JOIV : International Journal on Informatics Visualization
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    476 research outputs found

    Classifying Gender Based on Face Images Using Vision Transformer

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    Due to various factors that cause visual alterations in the collected facial images, gender classification based on image processing continues to be a performance challenge for classifier models. The Vision Transformer model is used in this study to suggest a technique for identifying a person’s gender from their face images. This study investigates how well a facial image-based model can distinguish between male and female genders. It also investigates the rarely discussed performance on the variation and complexity of data caused by differences in racial and age groups. We trained on the AFAD dataset and then carried out same-dataset and cross-dataset evaluations, the latter of which considers the UTKFace dataset.  From the experiments and analysis in the same-dataset evaluation, the highest validation accuracy of  happens for the image of size  pixels with eight patches. In comparison, the highest testing accuracy of  occurs for the image of size  pixels with  patches. Moreover, the experiments and analysis in the cross-dataset evaluation show that the model works optimally for the image size  pixels with  patches, with the value of the model’s accuracy, precision, recall, and F1-score being , , , and , respectively. Furthermore, the misclassification analysis shows that the model works optimally in classifying the gender of people between 21-70 years old. The findings of this study can serve as a baseline for conducting further analysis of the effectiveness of gender classifier models considering various physical factors

    Predicting Battery Storage of Residential PV Using Long Short-Term Memory

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    Solar power panels, or photovoltaic (PV), have recently grown rapidly as a renewable alternative energy source, especially since the increase in the basic electricity tariff. PV technology can be employed instead of the state electricity company to reduce the electricity used. Indonesia is one of the countries that have great potential in producing electricity from PV technology, considering that most of Indonesia's territory gets sunlight for most of the year and has a large land area. Considering the benefits of PV technology, it is necessary to carry out predictive monitoring and analysis of the energy generated by PV technology to maximize energy utilization in the future. The Internet of Things (IoT) and cloud computing system was developed in this research to monitor and collect data in real-time within 27 days and obtained 7831 data for each parameter that affects PV production. These data include data on the light intensity, temperature, and humidity at the location where the PV system is installed. The feature selection results using Pearson correlation revealed that the light intensity parameter significantly impacted the PV production system. This research used the Long Short-Term Memory (LSTM) method to predict future PV production. By tuning hyperparameters using 3000 epochs, the resulting RMSE value was 171.5720. The results indicated a significant change in the RMSE value compared to 100 epochs of 422.5780. This model can be applied as a forecasting system model at electric vehicle charging stations, given the increasing use of electric vehicles in the future.     Keywords— Forecasting; energy; Photovoltaic; LSTM; Internet of Thing.Â

    Solar Powered Vibration Propagation Analysis System using nRF24l01 based WSN and FRBR

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    Prevention of the effects caused by natural disasters such as earthquakes and landslides requires analysis of vibration propagation. In outdoor applications, internet sources such as WIFI are not always available, so it requires alternative data communications such as nRF24l01. The system also requires a portable power source such as solar power. This research aims to develop a vibration propagation analysis system based on the nRF24l01 wireless sensor network and solar power by implementing the fuzzy rule-based regression (FRBR) algorithm. The system consists of two piezoelectric and nrf24l01 vibration sensors. The system also uses a third node equipped with temperature and soil moisture sensors, air temperature and humidity, and light intensity as environmental variables. The evaluation results show the Quality of Services (QoS) results with a throughput of 99.564%, PDR 99.675%, and a delay of 0.0073s. The Fuzzy Association Rule (FAR) extraction results yield nine rules with average support of 0.319 and confidence of 1 for vibration propagation. The availability of solar power was evaluated with an average current value of 0.250A and a voltage of 3.266V. The results of FRBR are based on the propagation of the vibration that propagated and produced a mean square error (MSE) of 0.141 and a mean absolute error (MAE) of 0.165. The correlation matrix and FAR results show that only soil moisture has a major effect on the magnitude and duration of propagation. However, other variables can regress soil moisture with MSE 0.232 and MAE 0.287

    Player's Affective States as Meta AI Design on Augmented Reality Games

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    Games are considered one of the most popular entertainment forms worldwide. The interaction in the game environment makes the players addicted to playing the game. One technique to build an addicting game is utilizing the player's emotions using Meta Artificial Intelligence (AI). The player's emotions can be utilized by adjusting the game difficulty. Most of the game offers static and steady difficulty development throughout the game. This research proposes a Meta AI game design using the player's affective states. We argue that a dynamic difficulty development throughout the game will increase the player's game experiences. The player's facial expressions are utilized to extract the player's affective state information. To recognize the player's facial expressions, a Facial Expressions Recognition (FER) model was trained using VGG-16 architecture and The Indonesian Mixed Emotion Dataset (IMED) dataset in addition to a self-collected dataset. The emotions recognition model (from player's facial expressions) achieved the best validation accuracy of 99.98%. The model was implemented in the proposed Meta AI game design. The Meta AI game design proposed in this game was implemented in several game scenarios to be compared and evaluated. The proposed Meta AI game design was evaluated by 31 respondents using Game Experiences Questionnaire (GEQ). Overall, the results show that the game with Meta AI and Augmented Reality implemented significantly improved the Game Experiences Questionnaire (GEQ) score and the player's overall satisfaction compared to the other game scenarios

    An Overview Diversity Framework for Internet of Things (IoT) Forensic Investigation

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    The increasing utilization of IoT technology in various fields creates opportunities and risks for investigating all cybercrimes. At the same time, many research studies have concentrated on security and forensic investigations to collect digital evidence on IoT devices. However, until now, the IoT platform has not fully evolved to adjust the tools, methods, and procedures of IoT forensic investigations. The main reasons for investigators are the characteristics and infrastructure of IoT devices. For example, device number variations, heterogeneity, distribution of protocols used, data duplication, complexity, limited memory, etc. As a result, resulting is a tough challenge to identify, collect, examine, analyze, and present potential IoT digital evidence for forensic investigative processes effectively and efficiently. Indeed, there is not fully used and adapted international standard for the perfect IoT forensic investigation framework. In the research method, a literature review has been carried out by producing previous research studies that have contributed to further facing challenges. To keep the quality of the literature review, research questions (RQ) were conducted for all studies related to the IoT forensic investigation framework between 2015-2022. This research results highlight and provides a comprehensive overview of the twenty current IoT forensic investigation framework that has been proposed. Then, a summary or contribution is presented focusing on the latest research, grouping the forensic phases, and evaluating essential frameworks in the IoT forensic investigation process to obtain digital evidence. Finally, open research issues are presented for further research in developing IoT forensic investigative framework

    A Rule-based Mobile Application for Diagnosing Pet Disease: Design and Implementation

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    Animals kept in homes for personal enjoyment rather than for work or sustenance are typically referred to as "pets." A pet's daily schedule can include exercising its muscles and going outside to relieve stress. Pets may occasionally be drink from community water dishes that could be contaminated with other animals' bacteria, viruses, or parasites. The pets may unknowingly get infections due to this opening up their bodies to bacteria or viruses. Pet's behavior and condition need to be periodically checked. An animal's behavior is directly impacted by its health and vice versa. A pet disease diagnosis application is crucial for pet owners to receive consistent and suitable pet health care. It will help pet owners identify potential illnesses before their animals develop chronic ones. Thus, the construction of a mobile application for diagnosing pet diseases is presented in this paper. This application offers pet owners information on their animals' health and safety. Pet owners can contact veterinarians for rare cases or crises in this application's chat room. The rule-based inference is used to determine the possible diseases based on the pet's symptoms. System prototyping methodology is applied to develop this Android mobile application using Visual Studio Code and Firebase database. User acceptance testing is performed on the users to test how much further their satisfaction with the proposed pet disease diagnosis application is before the application is shifted to the production process. Â

    Evaluation of the Visual Learning Application for Mathematics using Holography Display for the topic on Shape and Space

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    Mathematics is an important foundation in the life of an individual. The problems associated with learning Mathematics that are commonly encountered, are due to factors such as: abstract phenomena and concepts, low imagination, and lack of understanding of the concepts being studied. Thus, the purpose of this study is to help primary school children improve their ability to recognise 3D shapes that are abstract phenomena. This paper presents the Effectiveness Usability Evaluation of the Visual Learning Application for Mathematics using Holography Display for the topic on Shape and Space called MEL-VIS. This study was conducted on eighty (80) primary school students. The results of the study showed that learning about 3D shapes with the E-Visual MEL-VIS application prototype is more effective than traditional methods

    Optimizing Pigeon-Inspired Algorithm to Enhance Intrusion Detection System Performance Internet of Things Environments

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    Intrusion Detection Systems (IDS) are crucial in maintaining network security and safeguarding sensitive information against external and internal threats. This study proposes a novel approach by utilizing a Pigeon-Inspired Algorithm optimized with the Hyperbolic Tangent Function (Tanh) function to enhance the performance of IDS in threat detection specifically tailored for Internet of Things (IoT) environments. We aim to create a more robust solution for optimizing intrusion detection systems by integrating the efficient and effective Tanh function into the Pigeon-Inspired Algorithm. The proposed method is evaluated on three widely-used datasets in the field of IDS: NSL-KDD, CICIDS2017, and CSE-CIC-IDS2018. Experimental results demonstrate that integrating the Tanh function into the Pigeon-Inspired Algorithm significantly improves the performance of the intrusion detection system. Our method achieves higher accuracy, True Positive Rate (TPR), and F1-score while reducing the False Positive Rate (FPR) compared to traditional Pigeon-Inspired Algorithms and several other optimization algorithms. The Pigeon-Inspired Algorithm optimized with the Tanh function offers an efficient and effective solution for enhancing intrusion detection system performance, specifically in Internet of Things environments. This method holds great potential for application in diverse network environments, bolstering information security and safeguarding systems from evolving cybersecurity threats. By extending the applicability and effectiveness of the Pigeon-Inspired Algorithm optimized with the Tanh function, researchers can contribute to developing more comprehensive and robust security solutions, addressing the ever-evolving landscape of IoT-based cybersecurity threats

    Predicting Factors that Affect East Asian Students’ Reading Proficiency in PISA

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    Teachers, schools, and parents contribute to equipping students with essential knowledge and skills during their education years. When students are approaching the end of their education, they are randomly selected to participate in Program for International Student Assessment (PISA) to assess their reading proficiency. Existing work on analyzing PISA achievement results concentrates solely on identifying factors related to Parent or in combination with Student. Limited work has been proposed on how factors related to Teacher and School affect the students’ reading proficiency in PISA. This study focuses on identifying the factors related to Teacher and/or School that affect East Asian students’ reading proficiency in PISA. The PISA achievement results from East Asian students are chosen as the domain study because they are consistently the top performers in PISA in the past decade. Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbors (KNN) and Random Forest (RF) are compared. Hamming score is used as the evaluation metric. The results indicate that RF produces the best predictive models with highest Hamming score of 0.8427. Based on the findings, School-related factors such as the number of school’s disciplinary cases, size of the school, the availability of computers with Internet facilities, the quality and educational qualifications of teachers have higher impact on the PISA achievement results. The identified factors can be used as a reference in assessing the current school’s teaching, learning environment, and organizing extra activities as part of intervention programs to cultivate reading habits and enhance reading abilities among students

    Introversion-Extraversion Prediction using Machine Learning

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    Introversion and extroversion are personality traits that assess the type of interaction between people and others. Introversion and extraversion have their advantages and disadvantages. Knowing their personality, people can utilize these advantages and disadvantages for their benefit. This study compares and evaluates several machine learning models and dataset balancing methods to predict the introversion-extraversion personality based on the survey result conducted by Open-Source Psychometrics Project. The dataset was balanced using three balancing methods, and fifteen questions were chosen as the features based on their correlations with the personality self-identification result. The dataset was used to train several supervised machine-learning models. The best model for the Synthetic Minority Oversampling (SMOTE), Adaptive Synthesis Sampling (ADASYN), and Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) datasets was the Random Forest with the 10-fold cross-validation accuracy of 95.5%, 95.3%, and 71.0%. On the original dataset, the best model was Support Vector Machine, with a 10-fold cross-validation accuracy of 73.5%. Based on the results, the best balancing methods to increase the models’ performance were oversampling. Conversely, the hybrid method of oversampling-undersampling did not significantly increase performance. Furthermore, the tree-like models, like Random Forest and Decision Tree, improved performance substantially from the data balancing. In contrast, the other models, excluding the SVM, did not show a significant rise in performance. This research implies that further study is needed on the hybrid balancing method and another classification model to improve personality classification performance


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