JOIV : International Journal on Informatics Visualization
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The Best Malaysian Airline Companies Visualization through Bilingual Twitter Sentiment Analysis: A Machine Learning Classification
Get PDFOnline reviews are crucial for business growth and customer satisfaction. There is no exception for the airlines’ company, which places third as the biggest contributor to Malaysia’s Gross Domestic Product. Customer opinions play an important role in maintaining the reputation and improving the quality of service of the airlines. However, there is no specific platform for online review. Most online ratings obtain English, leading to inaccurate results as not all reviews regarding different languages are considered. Airlines currently have no specific platform for online reviews despite being critical for business growth, performance, and customer experience improvement. Hence, this paper proposed implementing a web-based dashboard to visualize the best Malaysian airline companies. The airline companies involved are AirAsia, Malaysia Airlines, and Malindo Air. We designed and developed the proposed study through the bilingual analysis of Twitter sentiment using the Naïve Bayes algorithm. Naïve Bayes algorithm is a machine learning approach to do classification. The tweets extracted were analyzed as metrics that advance airline companies’ online presence. Testing phases have shown that the classifier successfully classified tweets’ sentiment with 93% accuracy for English and 91% for Bahasa. Every feature in the web-based dashboard functions correctly and visualizes a detailed analysis of sentiment. We applied the System Usability Scale to test the study’s usability and managed to get a score of 94.7%. The acceptability score ‘acceptable’ result concluded that the study reflects a good solution and can assist anyone in understanding the public views on airline companies in Malaysia
Genetic Algorithm for Artificial Neural Networks in Real-Time Strategy Games
Get PDFControlling each member of the soldiers to carry out battle with Non-Playable Characters (NPC) is one of the secrets to winning Real-Time Strategy games. The game could be more complicated and offer a more engaging experience if every NPC acts like humans rather than machines with patterned behavior. Like people during a war, each army member's command requires rapid reflexes and direction to strike or evade attacks. An intelligent opponent based on ANN as NPC can react quickly to their opponents. The accuracy of ANN could be enhanced by weight modifications using a Genetic Algorithm (GA). The crossover and mutation rates significantly impact GA's performance as an ANN setup. This research aims to find the best crossover and mutation rates in GA as a weight adjustment in ANN. Experiments were conducted using an RTS game simulator using 20 scenarios on a maximum of 4000 iterations. The initial setup of each troop is random, with a seven-unit type available. In this research, the troops won because their men were subjected to fewer attacks than the opposing forces. The GA optimal crossover and mutation rates are determined using troop victories as a baseline. According to the findings, the best crossover rate for GA as an ANN weight adjustment is 0.6, whereas the specific mutation rate is 0.09. The crossover rate of 0.6 has the highest average win value and tends to increase every generation. As for the mutation rate of 0.09, it has the highest average win value. Thus, this preliminary study can develop NPC more humanly
A Real-Time Application for Road Conditions Detection based on the Internet of Things
Get PDFBad road conditions may lead to road accidents, especially when drivers are unaware of potholes. The presence of potholes can increase from time to time and may get worse due to road age and bad weather. With the Internet of Things technology, vehicles on the road can be a means of collecting road condition data, such as vibration. The raw vibration data are useful only after they are processed into meaningful information. Information about the condition of roads can help other road users be aware of potholes. This paper proposes an Internet of Things application for road conditions detection. We design and implement a device comprising one NodeMCU ESP8266, one accelerometer gyroscope sensor to detect the existence of potholes based on the amount of detected vibration, and a GPS module to get the information about potholes' locations. For the web service, we use REST API so that users can get real-time potholes' information in the Android application. To cluster potholes based on detected vibration, i.e., deep, medium, and shallow, we implement the k-means clustering algorithm with k = 3. The Android application utilizes a Google map to visualize potholes' locations and the result of clustering on a road map. We use colored pins to indicate the depth of potholes. Deep potholes are shown on the map using red pins, medium potholes using orange pins, and shallow potholes using green pins
The VaccineLand: An Interactive Digital Board Game to Educate Public about Vaccines
Get PDFThe invention of the COVID-19 vaccines has resulted in various viewpoints and reactions from the public. The vaccines are guaranteed to be safe and clinically proven to enhance human antibodies preventing the virus from spreading. However, disseminating facts and truth about vaccines to the public is challenging due to many factors, including vaccine hesitancy and skepticism. Besides, some individuals are still concerned about vaccines' adverse effects and safety. Plus, the presence of various misleading online discussions promoted by anti-vaccine activists that spread false information about vaccines has worsened the situation. Therefore, in this paper, we proposed a digital board game, the VaccineLand, that provides information about vaccines in terms of their history, reasons for taking them, and the adverse effects on the public. Thus, VaccineLand was established based on the Game-Based Learning (GBL) model by its goal, which is to provide the public with facts and knowledge about vaccines in an interactive way. GBL is known as a type of gameplay with specific learning outcomes. Interactive GBL corresponds to the use of educational game applications for learning. This study investigates a usability evaluation consisting of four variables (usefulness, ease of use, ease of learning, and satisfaction) involving a total of 40 respondents. The finding indicates that the usability of this game was highly acceptable. We believe that VaccineLand can be a helpful tool to educate the public about vaccine
Identification of Mirai Botnet in IoT Environment through Denial-of-Service Attacks for Early Warning System
Get PDFThe development of computing technology in increasing the accessibility and agility of daily activities currently uses the Internet of Things (IoT). Over time, the increasing number of IoT device users impacts access and delivery of valuable data. This is the primary goal of cybercriminals to operate malicious software. In addition to the positive impact of using technology, it is also a negative impact that creates new problems in security attacks and cybercrimes. One of the most dangerous cyberattacks in the IoT environment is the Mirai botnet malware. The malware turns the user's device into a botnet to carry out Distributed Denial of Service (DDoS) attacks on other devices, which is undoubtedly very dangerous. Therefore, this study proposes a k-nearest neighbor algorithm to classify Mirai malware-type DDOS attacks on IoT device environments. The malware classification process was carried out using rapid miner machine learning by conducting four experiments using SYN, ACK, UDP, and UDPlain attack types. The classification results from selecting five parameters with the highest activity when the device is attacked. In order for these five parameters to be a reference in the event of a malware attack starting in the IoT environment, the results of the classification have implications for further research. In the future, it can be used as a reference in making an early warning innovative system as an early warning in the event of a Mirai botnet attack
Convolutional Neural Network featuring VGG-16 Model for Glioma Classification
Get PDFMagnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%.Â
Iris Image Watermarking Technique for Security and Manipulation Reveal
Get PDFProviding security while storing or sharing iris images has been considered as an interesting research topic and accordingly different iris image watermarking techniques have been presented. Most of the available techniques have been presented to ensure the attachment of the secret data to their related iris images or to hide a logo which can be used for copyright purposes. The previous security techniques can successfully meet their aims; however, they cannot reveal the manipulations in the iris region. This paper presents an iris image watermarking technique that can provide security and reveal manipulations in the iris region. At the sender side, the proposed technique divides the image into two regions (i.e., iris region and non-iris region) and generates the manipulation reveal data from the iris region then embeds it in the non-iris region. At the receiver side, the secret data is extracted from the non-iris region and compared with calculated data from the iris region to reveal manipulations if exist. Different experiments have been conducted to evaluate the performance of the proposed technique which proved its efficiency not only in providing security but also in revealing any manipulations in the iris region
The Development of Cellular Automata-based Entrepreneurial Growth Simulator
Get PDFEntrepreneurship plays an essential role in the economic growth of a country. These roles include creating jobs, reducing unemployment, increasing people's income, combining production factors (nature, labor, capital, and expertise), and increasing national productivity. For the economy to thrive and healthy, it requires at least 4% of the population who work as entrepreneurs. Due to this vital role, entrepreneurial growth must be maintained. One of the efforts to do this is by monitoring growth directly and continuously. Besides that, another way is to do a simulation. By knowing the condition of entrepreneurship at one time and all the factors that affect entrepreneurial growth, simulations can be carried out to determine or predict future conditions. Based on this simulation, essential steps can be taken, or policies can be made to maintain profitable entrepreneurial growth. This paper presents a mathematical model that can simulate and visualize entrepreneurship's growth in six provinces of Sumatra Island, Indonesia. This mathematical model uses cellular automata as its basis and is called Entrepreneurial Cellular Automata (ECA). One of the advantages of Cellular Automata is that it is easy to visualize. The entrepreneurial model used as a reference is a model from the Global Entrepreneurship Monitoring (GEM). This mathematical model has been implemented in a simulator program. This paper describes the simulator development and the use of simulator to simulate and visualize the entrepreneurial growth of the six provinces
The Comprehensive Mamdani Inference to Support Scholarship Grantee Decision
Get PDFFuzzy Mamdani has been mostly used in various disciplines of science. Its ability to map the input-output in the form of a surface becomes an interesting thing. This research took DSS case of a scholarship grantee. Many criteria in taking a decision need to be simplified so that the result obtained remains intuitive. The model completion by conducting two stages consisted of two phases. The first phase consists of four FIS blocks. The second phase consists of one FIS block. The FIS design in the first phase was designed in such a way so that the output obtained has a big score interval. FIS output at the first phase will become FIS input at the second phase. This big value range becomes good input at FIS in the second phase. Each FIS block has different total input. Until the surface formed must be seen from various dimensions to assure trend surface increasing or decreasing softly. This kind of thing is conducted by observing the movement of output dots kept for its soft surface form. The output dots change influenced by the membership function, the regulations used, total fuzzy set, and parameter value of membership function. This research used the Gaussian membership function. The Gaussian membership function is highly suitable for this DSS case. This article also explains the usage of a fuzzy set in each input, the parameter from the membership function, and the input value range. After observing the surface form with an intuitive approach, then this model needs to be evaluated. The evaluation was done to measure the model performance using Confusion Matrix. The result of model performance obtained accuracy in the amount of 85%
Virtual Reality (VR) in Superior Education Distance Learning: A Systematic Literature Review
Get PDFVirtual Reality (VR) is one of the most popular contemporary technologies and it is widely used in the videogame industry, nonetheless, this does not restrict its use in other areas of science, such as medicine or education. Due to the large commotion caused by the appearance of Covid-19, long distance virtual education technologies (e-learning) are being used. With this context, virtual reality is the focus of this study, which had the objective of understanding the work done in superior education at a distance, through the use of VR, by doing a systematic literature review (SLR; LSR in Spanish). The results reveal that the use of VR in education can improve the experience, motivation and the comprehension of abstract concepts for the students, offering them an immersive environment in which they can interact and achieve effective learning. It was concluded that the works that were reviewed regarding the topic evidence a strong growth in the application of VR in education, which in their majority, employ experimental comparative methods between groups of students which use VR when compared to others who use the traditional method