aquarium. For fuzzy logic, the criteria used are dry, filled, and full. In addition, this system is equipped with a website-based dashboard used to monitor real-time data from the sensor. The results of this study indicate the system is running well, with an average error of 32.2%. This indicates that the system has been well designed because the errors obtained are feasible to be minor, although there are several influencing factors, such as prototype construction and sensor readings. Thus, this prototype can be applied as a reference for making a real system for flood control
Saudi Learners' Perception of Infographics in Education: A Survey
Get PDFLearners' learning experiences diverge and undergo rapid shifts due to trends in technology, and the transfer of knowledge in varying formats and styles is ubiquitous. Infographics find its application in instructional design and technology that drives education with state-of-the-art tools and applied methods. Being a format of data, Infographics or visual images presents information or knowledge using visuals. This study aims to present the student perceptions of learning via infographics at Princess Nourah bint Abdurrahman University (PNU) in Saudi Arabia. The study employs a survey questionnaire consisting of 13 close-ended questions posed to assess the learners' responses at PNU. Applied Likert's scale-based questions present a conversion of user data input to quantitative figures, which present the level of understanding and the role of infographics in education. The survey involved 45 undergraduate female students pursuing undergraduate degree courses at PNU. Using survey research design methodology, the study investigated learners' perceptions of infographics to add to their learning experiences and provides a quantitative analysis of observed responses to the survey questionnaire. The study conducts an online survey and classifies participants of different age groups into five categories for assessment. The study findings reveal that PNU learners perceive a positive role of infographics in their learning. However, learners showcase varied perceptions of a) the acceptance of assignments based on infographics and b) the use of static versus animated infographics. The study guides research scholars toward the intuition of infographics in learning environments and reports the two research problems to be addressed in future works
Literature Reviews of RBV and KBV Theories Reimagined - A Technological Approach Using Text Analysis and Power BI Visualization
Get PDFOver the years, the Resource-Based View (RBV) and Knowledge-Based View (KBV) have solidified their roles as pivotal paradigms in strategic management literature. With an emphasis on Small and Medium Enterprises (SMEs), this study uses text analysis and Microsoft Power BI to explore these concepts innovatively. The study implements a systematic literature review, extracting data from Scopus, Web of Science, and DOAJ databases to assemble a comprehensive literature corpus. The methodology incorporates text analysis to draw out key themes, relationships, and trends, and these are subsequently visualized using Power BI to create an engaging, interactive representation of data. Components like word clouds, co-occurrence networks, and trend lines are generated, while Power BI's dynamic filtering and drill-down functionalities facilitate thorough data investigation. The results display significant overlap between RBV and KBV, denoting possible integration junctures for these theories within the domain of strategic management. Additionally, the study underscores the relevance of these insights for SMEs, emphasizing the part played by unique resources, encompassing knowledge assets, in catalyzing innovation and fostering a competitive edge. The study concludes by recognizing the significant theoretical and practical implications of integrating text analysis and Power BI in conducting literature reviews. This methodology bolsters our understanding of RBV and KBV, offering small and medium-sized enterprises a beneficial instrument to traverse these intricate theories. The study suggests that future research could broaden the application of this methodological approach to encompass other strategic management theories
Comparative Analysis of VGG-16 and ResNet-50 for Occluded Ear Recognition
Get PDFOccluded ear recognition is a challenging task in biometric systems due to the presence of occlusions that can hinder accurate identification. There is still a research gap in enhancing the robustness of deep learning to handle severities of occlusions with different datasets. This research focuses on developing a robust occluded ear recognition system by implementing fine-tuning techniques on three popular pre-trained deep learning models, Residual Neural Network (ResNet-50), Visual Geometry Group (VGG-16), and EfficientNet. The system is evaluated on two manually occluded ear datasets, which are the AMI ear dataset and the IITD ear dataset. The experiment results showed the fine-tuned ResNet-50 model performs better than the fine-tuned VGG-16 model. The results indicate that the model's ability to accurately predict the classes or labels decreases as more data is occluded. Higher occlusion rates lead to a loss of important information, making it more challenging for the model to distinguish between different patterns and make accurate predictions. According to the findings, the amount of occlusion influenced the identification accuracy and worsened as the occlusion became larger. In the future, ear recognition systems will likely continue to improve in accuracy and be adopted by a wider range of organizations and industries. They may also be integrated with other biometric technologies and used for personalization purposes. However, ethical considerations related to the use of ear recognition systems will also need to be addressed
Mel Frequency Cepstral Coefficients (MFCC) Method and Multiple Adaline Neural Network Model for Speaker Identification
Get PDFSpeech recognition technology makes human contact with the computer more accessible. There are two phases in the speaker recognition process: capturing or extracting voice features and identifying the speaker's voice pattern based on the voice characteristics of each speaker. Speakers consist of men and women. Their voices are recorded and stored in a computer database. Mel Frequency Cepstrum Coefficients (MFCC) are used at the voice extraction stage with a characteristic coefficient of 13. MFCC is based on variations in the response of the human ear's critical range to frequencies (linear and logarithmic). The sound frame is converted to Mel frequency and processed with several triangular filters to get the cepstrum coefficient. Meanwhile, at the speech pattern recognition stage, the speaker uses an artificial neural network (ANN) Madaline model (many Adaline/ which is the plural form of Adaline) to compare the test sound characteristics. The training voice's features have been inputted as training data. The Madaline Neural Network training is BFGS Quasi-Newton Backpropagation with a goal parameter of 0,0001. The results obtained from the study prove that the Madaline model of artificial neural networks is not recommended for identification research. The results showed that the database's speech recognition rate reached 61% for ten tests. The test outside the database was rejected by only 14%, and 84% refused testing outside the database with different words from the training data. The results of this model can be used as a reference for creating an Android-based real-time system
K-Means Algorithm Analysis for Election Cluster Prediction
Get PDFThe general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, it is very important to identify clusters of general election districts and map the number of voters to map the budget for the upcoming election. This process needs prediction to help reduce budgeting risk as an early warning. Based on the latest election data taken from Margokaton, Yogyakarta, Indonesia, many people voted in 2021, but the number of abstainers is high. In this case, cluster prediction is important to identify the election participants in each area. The K-Means algorithm could also predict abstainer areas in election activities to facilitate early mitigation in drafting election budgeting. Therefore, this study aimed to identify the pattern of voters in the election using the K-means algorithm. The data parameters comprised the list of voters, Unused ballot papers, and the sum of abstainers. This study is important because it contributes to reducing the election budget of each area. The data obtained from the Indonesia Ministry of Internal Affairs official website in 2021 were processed using the RapidMiner tool. The results showed more than 11% of the non-voters in cluster 1, 16% in Cluster 2, and 8% in cluster 3. The evaluation of clusters value is 2.04, indicating that the clustering using K-means is suitable, as shown by the DBI value close to 0. The results indicate that testing the cluster optimization of the K-Means algorithm using DBI is highly recommended. Based on this prediction result, the government needs special attention to clusters with many abstainers to decrease the number of abstainers and prevent overbudgeting. These results indicate the need to review the election participant data in 2024. Furthermore, there is a need for continuous socialization and education about election activities to reduce the number of abstainers and prevent overbudgeting
Selecting Control Menu on Electric Wheelchair Using Eyeball Movement for Difable Person
Get PDFEach country's number of people with disabilities and strokes increases yearly. Hand defects and stroke make them have limitations in doing activities. It caused their hand has paralyzed. Hence, they find it difficult to do daily activities, such as running a wheelchair, choosing a menu on the screen display, and so on. One solution offered is utilizing eye movement as a navigation tool that can replace the role of the user's hand, so they can run a wheelchair independently or choose a menu selection on display by themselves through the movement of their eyes. Detection of eyeball movements in this study only utilizes a camera as a sensor mounted in front of the user. So that it is more practical and easier to use than if we have to pair an electrooculography sensor in the area around the user's eyes. This research proposed a new approach to detect the five gazes (upward, downward, leftward, rightward, and forward) of the eyeball movements by using Backpropagation Neural Network (BPNN) and Dynamic Line Sector Coordinate (DLSC). Line Sector Coordinate is used to detect the eyeball movement based on the pupil coordinate position. The eyeball movement direction was analyzed from four lengths of a line. Our proposed method can detect five gaze directions that can be used for selecting four menus on the display monitor. The mean accuracy of our proposed method to detect eye movements for each gaze is 88.6%
Voice-Authentication Model Based on Deep Learning for Cloud Environment
Get PDFCloud computing is becoming an essential technology for many organizations that are dynamically scalable and employ virtualized resources as a service done over the Internet. The security and privacy of the data stored in the cloud is cloud providers' main target. Every person wants to keep his data safe and store it in a secure place. The user considers cloud storage the best option to keep his data confidential without losing it. Authentication in the trusted cloud environment allows making knowledgeable authorization decisions for access to the protected individual's data. Voice authentication, also known as voice biometrics, depends on an individual's unique voice patterns for identification to access personal and sensitive data. The essential principle for voice authentication is that every person's voice differs in tone, pitch, and volume, which is adequate to make it uniquely distinguishable. This paper uses voice metric as an identifier to determine the authorized customers that can access the data in a cloud environment without risk. The Convolution Neural Network (CNN) architecture is proposed for identifying and classifying authorized and unauthorized people based on voice features. In addition, the 3DES algorithm is used to protect the voice features during the transfer between the client and cloud sides. In the testing, the experimental results of the proposed model achieve a high level of accuracy, reaching about 98%, and encryption efficiency metrics prove the proposed model's robustness against intended attacks to obtain the data
Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis
Get PDFAutism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%
Security Awareness Strategy for Phishing Email Scams: A Case Study One of a Company in Singapore
Get PDFSocial Engineering Procedures and phishing are some of the standard procedures and problems today, mainly through sophisticated media such as email, the official means of communication companies use. Phishing emails are usually associated with Social Designing. They can be sent via joins and connections in this email, but they are not secure. Proliferation can be hacked into private/confidential data or total control over the computer/Email without the client's knowledge. The method used in this research is a cycle that will run continuously in a life cycle, starting from problem identification, then generating ideas and evaluating the Implementation of solutions. At each stage, a thorough checking process is needed to obtain results. Follow what you want. Achieved. The results of this study provide recommendations and some suggestions that companies can make; this aims to be one of the doors that provides restrictions for access from parties who are not entitled to access the application. Some thought has shown that this attack is growing and affecting the population. The evaluation stages in this study consist of 5 phases. Each phase is a step used to prevent both the system and the behavior in the company. Awareness is critical at the start considering this is the basis for the organization to determine who will take care of the personnel's knowledge related to information security. It thinks about using survey writing strategies and recommendations that can be made in anticipation of an attack, such as setting up representation or attention as early and often as possible