Jurnal Ilmu Komputer dan Informasi
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247 research outputs found
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SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH
Full text linkTo extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%. Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology
MACHINE LEARNING FOR DATA CLASSIFICATION IN INDONESIA REGIONAL ELECTIONS BASED ON POLITICAL PARTIES SUPPORT
Full text linkIn this paper, we discuss the implementation of Machine Learning (ML) to predict the victory of candidates in Regional Elections in Indonesia based on data taken from General Election Commission (KPU). The data consist of composition of political parties that support each candidate. The purpose of this research is to develop a Machine Learning model based on verified data provided by official institution to predict the victory of each candidate in a Regional Election instead of using social media data as in previous studies. The prediction itself simply a classification task between two classes, i.e. ‘win’ and ‘lose’. Several Machine Learning algorithms were applied to find the best model, i.e. k-Nearest Neighbors, Naïve Bayes Classifier, Decision Tree (C4.5), and Neural Networks (Multilayer Perceptron) where each of them was validated using 10-fold Cross Validation techniques. The selection of these algorithms aims to observe how the data works on different Machine Learning approaches. Besides, this research also aims to find the best combination of features that can lead to gain the highest performance. We found in this research that Neural Networks with Multilayer Perceptron is the best model with 74.20% of accuracy
GESTURE RECOGNITION FOR PENCAK SILAT TAPAK SUCI REAL-TIME ANIMATION
Full text linkThe main target in this research is a design of a virtual martial arts training system in real-time and as a tool in learning martial arts independently using genetic algorithm methods and dynamic time warping. In this paper, it is still in the initial stages, which is focused on taking data sets of martial arts warriors using 3D animation and the Kinect sensor cameras, there are 2 warriors x 8 moves x 596 cases/gesture = 9,536 cases. Gesture Recognition Studies are usually distinguished: body gesture and hand and arm gesture, head and face gesture, and, all three can be studied simultaneously in martial arts pencak silat, using martial arts stance detection with scoring methods. Silat movement data is recorded in the form of oni files using the OpenNI ™ (OFW) framework and BVH (Bio Vision Hierarchical) files as well as plug-in support software on Mocap devices. Responsiveness is a measure of time responding to interruptions, and is critical because the system must be able to meet the demand
TWEET CLASSIFICATION USING DEEP LEARNING ARCHITECTURE FOR CONCERT EVENT DETECTION
Full text linkTwitter social media is used by millions of users to share stories about their lives. There are millions of tweets sent by Twitter users in a short amount of time. These tweets can contain information about an incident, complaints from Twitter users, and others. Finding information about events from existing tweets requires great effort. Therefore, this study proposed a system that can detect events based on tweets using the CNN-LSTM architecture. Based on the classification testing obtained precision results of 70.97%, and recall amounted to 63.76%. The results obtained are good enough as a first step to detect events on Twitter
A COMPARISON OF CLUSTERING BY IMPUTATION AND SPECIAL CLUSTERING ALGORITHMS ON THE REAL INCOMPLETE DATA
Full text linkThe existence of missing values will really inhibit process of clustering. To overcome it, some of scientists have found several solutions. Both of them are imputation and special clustering algorithms. This paper compared the results of clustering by using them in incomplete data. K-means algorithms was utilized in the imputation data. The algorithms used were distribution free multiple imputation (DFMI), Gabriel eigen (GE), expectation maximization-singular value decomposition (EM-SVD), biplot imputation (BI), four algorithms of modified fuzzy c-means (FCM), k-means soft constraints (KSC), distance estimation strategy fuzzy c-means (DESFCM), k-means soft constraints imputed-observed (KSC-IO). The data used were the 2018 environmental performance index (EPI) and the simulation data. The optimal clustering on the 2018 EPI data would be chosen based on Silhouette index, where previously, it had been tested its capability in simulation dataset. The results showed that Silhouette index have the good capability to validate the clustering results in the incomplete dataset and the optimal clustering in the 2018 EPI dataset was obtained by k-means using BI where the silhouette index and time complexity were 0.613 and 0.063 respectively. Based on the results, k-means by using BI is suggested processing clustering analysis in the 2018 EPI dataset
Irregular Grid Interpolation using Radial Basis Function for Large Cylindrical Volume
Full text linkIrregular grid interpolation is one of the numerical functions that often used to approximate value on an arbitrary location in the area closed by non-regular grid pivot points. In this paper, we propose method for achieving efficient computation time of radial basis function-based non-regular grid interpolation on a cylindrical coordinate. Our method consist of two stages. The first stage is the computation of weights from solving linear RBF systems constructed by known pivot points. We divide the volume into many subvolumes. At second stages, interpolation on an arbitrary point could be done using weights calculated on the first stage. At first, we find the nearest point with the query point by structuring pivot points in a K-D tree structure. After that, using the closest pivot point, we could compute the interpolated value with RBF functions. We present the performance of our method based on computation time on two stages and its precision by calculating the mean square error between the interpolated values and analytic functions. Based on the performance evaluation, our method is acceptable
Visual Recognition Of Graphical User Interface Components Using Deep Learning Technique
Full text linkGraphical User Interface (GUI) building in software development is a process which ideally need to go through several steps. Those steps in the process start from idea or rough sketch of the GUI, then refined into visual design, implemented in coding or prototype, and finally evaluated for its function and usability to discover design problem and to get feedback from users. Those steps repeated until the GUI considered satisfactory or acceptable by the user. Computer vision technique has been researched and developed to make the process faster and easier; for example generating code for implementation, or automatic GUI testing using component images. But among those techniques, there are still few for usability testing purpose. This preliminary research attempted to make the foundation for usability testing using computer vision technique by built minimalist dataset which has images of various GUI components and used the dataset in deep learning experiment for GUI components visual recognition. The experiment results showed deep learning technique suitable for the intended task, with accuracy of 95% for recognition of two different types of components, and accuracy of 72% for six different types of component
MODIFICATION OF ALEXNET ARCHITECTURE FOR DETECTION OF CAR PARKING AVAILABILITY IN VIDEO CCTV
Full text linkThe difficulty of finding a parking space in public places, especially during peak hours is a problem experienced by drivers. To assist the driver in finding parking space availability, a system is needed to monitor parking availability. One study to detect the availability of parking lots utilizing CCTV. However, research on the availability of parking spaces on CCTV data has several problems, detecting parking slots that are done manually to be inefficient when applied to different parking lots. Also, research to detect the availability of parking lots using the Convolution Neural Network (CNN) method with existing architecture has many parameters. Therefore, this study proposes a system to detect the availability of car parking lots using You Only Look Once (YOLO) V3 for marking the parking space and proposed a new architecture CNN called Lite AlexNet which has few parameters than other methods to speed up the process of detecting parking space availability. The best accuracy of the marking stage using YOLO V3 is 92.31% where the weather was cloudy. For the proposed Lite AlexNet get the best time training average which is 7 second compare to other existing methods and the average accuracy in every condition is 92.33% better than other methods
Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System
Full text linkThis paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction
SPAMMER DETECTION BASED ON ACCOUNT, TWEET, AND COMMUNITY ACTIVITY ON TWITTER
Full text linkSpammers are the activities of users who abuse Twitter to spread spam. Spammers imitate legitimate user behavior patterns to avoid being detected by spam detectors. Spammers create lots of fake accounts and collaborate with each other to form communities. The collaboration makes it difficult to detect spammers' accounts. This research proposed the development of feature extraction based on hashtags and community activities for the detection of spammer accounts on Twitter. Hashtags are used by spammers to increase popularity. Community activities are used as features for the detection of spammers so as to give weight to the activities of spammers contained in a community. The experimental result shows that the proposed method got the best performance in accuracy, recall, precision and g-means with are 90.55%, 88.04%, 3.18%, and 16.74%, respectively. The accuracy and g-mean of the proposed method can surpassed previous method with 4.23% and 14.43%. This shows that the proposed method can overcome the problem of detecting spammer on Twitter with better performance compared to state of the art