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28,451 Article Results

Improved Face Recognition Across Poses using Fusion of Probabilistic Latent Variable Models

10.12928/telkomnika.v15i4.5731
Moh Edi; Universitas Gadjah Mada, Indonesia Wibowo , Dian; Queensland University of Technology, Australia Tjondronegoro , Vinod; Queensland University of Technology, Australia Chandran , Reza; Universitas Gadjah Mada, Indonesia Pulungan , Jazi Eko; Universitas Gadjah Mada, Indonesia Istiyanto
Uncontrolled environments have often required face recognition systems to identify faces appearing in poses that are different from those of the enrolled samples. To address this problem, probabilistic latent variable models have been used to perform face recognition across poses. Although these models have demonstrated outstanding performance, it is not clear whether richer parameters always lead to performance improvement. This work investigates this issue by comparing performance of three probabilistic latent variable models, namely PLDA, TFA, and TPLDA, as well as the fusion of these classifiers on collections of video data. Experiments on the VidTIMIT+UMIST and the FERET datasets have shown that fusion of multiple classifiers improves face recognition across poses, given that the individual classifiers have similar performance. This proves that different probabilistic latent variable models learn statistical properties of the data that are complementary (not redundant). Furthermore, fusion across multiple images has also been shown to produce better perfomance than recogition using single still image.
Volume: 15
Issue: 4
Page: 1971-1981
Publish at: 2017-12-01

Contradictory of the Laplacian Smoothing Transform and Linear Discriminant Analysis Modeling to Extract the Face Image Features

10.12928/telkomnika.v15i4.6576
Arif; University of Trunojoyo, Indonesia Muntasa , Indah Agustien; University of Trunojoyo, Indonesia Siradjuddin
Laplacian smoothing transform uses the negative diagonal element to generate the new space. The negative diagonal elements will deliver the negative new spaces. The negative new spaces will cause decreasing of the dominant characteristics. Laplacian smoothing transform usually singular matrix, such that the matrix cannot be solved to obtain the ordered-eigenvalues and corresponding eigenvectors. In this research, we propose a modeling to generate the positive diagonal elements to obtain the positive new spaces. The secondly, we propose approach to overcome singularity matrix to found eigenvalues and eigenvectors. Firstly, the method is started to calculate contradictory of the laplacian smoothing matrix. Secondly, we calculate the new space modeling on the contradictory of the laplacian smoothing. Moreover, we calculate eigenvectors of the discriminant analysis. Fourth, we calculate the new space modeling on the discriminant analysis, select and merge features. The proposed method has been tested by using four databases, i.e. ORL, YALE, UoB, and local database (CAI-UTM). Overall, the results indicate that the proposed method can overcome two problems and deliver higher accuracy than similar methods. 
Volume: 15
Issue: 4
Page: 1794-1807
Publish at: 2017-12-01

Assessing Effectiveness of Research for Load Shedding in Power System

10.11591/ijece.v7i6.pp3235-3245
Raghu C.N. , A. Manjunatha
The research on loadshedding issues dates back to 1972 and till date many studies were introduced by the research community to address the issues. A closer review of existing techniques shows that still the effectiveness of loadshedding schemes are not yet benchmarked and majority of the existing system just considers the techniques to be quite symptomatic to either frequency or voltage. With an evolution of smart grids, majority of the controlling features of power system and networks are governed by a computational model. However, till date not enough evidences of potential computational model has been seen that claims to have better balance between the load shedding schemes and quality of power system performance. Hence, we review some significant literatures and highlights the research gap with the existing technqiues of load balancing that is meant for assisting the researcher to conclude after the selection process of existing system as a reference for future direction of study.
Volume: 7
Issue: 6
Page: 3235-3245
Publish at: 2017-12-01

Quality of Experience (QOE) Aware Video Attributes Determination for Mobile Streaming Using Hybrid Profiling

10.11591/ijeecs.v8.i3.pp597-609
Muhamad Hanif Jofri , Mohd Farhan Md Fudzee , Mohd Norasri Ismail , SHAHREEN KASIM , Jemal Abawajy
Today, consumers use a smartphone device to display the media contents for work and entertainment purposes, as well as watching online video. Online video streaming is the main cause that consume smartphone’s energy quickly. To overcome this problem, smartphone’s energy management is crucial. Thus, a hybrid energy-aware profiler is proposed. Basically, a profiler will monitor and manage the energy consumption in the smartphone devices. The hybrid energy-aware profiler will set up a protocol preference of both the user and the device. Then, it will estimates the energy consumption in smartphone. However, saving energy alone can contribute to the Quality of Experience (QoE) neglection, thus the proposed solution takes into account the client QoE. Even though there are several existing energy-aware profilers that have been developed to manage energy use in smartphones however, most energy-aware profilers does not consider QoE at the same time. The proposed solution consider both, the performance of the hybrid energy-aware profiler is compared with the baseline energy models against a variation of content adaptation according to the pre-defined variables. Three types of variables were determined; resolution, frame rate and energy consumption in smartphone devices. In this area, QoE subjective methods based on MOS (Mean Opinion Score) are the most commonly used approaches for defining and quantifying real video quality. Nevertheless, although these approaches have been established to consistently quantify users’ amounts of approval, they do not adequately realize which are the criteria of video attribute that important. In this paper, we conducted an experiment with a certain devices to measures user’s QoE and energy usage of video attribute in smartphone devices. Our results demonstrate that the list of possible solution is a relevant and useful video attribute that satify the users.
Volume: 8
Issue: 3
Page: 597-609
Publish at: 2017-12-01

Automatic Data Interpretation in Accounting Information Systems Based On Ontology

10.12928/telkomnika.v15i4.6414
Irvan; Institut Teknologi Bandung, Indonesia Iswandi , Iping Supriana; Institut Teknologi Bandung, Indonesia Suwardi , Nur Ulfa; Institut Teknologi Bandung, Indonesia Maulidevi
Financial transactions recorded into accounting journals based on the evidence of the transaction. There are several kinds of evidence of transactions, such as invoices, receipts, notes, memos and others.  Invoice as one of transaction receipt has many forms that it contains a variety of information.  The information contained in the invoice identified based on rules.  Identifiable information includes: invoice date, supplier name, invoice number, product ID, product name, quantity of product and total price.  In this paper, we proposed accounting ontology and Indonesian accounting dictionary. It can be used in intelligence accounting systems. Accounting ontology provides an overview of account mapping within an organization. The accounting dictionary helps in determining the account names used in accounting journals.  Accounting journal created automatically based on accounting evidence identification.  We have done a simulation of the 160 Indonesian accounting evidences, with the result of precision 86.67%, recall 92.86% and f-measure 89.67%.
Volume: 15
Issue: 4
Page: 1817-1829
Publish at: 2017-12-01

Artificial Neural Network Based Target Recognition for Marine Search

10.11591/ijeecs.v8.i3.pp616-618
Capt. V. Ramachandran
The key point of marine search and rescue is to find out and recognize the distress objects. At present, the visual search method is usually adopted to detect the ships in distress, and this method can only be used at good sea condition and visibility. In this paper, a new target detection and recognition system is proposed. The parameters of radar transmitter and echo graphics and the invariant moments of radar images are extracted as the system’s recognition features, and the system’s target classifier is based on Artificial Neural Networks (ANN). The developed recognition classifier has been tested using three kinds of target Images, the target’s features are used as the inputs of trained ANN and the outputs of networks are target classification. Sea experimental results show that the proposed method is well-clustering and with high classified accuracy.
Volume: 8
Issue: 3
Page: 616-618
Publish at: 2017-12-01

250 MHz Multiphase Delay Locked Loop for Low Power Applications

10.11591/ijece.v7i6.pp3323-3331
Shruti Suman , K. G. Sharma , P. K. Ghosh
Delay locked loop is a critical building block of high speed synchronous circuits. An improved architecture of amixed signaldelay locked loop (DLL) is presented here. In this DLL, delay cell based on single ended differential pair configuration is used for voltage controlled delay line (VCDL) implementation. This delay cell provides a high locking range with less phase noise and jitter due to differential pair configuration.For increasing the acquisition range and locking speed of the DLL, modified true single phase clock (TSPC) based phase frequency detector is used. The proposed design is implemented at 0.18 um CMOS technology and at power supply of 1.8V . It has power consumption of 1.39 mW at 125 MHz center frequency with locking range from 0.5 MHz to 250 MHz.
Volume: 7
Issue: 6
Page: 3323-3331
Publish at: 2017-12-01

Intelligent Bridge Seismic Monitoring System Based on Neuro Genetic Hybrid

10.12928/telkomnika.v15i4.6006
Reni; Universitas Riau, Indonesia Suryanita , Mardiyono; Politeknik Negeri Semarang, Indonesia Mardiyono , Azlan; Universiti Teknologi Malaysia, Malaysia Adnan
The natural disaster and design mistake can damage the bridge structure. The damage caused a severe safety problem to human. The study aims to develop the intelligent system for bridge health monitoring due to earthquake load. The Genetic Algorithm method in Neuro-Genetic hybrid has applied to optimize the acceptable Neural Network weight. The acceleration, displacement and time history of the bridge structural responses are used as the input, while the output is the damage level of the bridge. The system displays the alert warning of decks based on result prediction of Neural Network analysis. The best-predicted rate for the training, testing and validation process is 0.986, 0.99, and 0.975 respectively. The result shows the damage level prediction is agreeable to the damage actual values. Therefore, this method in the bridge monitoring system can help the bridge authorities to predict the health condition of the bridge rapidly at any given time. 
Volume: 15
Issue: 4
Page: 1830-1840
Publish at: 2017-12-01

Pairwise Sequence Alignment between HBV and HCC Using Modified Needleman Wunsch Algorithm

10.12928/telkomnika.v15i4.5813
Lailil; Brawijaya University, Indonesia Muflikhah , Edy; Brawijaya University, Indonesia Santoso
Ths paper aims to find similarity of Hepatitis B virus (HBV) and Hepatocelluler Carcinoma (HCC) DNA sequences.The similarity of sequence allignments indicates that they have similarity of chemical and physical properties. Mutation of the virus DNA in X region has potential role in HCC. It is observed using pairwise sequence alignment of genotype-A in HBV. This paper is to purpose the modified method of Needleman Wunsch algorithm for optimum global DNA sequence alignment. The main idea is to optimize filling matrix and backtracking proccess of DNA components, so that there is reduction of computational time and space complexity. This research is applied to DNA sequence of 858 hepatitis B virus and 12 carcinoma patient. There are 10,296 pairwise of DNA sequences to be aligned globally using the modified method. As a result, it is achieved high similarity of 96.547% and validity of 99.854%. There is reduction of computational time as 34.6% and space complexity as 42.52%
Volume: 15
Issue: 4
Page: 1785-1793
Publish at: 2017-12-01

Selective Green Device Discovery for Device-to-Device Communication

10.12928/telkomnika.v15i4.6686
Bhaskara; Telkom University, Indonesia Narottama , Arfianto; Telkom University, Indonesia Fahmi , Rina Pudji; Telkom University, Indonesia Astuti , Desti Madya; Telkom University, Indonesia Saputri , Nur; Telkom University, Indonesia Andini , Hurianti; Telkom University, Indonesia Vidyaningtyas , Patricius Evander; Telkom University, Indonesia Christy , Obed Rhesa; Telkom University, Indonesia Ludwiniananda , Furry; Telkom University, Indonesia Rachmawati
The D2D communication is expected to improve devices’ energy-efficiency, which has become a major requirement of the future wireless network. Before the D2D communication can be performed, the device discovery between devices must be done. The previous works usually only assumed one mode of device discovery, i.e. either use network-assisted (with network supervision) or independent (without network supervision) device. Therefore, we propose a selective device discovery for device-to-device (D2D) communication that can utilize both device discovery modes and maintain devices’ energy-efficiency. Different from previous works, our proposed method selects the best device discovery mode to get the best energy-efficiency. Moreover, to further improve the energy-efficiency, our proposed method also deployed in D2D cluster with multiple cluster heads. The proposed method selects the most suitable mode using thresholds (cluster energy consumption and new device acceptance) and cluster energy expectation. Our experiment result indicates that the proposed method provides lowest energy consumption per new accepted device while compared with schemes with full network-assisted and independent device discovery in low numbers of new device arrival (for the number of new devices arrival = 1 ~ 3).
Volume: 15
Issue: 4
Page: 1666-1676
Publish at: 2017-12-01

Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

10.12928/telkomnika.v15i4.5021
Hanung Adi; Universitas Gadjah Mada, Indonesia Nugroho , Yuli; Universitas Gadjah Mada Politeknik Caltex Riau, Indonesia Triyani , Made; Universitas Gadjah Mada Politeknik Caltex Riau, Indonesia Rahmawaty , Igi; Universitas Gadjah Mada, Indonesia Ardiyanto
Breast cancer is the most commonly diagnosed cancer among females worldwide. Computer aided diagnosis (CAD) was developed to assist radiologists in detecting and evaluating nodules so it can improve diagnostic accuracy, avoid unnecessary biopsies, reduce anxiety and control costs. This research proposes a method of CAD for breast ultrasound images based on margin and posterior acoustic features. It consists of preprocessing, segmentation using active contour without edge (ACWE) and morphological, feature extraction and classification. Texture and geometry analysis was used to determine the characteristics of the posterior acoustic and margin nodules. Support vector machines (SVM) provided better performance than multilayer perceptron (MLP). The performance of proposed method achieved the accuracy of 91.35%, sensitivity of 92.00%, specificity of 89.66%, PPV of 95.83%, NPV of 81.26% and Kappa of 0.7915. These results indicate that the developed CAD has potential to be implemented for diagnosis of breast cancer using ultrasound images.
Volume: 15
Issue: 4
Page: 1776-1784
Publish at: 2017-12-01

Vision Based Human Decoy System for Spot Cooling

10.12928/telkomnika.v15i4.7229
Tan; Universiti Tun Hussein Onn Malaysia Chun Hou , Wan; Universiti Tun Hussein Onn Malaysia Nurshazwani Wan Zakaria , Tai; Universiti Tun Hussein Onn Malaysia Sue Jing , Razali; Universiti Tun Hussein Onn Malaysia Tomari , Tee; Universiti Tun Hussein Onn Malaysia Kian Sek , Anis; Universiti Tun Hussein Onn Malaysia Azwani Muhd Suberi
This project aims to reduce the energy consumption of air conditioner usage while maintaining occupant comfort. Cooling down the unoccupied space can be considered as waste of energy. Therefore, a human decoy system is proposed to track any human in the detection area. Image contains depth data in each pixel which can be used to detect the presence of target subject as well as their position. The acquired position data is processed by using MATLAB and subsequently is transmitted to Arduino Mega using serial communication to control stepper motors. The experimental results show that the air conditioner airflow is successfully can be directed to the target human subject with average response of 0.860 seconds per movement within detection area.
Volume: 15
Issue: 4
Page: 1512-1519
Publish at: 2017-12-01

Finding Knowledge from Indonesian Traditional Medicine using Semantic Web Rule Language

10.11591/ijece.v7i6.pp3674-3682
Ridowati Gunawan , Khabib Mustofa
One of the natural resources in Indonesia is a lot of plants which can be used in healing diseases. Thosekinds of plants can be used in “Jamu”. Jamu is a name given to traditional medicine in Indonesia. Usually Jamu is composed from several plants as ingredients. Particularly, some parts of the plant like the leaves, roots, or branches have different purpose in Jamu. Nowadays the knowledge about Jamu can be known by building Ontology. Ontology can be built and developed to enrich the content. Knowledge in Ontology is built by several rules using Semantic Web Rule Language (SWRL).Knowledge gained from SWRL is easily searchable so that users can double check the results obtained.
Volume: 7
Issue: 6
Page: 3674-3682
Publish at: 2017-12-01

Detection of Infiltrate on Infant Chest X-Ray

10.12928/telkomnika.v15i4.3163
Jufriadif; Universitas Putra Indonesia YPTK, Indonesia Na'am , Johan; Gunadarma University Jakarta, Indonesia Harlan , Gunadi Widi; Universitas Putra Indonesia YPTK Padang, Indonesia Nurcahyo , Syafri; Universitas Putra Indonesia YPTK Padang, Indonesia Arlis , Sahari; Universitas Putra Indonesia YPTK Padang, Indonesia Sahari , Mardison; Universitas Putra Indonesia YPTK Padang, Indonesia Mardison , Larissa Navia; Universitas Putra Indonesia YPTK Padang, Indonesia Rani
Currently, Chest X-ray is still widely used around the world for disease examination. This is due to its low cost, low radiation and a lot of disease information. The commonly detected disease using chest x-rays is lung disease. The characteristic of this disease is infiltrate. However, the accuracy of Chest X-ray observations is still low. Therefore, this research offers a method to perform Chest X-ray image processing in clarifying the information contained therein. This research used Chest X-ray of infant patients who treated at Central Public Hospital (RSUP) Dr. M. Djamil Padang. The total of the images tested were 17 images. In these images, there were some suspected infiltrates after being analyzed by doctors. Software used was Matlab which is conducted by applying image processing method. The method used consisted of 4 parts, that was Cropping, Filtering, Detecting Edge, and Sharpening Edge. The results of the research showed that the method could clarify edge detection of the objects contained in the image, so that the infiltrate could be more easily recognized. With this easiness, it will help the doctor to remove doubts for infiltrate observations in the Infant's lungs.
Volume: 15
Issue: 4
Page: 1938-1946
Publish at: 2017-12-01

The Use of Polymer Based Gas Sensor for Detecting Formalin in Food Using Artificial Neural Network

10.12928/telkomnika.v15i4.6164
Budi; Universitas Muria Kudus, Indonesia Gunawan , Arief; Universitas Jenderal Soedirman Grendeng, Indonesia Sudarmaji
The usage of formalin as preservative substance in food is dangerous and make much threat to public society. Yet, it is difficult to identify the presence of formalin in food sensory. It commonly requires laboratory-based testing to detect the formalin. This work describes a detector system of formalin presence in food which employs a series of polymer-based gas sensor and uses a neural network detection method. The sensors are the polymer-carbon composite which made of the polymer mixed with active carbon. There are four types of polymer used, i.e. Polyethylene Glycol (PEG) 6000, PEG200, PEG20M, and PEG1450. The polymer-carbon composite provided a unique characteristic when it is exposed to vapor of food with or without formalin. The resistance of each polymer is different for each detected vapor. The combination of those sensors gives a pattern of voltage output on the sensors when they are exposed certain gas so that every gas has its unique output pattern. The method of detection uses an algorithm of back-propagation of the neural network. That voltage pattern of sensors serves as input to an artificial intelligence program. The result shows that the system has the accuracy of 75% in detecting formalin in food.
Volume: 15
Issue: 4
Page: 1641-1650
Publish at: 2017-12-01
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