EMITTER International Journal of Engineering Technology
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160 research outputs found
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Technique of Standing Up From Prone Position of a Soccer Robot
No full textOne of the humanoid robots being developed in the field of sports is a soccer robot. A soccer robot is a humanoid robot that can perform activities such as playing football. And a variety method fall down of robot soccer such: falling down toward the front direction, side direction, and rear direction. This paper describes the most stands up methods of a soccer robot from its prone position. The proposed method requires only limited movement with degrees of freedom. The movement standing-up of soccer robot has been implemented on the real robot. Tests we performed showed that reliable standing-up from prone position is possible after a fall and such recovery procedures greatly improve the overall robustness of a Soccer Robot
Classification of Radical Web Content in Indonesia using Web Content Mining and k-Nearest Neighbor Algorithm
No full textRadical content in procedural meaning is content which have provoke the violence, spread the hatred and anti nationalism. Radical definition for each country is different, especially in Indonesia. Radical content is more identical with provocation issue, ethnic and religious hatred that is called SARA in Indonesian languange. SARA content is very difficult to detect due to the large number, unstructure system and many noise can be caused multiple interpretations. This problem can threat the unity and harmony of the religion. According to this condition, it is required a system that can distinguish the radical content or not. In this system, we propose text mining approach using DF threshold and Human Brain as the feature extraction. The system is divided into several steps, those are collecting data which is including at preprocessing part, text mining, selection features, classification for grouping the data with class label, simillarity calculation of data training, and visualization to the radical content or non radical content. The experimental result show that using combination from 10-cross validation and k-Nearest Neighbor (kNN) as the classification methods achieve 66.37% accuracy performance with 7 k value of kNN method[1]
Underwater Acoustic Channel Characterization of Shallow Water Environment
No full textUnderstanding of channel propagation characteristics is a key to the optimal design of underwater acoustic communication. Generally, modelling of underwater acoustic channel is performed based on measurement result in certain site at certain times. Different sites might have different characteristics, each of which can generally be described by a model obtained by averaging measurement results at multiple points in the same environment. This paper describes a characterization of the underwater acoustic channel of tropical shallow water in a Mangrove estuary, which has sediment up to 60 cm at the bottom. Such a channel model is beneficial for the design of communication system in an autonomous underwater vehicle, for instance. The measurement result of delay spread parameter from three different points with the distance of 14 ~ 52 m, has various values. The root mean square (RMS) of delay spread ranges between 0.0621 ~ 0.264 ms, and the maximum delay spread varies with the value of 0.187 ~ 1.0 ms. The pdf fitting shows that Rayleigh distribution describes the fading variation more accurately than Nakagami and Ricean
Semantic Madurese Batik Search with Cultural Computing of Symbolic Impression Extraction and Analytical Aggregation of Color,Shape and Area Features
No full textLack of information media about Madurese batik Causes low awareness of younger generation to maintain the production of Madurese batik. Actually, Madurese Batik also has a high philosophy, which the motif and colour reflect the character of the Madurese. Madurese Batik has useful motif as a mean of traditional communication in the form of certain cultural symbols. We collected images of Madurese Batik by identifying the impression of Madurese Batik motif taken from several literature books of Madurese Batik and also the results of observation of experts or craftsmen who understand about Madurese Batik. This research proposed a new approach to create on application which can identify Madurese Batik impression by using 3D-CVQ feature extraction methods to extract color features, and used Hu Moment Invariant for feature feature extraction. Application searching of Madurese Batik image has two ways of searching, those are based on the image input Madurese Batik and based on the input of impression Madurese batik. We use 202 madurese batik motifs and use search techniques based on colors, shapes and aggregations (color and shape combinations). Â Performance results using based on image queries used: (1) based on color, the average precision 90%, (2) based on shape, the average precision 85%, (3) based on aggregation, the average precision 80%, the conclusion is the color as the best feature in image query. While the performance results using based on the impression query are: Â (1) based on color, the average value of true 6.7, total score 40.3, (2) based on shape, the average value of true 4.1, total score 24.1, and (3) based on the aggregation, the average value of true 2.5, the total score is 13.8, the conclusion is the color as the best feature in impression query
Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG) Device
No full textThe implementation of Artificial Neural Network in prediction the direction of electric wheelchair from brain signal input for physical mobility impairment.. The control of the wheelchair as an effort in improving disabled person life quality. The interaction from disabled person is helping in relation to social life with others. Because of the mobility impairment, the wheelchair with brain signal input is made. This wheel chair is purposed to help the disabled person and elderly for their daily activity. ANN helps to develop the mapping from input to target. ANN is developed in 3 level: input level, one hidden level, and output level (6-2-1). There are 6 signal from Neurosky Mindset sensor output, Alpha1, Alpha2, Raw signal, Total time signal, Attention Signal, and Meditation signal. The purpose of this research is to find out the output value from ANN: value in turning right, turning left, and forward. From those outputs, we can prove the relevance to the target. One of the main problem that interfering with success is the problem of proper neural network training. Arduino uno is chosen to implement the learning program algorithm because it is a popular microcontroller that is economic and efficient. The training of artificial neural network in this research uses 21 data package from raw data, Alpha1, Aplha2, Meditation data, Attention data, total time data. At the time of the test there is a value of Mean square Error(MSE) at the end of training amounted to 0.92495 at epoch 9958, value a correlation coefficient of 0.92804 shows that accuracy the results of the training process good.  Keywords: Navigation, Neural network, Real-time training, ArduinoÂ
Javanese Character Feature Extraction Based on Shape Energy
No full textJavanese character is one of Indonesia's noble culture, especially in Java. However, the number of Javanese people who are able to read the letter has decreased so that there need to be conservation efforts in the form of a system that is able to recognize the characters. One solution to these problem lies in Optical Character Recognition (OCR) studies, where one of its heaviest points lies in feature extraction which is to distinguish each character. Shape Energy is one of feature extraction method with the basic idea of how the character can be distinguished simply through its skeleton. Based on the basic idea, then the development of feature extraction is done based on its components to produce an angular histogram with various variations of multiples angle. Furthermore, the performance test of this method and its basic method is performed in Javanese character dataset, which has been obtained from various images, is 240 data with 19 labels by using K-Nearest Neighbors as its classification method. Performance values were obtained based on the accuracy which is generated through the Cross-Validation process of 80.83% in the angular histogram with an angle of 20 degrees, 23% better than Shape Energy. In addition, other test results show that this method is able to recognize rotated character with the lowest performance value of 86% at 180-degree rotation and the highest performance value of 96.97% at 90-degree rotation. It can be concluded that this method is able to improve the performance of Shape Energy in the form of recognition of Javanese characters as well as robust to the rotation
A Similarity-Ranking Method on Semantic Computing for Providing Information-Services in Station-Concierge System
No full textThe prevalence of smartphones and wireless broadband networks have been progressing as a new Railway infomration environment.　According to the spread of such devices and information technology, various types of information can be obtained from databases connected to the Internet. One scenario of obtaining such a wide variety of information resources is in the phase of user’s transportation. This paper proposes an information provision system, named the Station Concierge System that matches the situation and intention of passengers. The purpose of this system is to estimate the needs of passengers like station staff or hotel concierge and to provide information resources that satisfy user’s expectations dynamically. The most important module of the system is constructed based on a new information ranking method for passenger intention prediction and service recommendation. This method has three main features, which are (1) projecting a user to semantic vector space by using her current context, (2) predicting the intention of a user based on selecting a semantic vector subspace, and (3) ranking the services by a descending order of relevant scores to the user’ intention. By comparing the predicted results of our method with those of two straightforward computation methods, the experimental studies show the effectiveness and efficiency of the proposed method. Using this system, users can obtain transit information and service map that dynamically matches their context
Data Mining Approach for Breast Cancer Patient Recovery
No full textBreast cancer is the second highest cancer type which attacked Indonesian women. There are several factors known related to encourage an increased risk of breast cancer, but especially in Indonesia that factors often depends on the treatment routinely. This research examines the determinant factors of breast cancer and measures the breast cancer patient data to build the useful classification model using data mining approach.The dataset was originally taken from one of Oncology Hospital in East Java, Indonesia, which consists of 1097 samples, 21 attributes and 2 classes. We used three different feature selection algorithms which are Information Gain, Fisher’s Discriminant Ratio and Chi-square to select the best attributes that have great contribution to the data. We applied Hierarchical K-means Clustering to remove attributes which have lowest contribution. Our experiment showed that only 14 of 21 original attributes have the highest contribution factor of the breast cancer data. The clustering algorithmdecreased the error ratio from 44.48% (using 21 original attributes) to 18.32% (using 14 most important attributes).We also applied the classification algorithm to build the classification model and measure the precision of breast cancer patient data. The comparison of classification algorithms between Naïve Bayes and Decision Tree were both given precision reach 92.76% and 92.99% respectively by leave-one-out cross validation. The information based on our data research, the breast cancer patient in Indonesia especially in East Java must be improved by the treatment routinely in the hospital to get early recover of breast cancer which it is related with adherence of patient
Tooth Color Detection Using PCA and KNN Classifier Algorithm Based on Color Moment
No full textMatching the suitable color for tooth reconstruction is an important step that can make difficulties for the dentists due to the subjective factors of color selection. Accurate color matching system is mainly result based on images analyzing and processing techniques of recognition system.  This system consist of three parts, which are data collection from digital teeth color images, data preparation for taking color analysis technique and extracting the features, and data classification involve feature selection for reducing the features number of this system. The teeth images which is used in this research are 16 types of teeth that are taken from RSGM UNAIR SURABAYA. Feature extraction is taken by the characteristics of the RGB, HSV and LAB based on the color moment calculation such as mean, standard deviation, skewness, and kurtosis parameter. Due to many formed features from each color space, it is required addition method for reducing the number of features by choosing the essential information like Principal Component Analysis (PCA) method. Combining the PCA feature selection technique to the clasification process using K Nearest Neighbour (KNN) classifier algorithm can be improved the accuracy performance of this system. On the experiment result, it showed that only using KNN classifier achieve accuracy percentage up to 97.5 % in learning process and 92.5 % in testing process while combining PCA with KNN classifier can reduce the 36 features to the 26 features which can improve the accuracy percentage up to 98.54 % in learning process and  93.12% in testing process. Adding PCA as the feature selection method can be improved the accuracy performance of this color matching system with little number of features.Â
Moment Invariant Features Extraction for Hand Gesture Recognition of Sign Language based on SIBI
No full textMyo Armband became an immersive technology to help deaf people for communication each other. The problem on Myo sensor is unstable clock rate. It causes the different length data for the same period even on the same gesture. This research proposes Moment Invariant Method to extract the feature of sensor data from Myo. This method reduces the amount of data and makes the same length of data. This research is user-dependent, according to the characteristics of Myo Armband. The testing process was performed by using alphabet A to Z on SIBI, Indonesian Sign Language, with static and dynamic finger movements. There are 26 class of alphabets and 10 variants in each class. We use min-max normalization for guarantying the range of data. We use K-Nearest Neighbor method to classify dataset. Performance analysis with leave-one-out-validation method produced an accuracy of 82.31%. It requires a more advanced method of classification to improve the performance on the detection results