Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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Visual Analysis Based on CMY and RGB Image Cryptography Using Vigenere and Beaufort Cipher
Get PDFThe achievement of visual aspects and image security often cannot meet visibility standards, for example the acquisition of PSNR and UACI NPCR values. To increase security, this research has implemented a combination of the Vigenere cipher and Beaufort and the use of Fibonacci as a randomizer. The combination of the Vigenere Cipher and Beaufort Cipher substitution algorithms with the Fibonacci technique can be applied to encrypt color images in RGB and CMY, with a size of 256x256 pixels and in .bmp format. The Fibonacci cut-off value used in this study is 10000. The highest entropy value of the cipher image peppers.bmp is 7,991. The lowest PSNR cipher image value is accordion.bmp where for RGB it is 5,439 dB and for CMY it is 5,403 dB. accordion.bmp's highest UACI value is 44.018% for RGB and 44.312% for CMY. The NPCR value in the airplane.bmp image has the highest value in RGB of 99.792% and for CMY the highest value is in splash.bmp with a value of 99.798%. Evaluation of the decryption results shows that the decryption process can run perfectly as indicated by the values of MSE=0, PSNR=inf, UACI and NPCR=0%. Therefore, encrypt and decrypt was proven that the results obtained in the visual aspect are very good
Evolutionary Algorithm in Game – A Systematic Review
Get PDFResearch in the game field is increasingly numerous and challenging. The high interest in research on games is influenced by public awareness of the importance of games in developing ways of thinking, although it is undeniable that many people only pursue pleasure in playing games. In the past, not much games research has influenced into topics such as artificial intelligence, education, or other computer topics. But now games are having a tremendous impact on these topics. In fact, not infrequently games are used in various areas of life. Right now, artificial intelligence is an integral part of the game. If before, it was only used for creating an enemy. Right now artificial intelligence can affect various things, starting from assets, game difficulty levels, non-player characters (NPC), and even bots (AI agents) to run player characters. The complexity of artificial intelligence which is getting higher and higher requires a good optimization algorithm. The evolutionary algorithm is one of the optimization algorithms, even though it cannot find the best one, with the high speed it can find a good solution. Through this paper review, good conclusions are drawn regarding the use of evolutionary algorithms, representations made, fitness functions used, ways to prove a success, to what topics should be studied further
Water Level Detection for Flood Disaster Management Based on Real-time Color Object Detection
Get PDFCurrently, the water level monitoring system for a river uses instruments installed on the banks of the river and must be checked continuously and manually. This study proposes a real-time water level detection system based on a computer vision algorithm. In the proposed system, we use color object tracking technique with a bar indicator as a reference’s level. We set three bar indicators to determine the status of the water level, namely NORMAL, ALERT and DANGER. A camera was installed across the bar level indicators to capture bar indicator and monitoring the water level. In the simulation, the monitoring system was installed in 5-100 lux lighting conditions. For experimental purposes, we set various distances of the camera, which is set of 40-80 centimeters and the camera angle is set of 30-60 degrees. The experiment results showed that this system has an accuracy of 94% at camera distance is in range 50-80 centimeters and camera angle is 60o. Based on these results, it can be concluded that this proposed system can determine the water level well in varying lighting conditions
Harmonic Reduction Using THIPWM Switching Technique with Type-2 Fuzzy on 3-Phase Motor
Get PDFThe development of the increasingly advanced industrial world has increased the need and use of electric motors for various purposes. In the industrial world, many electric motors are found as a driving device to drive various equipment needed, including a three-phase induction motor. The induction motor is expected to operate normally by the desired working characteristics. But it is undeniable that in its use, there are disturbances that can cause damage to the work system of the Induction motor, one of which is harmonic interference. The influence of harmonics on the induction motor causes copper and core losses which will reduce the efficiency motor and cause harmonic torque along with fundamental torque to produce vibration and noise, which considerably affect the operation three-phase induction motor. In this study, a 3-phase inverter was used with the Third Harmonic Injection pulse width modulation (THIPWM) method, with the use THIPWM Switching Method expected to increase the output voltage three-phase inverter and reduce the harmonics caused by the three-phase induction motor. In optimizing a 3-phase induction motor's speed regulation, scalar control or voltage/frequency (v/f) regulation is used. With the use THIPWM switching on this three-phase inverter, it is evident from simulation results that the harmonic value of THDV is 55.62%. THDI is 19.04%, as well the acceleration 3-phase induction motor with a rise time value of 48.547ms with steady-state error of 0.08% at set point 1200 rpm and with rise time value of 52.938ms with steady-state error 0% at set point 1000 rpm
Improving the Major Recommendation Systems: Analysis of Hybrid Naïve Bayes-based Collaborative Filtering and Fuzzy Logic
Get PDFMajor recommendation systems have been widely used to assist prospective students in choosing major that matches their interests and potential. In an effort to improve the performance of the recommendation system, this study proposed to use collaborative filtering techniques with naïve Bayes approach. In addition, this study improved the input parameters using fuzzy logic in determining the recommended majors. The methodology used started from collecting user data, including gender, academic history, interests, and other relevant attributes. The data were used to train the naïve Bayes technique by estimating the probability of feature conformity between users and students in the recommended majors. However, there were problems such as uncertainty and ambiguity in user preferences for input data. The fuzzy logic method aimed to improve the input parameters to more accurately reflect the user preferences. The results of improving the input parameters by using fuzzy logic were then used in the naïve Bayes technique to obtain recommendations for the direction that best suits the user’s preferences. The final stage of this study used evaluation metrics such as precision, recall, and f1-score to measure the performance of the recommendation system in providing accurate recommendations. The use of a hybrid of naïve Bayes and fuzzy logic algorithms obtains an accuracy value of 87.27%, a precision value of 87.33%, a recall value of 87.24%, and an f1-score value of 87.26%. These results are higher than the usual naïve Bayes model applied in major recommendation systems
Fish swarmed Fuzzy Time Series for Photovoltaic’s Forecasting in Microgrid
Get PDFForecasting irradiation and temperature is important for designing photovoltaic systems because these two factors have a significant impact on system performance. Irradiation refers to the amount of solar radiation that reaches the earth's surface, and directly affects the amount of energy that can be generated by a photovoltaic system. Therefore, accurate irradiation forecasting is essential for estimating the amount of energy a photovoltaic system can produce, and can assist in determining the appropriate system size, configuration, and orientation to maximize energy output. Temperature also plays an important role in the performance of a photovoltaic system. With increasing temperature, the efficiency of the solar cell decreases, which means that the energy output of the system also decreases. Therefore, accurate temperature forecasts are essential for estimating system energy output, selecting suitable materials, and designing effective cooling systems to prevent overheating. In summary, forecasting irradiation and temperature is important for designing photovoltaic systems as it helps in determining suitable system size, configuration, orientation, material selection, and cooling system, which ultimately results in higher energy output and better system performance. In recent decades, many forecasting models have been built on the idea of fuzzy time series. There are several forecasting models proposed by integrating fuzzy time series with heuristic or evolutionary algorithms such as genetic algorithms, but the results are not satisfactory. To improve forecasting accuracy, a new hybrid forecasting model combines fish swarm optimization algorithm with fuzzy time series. The results of irradiance prediction/forecasting with the smallest error are using the type of Fuzzy Time Series prediction model optimized with FSOA with RMSE is 0.83832
Buck-boost Converter using GA-based MPPT for Solar Energy Optimization
Get PDFEnergy optimization in the Solar Power Plant system needs to have more attention. Indonesia is a tropical country that has two seasons, where the weather and cloud movements are frequently unpredictable, especially in the southern region of Java Island. To overcome this problem, an inverter equipped with maximum power point tracking (MPPT) was used. However, the current MPPT switching system was still not optimal with an efficiency of around 90%. In this study, the installation of MPPT was carried out in order to optimize the power in solar photovoltaic (PV) system due to the fluctuations of solar irradiation at PT. Jatinom Indah Agri, Blitar City. The maximum power generated by solar photovoltaic could be achieved by using the combination of DC - DC converter and artificial intelligence. In this study, the modeling of solar PV system was made using MATLAB software, where the design of the solar PV system consisted of a PV module with capacity 240W, DC to DC converter, battery and MPPT. Genetic Algorithm (GA)-based MPPT had been tested and compared to Particle Swarm Optimization (PSO)-based MPPT and conventional MPPT, where the GA-based MPPT worked well in finding the maximum power point in the solar photovoltaic system. It was found that GA-based MPPT produced a maximum power point close to PV power with an efficiency of 92%, while the effciciency of PSO-based MPPT and conventional MPPT were 85% and 79% respectively. In selecting the method for designing MPPT, a method with a wide range of sample data is required. This is due to the fluctuation of solar irradiance received by the solar PV
Spatial and Spectral EEG Signal Analysis with Case Study of Slogans on Consumer’s Behaviour
Get PDFNeuromarketing utilizes neuroscientific techniques to investigate consumer behavior, providing valuable insights beyond traditional research methods such as questionnaires and interviews which may not provide a complete understanding of consumer decision-making processes. Electroencephalography (EEG) has emerged as a promising tool for analyzing consumer responses to marketing stimuli. Nevertheless, the neural processing of slogans and their impact on short-term memory recall using EEG signals remains understudied. This research aims to bridge this gap by examining the neural activity associated with the recall of slogans using EEG analysis. By employing a spatial selection and spectral processing method, which involves Butterworth BPF filtering and L2-norm normalization to identify optimal channel combinations, active brain areas involved in slogan processing can be identified. Results reveal prominent activation in the frontal and occipital regions, particularly the F4 channel, indicating active recall and visual processing in individuals who correctly respond to slogans. These findings underscore the significance of slogans as visual marketing stimuli and offer insights for effective branding strategies. Leveraging EEG signals and understanding short-term memory processes enables marketers to optimize the impact of slogans on consumer engagement and brand recognition
Utilization of AR Technology for The Development of Speech Therapy Applications by Optimizing Marked-Based Tracking Method
Get PDFAutism is a developmental disorder that attacks children mentally and causes difficulties in interacting with the social environment. Therapy that can be done to people with autism to deal with communication disorders is speech therapy. Application usage health or better known as mobile health is easy and fast to help users in accessing information about various health problems, one of which is child development or better known as an autism spectrum disorder (ASD) which was developed using augmented reality (AR) technology. The purpose of the study is to optimizing the marked-based tracking method to augmented reality technology for speech therapy tutorials for children with autism. The results obtained from this study are the SELPY application (Self Autism Therapy) mobile-based speech therapy for people with autism which is a product of the application of appropriate technology in the field of information technology, especially in the health sector. The marked-based tracking method has been successfully implemented in the development of speech therapy AR applications for children with autism spectrum disorder (ASD). This is by the results of the tests that have been carried out, namely distance testing and angle testing. The most ideal distance to detect marker/image targets is 40cm to 50cm with a smartphone tilt angle of 200 to 300
Implementation of Generative Adversarial Network (GAN) Method for Pneumonia Dataset Augmentation
Get PDFAs a communicable disease, the majority of pneumonia cases are brought on by bacteria or viruses, which cause the lungs' alveoli to swell with fluid or mucus. Pneumonia may arise from this and further making breathing challenging since the lungs' air sacs are unable to contain enough oxygen for the body. Pneumonia may generally be diagnosed clinically (by a physician based on physical symptoms) as well as through a photo chest radiograph, CT scan, and MRI. In this case, the lower cost of a chest radiograph examination making it as one of the most popular medical imaging tests. However, chest radiograph photo readings have a disadvantage, where it takes a long time for medical staff or physicians to identify the patient's illness since it is difficult to detect the condition. Therefore, an identification of chest radiograph imagery into various forms using machine learning becomes one way to address this issue. This research focuses on building a deep neural network model using techniques from the Generative Adversarial Network algorithm. GAN is a category of machine learning techniques using two models to be trained simultaneously, one is a generator model to generated fake data and the other is a discriminator model used to separate the raw data from the real data set images. The dataset used is Chest X-Ray images obtained from repo GitHub and repo Kaggle totaling 5,863 with normal data 1583 images and pneumonia data 4273 imagesThe results showed that the use of the Generative Adevrsarial Network method as augmentation data proved to be more effective in improving the generalization of neural networks, this can be seen from the results the result of the accuracy value obtained is 97%