Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
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Network Forensics Against Ryuk Ransomware Using Trigger, Acquire, Analysis, Report, and Action (TAARA) Method
Get PDFThis study aims to reconstruct an attack event and analyze the source of viral infection based on network traffic logs so that the information obtained can be used for a new reference in the security system. Recent attacks on computer network systems cannot be easily detected, as cybercrime has used a variant of the Ryuk Ransomware virus to penetrate security systems, encrypt drives, and computer network resources. This virus is very destructive and has an effective design with a file size of about 200,487 Bytes so it does not look suspicious. The research steps are done through Trigger, Acquire, Analysis, Report, and Action (TAARA). The forensic tools used to obtain log data are Wireshark, NetworkMiner, and TCPDUMP. Based on the results of forensic data obtained include a timestamp, source of the attack, IP address, MAC address, hash signature sha256, internet protocol, and the process of infection. Based on the data obtained in this study has been by the expected objectives
An Evaluation of Complementary Filter Method in Increasing the Performance of Motion Tracking Gloves for Virtual Reality Games
Get PDFIn the use of Virtual Reality-based video games, users need additional devices to interact, one of which is a Motion Tracking Glove. The Motion Tracking Glove is one of the enhancements that users can use to interact with objects in VR video games. To get the angle value, an accelerometer sensor is used in the MPU6050 module. However, the problem that arises is the accuracy of the sensor because VR demands a low error rate. The purpose of this study is to improve the accuracy of the angular value of the accelerometer sensor value with a complementary filter. Complementary filters can increase the accuracy of the accelerometer sensor by combining its value with the gyroscope sensor value. The Motion Tracking Glove is built using the Arduino Nano and the MPU6050 module to capture angles that move according to hand movements, to connect and exchange data to the main VR device, the Motion Tracking Glove using the Bluetooth module. The results are RMSE 0.6 and MAPE 2.5% with a static Motion Tracking Glove position without movement. In sending Motion Tracking Glove data using the Bluetooth module, the resulting delay time when sending ranges from 0.1 second to 0.4 seconds by trying to move the Motion Tracking Glove from 0 degrees to 90 degrees and back to 0 degrees
Flood Disaster and Early Warning: Application of ANFIS for River Water Level Forecasting
Get PDFIntensively monitoring river water level and flows in both upstream and downstream catchments are essential for flood forecasting in disaster risk reduction. This paper presents a developed flood river water level forecasting utilizing a hybrid technique called adaptive neuro-fuzzy inference system (ANFIS) model, employed for Kelantan river basin, Kelantan state, Malaysia. The ANFIS model is designed to forecast river water levels at the downstream area in hourly lead times. River water level, rainfall, and river flows were considered as input variables located in upstream stations, and one river water level in the downstream station is chosen as flood forecasting point (FFP) target. Particularly, each of these input-output configurations consists of four stations located in different areas. About twenty-seven data with fifteen minutes basis recorded in January 2013 to March 2015 were used in training and testing the ANFIS network. Data preprocessing is done with feature reduction by principal component analysis and normalization as well. With more attributes in input configurations, the ANFIS model shows better result in term of coefficient correlation ( ) against artificial neural network (ANN)-based models and support vector machine (SVM) model. In general, it is proven that the presented ANFIS model is a capable machine learning approach for accurate forecasting of river water levels to predict floods for disaster risk reduction and early warning
Opinion Spam Classification on Steam Review using Support Vector Machine with Lexicon-Based Features
Get PDFSteam is a video game digital distribution platform developed by Valve Software. Steam provides a user review feature, where users can write about criticism or comments on games that can contain positive or negative sentiments. Based on the questionnaire that the author conducted to Steam users from all over Indonesia, the user review feature provided by Steam was not sufficient. This is because there are fake reviews that allow biased opinions from certain parties so that a phenomenon called review bombing often occurs where users review only to drop or raise the image of a product, not to review it sincerely. From these problems, a solution design is needed that can classify fake reviews on the Steam service. The Support Vector Machine (SVM) classification method was chosen as the model in combination with lexicon-based feature retrieval and Term Frequency – Inverse Document Frequency (TF-IDF) weighting. Of the 236 classification test data conducted by SVM, it produced 105 reviews which were categorized as Valid Reviews. Meanwhile, those categorized as Opinion Spam by SVM are 131 reviews. The accuracy level of the data classification model using Support Vector Machine method is of 81% by dividing training data by 70% and test data by 30% with a random state level of 109. A dashboard in the form of a web application has also been made that contains the classification model to be used for buying reference for Steam user
Firefly Algorithm For Optimizing Single Axis Solar Tracker
Get PDFSolar cells mounted on solar panel modules are expected to track sunlight throughout the day to produce maximum energy. The Firefly algorithm (FA) is embedded in the Arduino Mega microcontroller to control the tracking of the sun's position by the solar panel so that the absorption of solar energy can be as much as possible to get maximum electrical energy. The brightest light captured by the solar panel is represented as the light intensity of a firefly. The output of the solar tracking system is obtained by finding the best value of light intensity between fireflies. Parameter changes in FA, such as firefly population, random numbers, and number of iterations affect the results of FA. The largest population, the highest random number and iteration provide the best solution but take a long time to execute. FA can control solar panels in tracking the sun's position precisely with an average error of 1.28% and can absorb a total energy of 666.14 Watt/day. The best solution (98% of setpoint 720) was obtained when the population was set to 50, the random number to 0.8, and iteration to 50. This research can be used as a reference for later using a controller with higher specifications to speed up the FA process time in getting maximum control results
Autonomous Visual Servoing for Alternately Working Arm Robots
Get PDFRobots have infiltrated many aspects of human life up to this point, and with the term Industry 4.0, robots have even become the primary workforce in various factories. This condition necessitates that the robots collaborate without clashing. This paper discusses the application of two arm robot manipulators working alternately in sorting agricultural products. The proposed method employs simple image processing to detect the object and becomes the input to the system to control the robots. The effectiveness of the proposed method is enhanced by the application of a Fuzzy Logic Controller to smoothen robots’ joints motions. The average time required by the robot to finish their task from detecting to returning to standby position is 11.76 s for green tomatoes and 12.86 s for red tomatoes. The experimental results show that the proposed method is effective in controlling two robots to pick and place agricultural products using visual servoing
Balancing Entertainment, Cost, and Educational Strength: A Design Framework for Medium-Coupling Educational Games
Get PDFGames are powerful tools for teaching and learning, especially in the age of Education 4.0. The rapid growth of educational games is hindered by various problems, including their development cost. The medium coupling approach, which allows an educational game to be both engaging and economical, is one potential solution to the problem; unfortunately, it has received only sporadic attention. This study aims to explore the matter of designing medium-coupling educational games through a design framework. The framework dissects an educational game’s game content and educational content into parts to be interconnected. We applied the framework to design and develop an educational role-playing game (RPG). Sixty first-year Informatics students tested the game, which presented three learning topics with different characteristics. A post-test questionnaire’s results validate the game’s entertainment and educational values while also uncovering how it works under different learning contents. A discussion with the participants gathered insightful suggestions and critiques, which, together with the questionnaire’s results, are synthesized into design principles for medium-coupling games. We conclude that the framework is useful for creating games that are entertaining, educational, and cost-efficient
Design and Implementation LETS (Low Power Cluster Server) for Sustaining UMKM during Pandemic
Get PDFCovid pandemic is spreading since end 2019 arround globe, this resulting several countries suffer economic crisis. Due people movement restriction and international border is closed, impact halting on export and import. Indonesia with 267,7 million people manage to overcome global crisis during 1998 and 2008 with encourage small and local industry. According data from BPS, SME support 60% GDP with Rp 7,7 Billion and provide 85 million employment. To sustain SME during customer shifting behavior, we propose small cluster server called as LETS. LETS is modular, and composed with several Raspberry Pi 4, also consume few energies compared with traditional server. Although Raspberry Pi 4 is small size with few limitations, with lightweight application (ansible, k3s, and nginx) it can provide web server service. LETS provide handling traffic up to 742 simultaneus requests, which sufficient of SME requirement
A Hybrid Tabu Search and Genetic Algorithm Imputation Approach for Incomplete Data
Get PDFThe common problem for data collection is happening missing value during the data collection and processing process that the quality of the data testing is decreased. A computational based technique for dealing with missing values, namely Genetic Algorithm Imputation (GAI). The usage was used to estimate the dataset's missing values. GAI generates the optimal set of missing values with the acquisition of information as a function of fitness to measure individual solutions' performance. GAI conducts continuous searching until the missing criteria value is found according to best fitness. So, it is trapped in optimal conditions temporarily. The improvement of GAI with tabu search is known as TS-GAI, that strength is two metaheuristic techniques modified at the mutase stage to distract the local optima's search. In applying missing values, this technique works better when many possible values are used instead of the mixed attribute having missing values. Because the new generation chromosome values generate many opportunities to make up for the missing values. The experimental results show that the TS-GAI shows better performance on 30% MV with a fitness value of 0.212. It converges at 159 iterations. Generally, TS-GAI is a faster iteration than simple GAI and it has a lower RMSE level than other imputation techniques
The Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning
Get PDFDigital image modification or image forgery is easy to do today. The authenticity verification of an image become important to protect the image integrity so that the image is not being misused. Error Level Analysis (ELA) can be used to detect the modification in image by lowering the quality of image and comparing the error level. The use of deep learning approach is a state-of-the-art in solving cases of image data classification. This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach. The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection. The impacts of applying different ELA compression levels, such as 10, 50, and 90 percent, were also compared in this study. According to the results, adopting the ELA feature increases validation accuracy by about 2.7% and give the better test accuracy. However, the use of ELA will slow down the processing time by about 5.6%