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Bag Toss Game based on Internet of Education Things (IoET) for the Development of Fine Motor Stimulation in Children 5-6 Years Old
Full text linkThe development of a child's motor skills starts when a child is 0 months old to 6 years old. In general, the development of motor skills divided into fine motor development and gross motor development. Fine motor development is a development that involves small muscles to follow certain movements. An example of a game activity to help stimulate small muscle development is the Bag Toss game. This game helps stimulate fine motor development by increasing eye coordination with the hand. In addition to the types of activities that boost fine motor development, it also requires the ability to monitor, record, and process the results of children's activities to assess and analyze the status of a child's fine motor development. In this study, we developed the Bag Toss game system that connected to the Internet of Things (IoT) platform. Bag Toss game has linked with a sensor that will record children's play activities. The results of recording data will be sent to the IoT platform to be processed and presented through the internet network. The implementation of IoT for educational purposes is known as the Internet of Educational Things (IoET). The system built will be tested in terms of functionality, reading accuracy and child assessment. The functionality of the system works 100% according to predetermined component functions, as well as for 100% successful reading accuracy for the scenario of throwing distances of 1 meter and 1.5 meters. In addition, the average delay time for every hole is 0.62 seconds. The delay value can still be tolerated and does not interfere with the game when the child assessment is conducted. The child assessment involved 4 children, the results obtained that 3 children are in the Well Development (BSH) stage and 1 child in Very Well Development (BSB) stage
A Technique For Lock-In Prediction On A Fluid Structure Interaction Of Naca 0012 Foil With High Re
Full text linkA numerical lock-in prediction technique of a NACA 0012 hydrofoil, immersed in a flow having a Re of 3.07x106 is proposed in this paper. The technique observes the foil’s response as part of a fluid-structure interaction analysis. The response is modelled by foil’s vibration which is represented by spring and damper components. The technique identifies and predicts the foil’s lock-in when it vibrates. The prediction is examined using the Phase Averaged Method which employs the Hilbert Transform Method. The aim of this paper is to propose a numerical way to identify a lock-in condition experienced by a NACA 0012 foil in a high Reynolds number flow. The foil’s mechanical properties are selected and its motions are restricted in two modes which are in the pitch and heave directions. The rotational and transverse lock-in modes are identified in the model. The existence of lock-in is verified using pressure distribution plot, the history of trailing edge displacement and fluid regime capture. The history of total force coefficients is also shown to justify the result. The result shows that the technique can predict reliably the lock-in condition on the foil’s interaction. Three main fluid induced vibration frequencies are generated in the interaction. None of them are close to natural frequency of the foil and lock-in is apparently not found in the typical operational condition
Face Recognition System for Prevention of Car Theft with Haar Cascade and Local Binary Pattern Histogram using Raspberry Pi
Full text linkIn this era, the occurrence of vehicle theft has become a fairly frequent problem, especially in big cities like Jakarta and Surabaya. Although the technology for car security is very sophisticated (e.g. keyless system), but there are many cases that thieves still can break into the system. Once a car was stolen, the whereabouts of the car was unknown and the thief was on the loose. The goal of this research is to overcome this problem. As a device, this research works on Raspberry Pi 3 that connected with the Raspberry Pi Camera. Using the OpenCV library, with the Haar Cascade method for face detection, and Local Binary Pattern Histogram for face recognition. The device must be connected to the internet in order to send the information using a Telegram message. The research results show the success of the device system in face-recognizing between the car owner and car thief with optimal conditions in the morning until the afternoon with the light intensity around 660 to 1000 lux, and best recognizing distance at 50 cm. The success rate for obtaining the car’s location for the outdoor condition is 100%. Even if there is a slope or an error data, it can be tolerated because the difference was not too high, about 0.1-1.0 m
Unsupervised Twitter Sentiment Analysis on The Revision of Indonesian Code Law and the Anti-Corruption Law using Combination Method of Lexicon Based and Agglomerative Hierarchical Clustering
Full text linkThe rejection on ratification of the revision of Indonesian Code Law or known as RKUHP and Corruption Law raises several opinions from various perspectives in social media. Twitter as one of many platforms affected, has more than 19.5 million users in Indonesia. Twitter is one of many social media in Indonesia where people can share their views, arguments, information, and opinions from all points of view. Since Twitter has a great diversity of users, it needs a system which is designed to determine the opinion tendency towards the problems or objects. The purpose of this study is to analyze the sentiment of Twitter users' tweets to reject the revision of the Law whether they have positive or negative sentiments using the Agglomerative Hierarchical Clustering method. The data that being used in this study were obtained from the results of crawling tweets based on hashtag (#) (#ReformasiDikorupsi). The next stage is pre-processing which consists of case folding, tokenizing, cleansing, sanitizing, and stemming. The extraction features Lexicon Based and Term Frequency (TF) which performs the process automatically. In the clustering stage, two clusters use three approaches; single linkage, complete linkage and average linkage. In the accuracy calculation phase, the writer uses the error ratio, confusion matrix, and silhouette coefficient. Therefore, the results are quite good. From 2408 tweets, the highest accuracy results are 61.6%
Implementation of a V/f Controlled Variable Speed Induction Motor Drive
Full text linkDSP implementation of speed control of three phase induction motor drive is presented in this paper. A closed loop speed control has been achieved using constant V/f technique which is a simple scalar control method used to control the magnitude of the control quantities. In this study, a fourth order polynomial drived from V/f curve is constructed instead of using look up table which takes much time for determination of voltage from frequency value. A PI controller is used in speed control. dSPACE DS-1103 controller board is used for the implementation with Matlab/Simulink, which has a simple real time interface. Steady-state speed characteristics and transient responses with various reference speed commands are presented by experimental system. The simulation and experimental results provide a smooth speed response and good performance under various dynamic operations. Real time speed control has been implemented and some results are presented
Developing Shooter Game Interaction using Eye Movement Glasses
Full text linkA quadriplegic is a paralysis that affects limitations in some physical movements and psychological disorders. They have limited media to interact with computers so a suitable solution is needed in the form of a media that can recognize other body parts movements which in this research uses eye movement. one of the solutions to this problem is to propose alternative technologies to interact and play games. We propose a simple technique by using a camera mounted on the glasses that will take the eye area. This technique will help reduce unnecessary parts of eye detection so that performance increases. The eyes will be processed using basic image processing and then determined the center position of the pupil using the Mean method. This system consists of pupil movements for pointer motion control and blinking of eyes for shooting. The performance test of this method toward the system, which has used 10 people with 7 experiments, shows an accuracy of 84.86 percent, the speed of movement with a duration of 2.22 seconds and the speed of response blinking with a duration of 0.026 seconds. In addition, we can distinguish between intentional blink and unintentional blink in which intentional blink has a duration of 0.30 seconds and unintentional 0.12 seconds. It can be concluded that by using this method and this technique is able to achieve good accuracy and also able to use intentional blink as shoot trigger
Design and Implementation of Embedded Water Quality Control and Monitoring System for Indoor Shrimp Cultivation
Full text linkMaintaining the water quality of a pond is one of the main issues on aquaculture management. Water quality represents the condition of a pond based on several water parameters such as dissolved oxygen (DO), temperature, pH, and salinity. All of these parameters need to be strictly supervised since it affects the life-sustainability of cultivated organisms. However, DO is said to be the main parameter since it affects the growth and survival rate of the shrimp. Therefore, a water quality control and monitoring system is needed to maintain water parameters at acceptable value. The system is developed on a mini-PC and microcontroller which are integrated with several sensors and actuator forming an embedded system. Then, this system is used to collect water quality data that is consisting of several water parameters and control the DO as the main parameter. In accordance with the stability needs against the sensitive environment, a fuzzy logic-based controller is developed to maintain the DO rate in the water. This system is also equipped with SIM800 module to notice the farmer by SMS, built-in wifi module for web-based data logging, and improved with Android-based graphical user interface (GUI) to perform user-friendly monitoring. From the experiment results, a fuzzy controller that is attached to the system can control the DO at the acceptable value of 6 ppm. The controller is said to have high robustness since its deviation for long-time use is only 0.12 ppm. Another test shows that the controller is able to overcome the given disturbance and easily adapt when the DO’s set point is changed.  Finally, the system is able to collect and store the data into cloud storage periodically and show the data on a website
Collaboration FMADM And K-Means Clustering To Determine The Activity Proposal In Operational Management Activity
Full text linkIndonesian government agencies under the Ministry of Energy and Mineral Resources still use manual methods in determining and selecting proposals for operational activities to be carried out. This study uses the Decision Support System (DSS) method, namely Fuzzy Multiple Attribute Decision Decision (Fmadm) and K-Means Clustering method in managing Operational Plan activities. Fmadm to select the best alternative from a number of alternatives, alternatives from this study proposed activity proposals, then ranking to determine the optimal alternative. The K-Means Clustering Method to obtain cluster values for alternatives on the criteria for activity dates, types of activities, and activity ceilings. The last iteration of the Euclidian distance calculation data on k-means shows that alternatives that have the smallest centroid value are important proposal criteria and the largest centroid value is an insignificant proposal criteria. The results of the collaboration of the Fmadm and K-Means Clustering methods show the optimal ranking of activities (proposal activities) and the centroid value of each alternative
FPGA Based Design of Artificial Neural Processor Used for Wireless Sensor Network
Full text linkIn this paper, a simulation of artificial intelligent system has been designed for processing the incoming data of sensor units and then presenting proper decision. The Back-propagation Neural Network BPNN has been used as the proposed intelligent system for this work, whereas the BPNN is considered as a trained network in conjunction with an optimization method for changing the weights and biases of the overall network. The main two features of the BPNN are: high speed processing, and producing lowest Mean-Square-Error MSE ( cost function ) in few iterations. The proposed BPNN has used the linear activation functions 'Satlins' and 'Satline' for the hidden and output layer respectively, and has used the training function 'Traingda' ( which is gradient descent with adaptive learning rate) as a powerful learning method. It is worth to mention, that no previous research used these three functions together for such analysis. The MATLAB software package has been used for designing and testing the proposed system. An optimal result has been obtained in this work, where the value of Mean-Square-Error has reached to zero  in 87 epochs, and the real and desired outputs have been fitted. In fact, there is no previous work has reached to this optimal result. The proposed BPNN has been implemented in FPGA, which is fast, and low power tool
Cluster-Based News Representative Generation with Automatic Incremental Clustering
Full text linkNowadays, a large volume of news circulates around the Internet in one day, amounting to more than two thousand news. However, some of these news have the same topic and content, trapping readers among different sources of news that say similar things. This research proposes a new approach to provide a representative news automatically through the Automatic Incremental Clustering method. This method began with the Data Acquisition process, Keyword Extraction, and Metadata Aggregation to produce a news metadata matrix. The news metadata matrix consisted of types of word in the column and news section of each line. Furthermore, the news on the matrix were grouped by the Automatic Incremental Clustering method based on the number of word similarities that arised, calculated using the Euclidean Distance approach, and was done automatically and real-time. Each cluster (topic) determined one representing news as a Representative News based on the location of the news closest to the midpoint/centroid on the cluster. This study used 101 news as experimental data and produced 87 news clusters with 85.14% precision ratio