Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

29,922 Article Results

The prediction of mobile data traffic based on the ARIMA model and disruptive formula in industry 4.0: A case study in Jakarta, Indonesia

10.12928/telkomnika.v18i2.12989
Ajib Setyo; Universitas Indonesia Arifin , Muhammad Idham; Universitas Indonesia Habibie
Disruptive technologies, which are caused by the cellular evolution including the Internet of Things (IoT), have significantly contributed data traffic to the mobile telecommunication network in the era of Industry 4.0. These technologies cause erroneous predictions prompting mobile operators to upgrade their network, which leads to revenue loss. Besides, the inaccuracy of network prediction also creates a bottleneck problem that affects the performance of the telecommunication network, especially on the mobile backhaul. We propose a new technique to predict more accurate data traffic. This research used a univariate Autoregressive Integrated Moving Average (ARIMA) model combined with a new disruptive formula. Another model, called a disruptive formula, uses a judgmental approach based on four variables: Political, Economic, Social, Technological (PEST), cost, time to market, and market share. The disruptive formula amplifies the ARIMA calculation as a new combination formula from the judgmental and statistical approach. The results show that the disruptive formula combined with the ARIMA model has a low error in mobile data forecasting compared to the conventional ARIMA. The conventional ARIMA shows the average mobile data traffic to be 49.19 Mb/s and 156.93 Mb/s for the 3G and 4G, respectively; whereas the ARIMA with disruptive formula shows more optimized traffic, reaching 56.72 Mb/s and 199.73 Mb/s. The higher values in the ARIMA with disruptive formula are closest to the prediction of the mobile data forecast. This result suggests that the combination of statistical and computational approach provide more accurate prediction method for the mobile backhaul networks.
Volume: 18
Issue: 2
Page: 907-918
Publish at: 2020-04-01

Evaluate the performance of K-Means and the fuzzy C-Means algorithms to formation balanced clusters in wireless sensor networks

10.11591/ijece.v10i2.pp1515-1523
Ali Abdul-hussian Hassan , Wahidah Md Shah , Mohd Fairuz Iskandar Othman , Hayder Abdul Hussien Hassan
The clustering approach is considered as a vital method for wireless sensor networks (WSNs) by organizing the sensor nodes into specific clusters. Consequently, saving the energy and prolonging network lifetime which is totally dependent on the sensors battery, that is considered as a major challenge in the WSNs. Classification algorithms such as K-means (KM) and Fuzzy C-means (FCM), which are two of the most used algorithms in literature for this purpose in WSNs. However, according to the nature of random nodes deployment manner, on certain occasions, this situation forces these algorithms to produce unbalanced clusters, which adversely affects the lifetime of the network. Based for our knowledge, there is no study has analyzed the performance of these algorithms in terms clusters construction in WSNs. In this study, we investigate in KM and FCM performance and which of them has better ability to construct balanced clusters, in order to enable the researchers to choose the appropriate algorithm for the purpose of improving network lifespan. In this study, we utilize new parameters to evaluate the performance of clusters formation in multi-scenarios. Simulation result shows that our FCM is more superior than KM by producing balanced clusters with the random distribution manner for sensor nodes.
Volume: 10
Issue: 2
Page: 1515-1523
Publish at: 2020-04-01

Joint control of a robotic arm using particle swarm optimization based H2/H∞ robust control on arduino

10.12928/telkomnika.v18i2.14749
Petrus; Politeknik Mekatronika Sanata Dharma Sutyasadi , Martinus Bagus; Politeknik Mekatronika Sanata Dharma Wicaksono
This paper proposes a small structure of robust controller to control robotic arm’s joints where exist some uncertainties and unmodelled dynamics. Robotic arm is widely used now in the era of Industry 4.0. Nevertheless, the cost for an industry to migrate from a conventional automatic machine to industrial robot still very high. This become a significant challenge to middle or small size industry. Development of a low cost industrial robotic arm can be one of good solutions for them. However, a low-cost manipulator can bring more uncertainties. There might be exist more unmodelled dynamic in a low-cost system. A good controller to overcome such uncertainties and unmodelled dynamics is robust controller. A low-cost robotic arm might use small or medium size embedded controller such as Arduino. Therefore, the control algorithm should be a small order of controller. The synthesized controller was tested using MATLAB and then implemented on the real hardware to control a robotic manipulator. Both the simulation and the experiment showed that the proposed controller performed satisfactory results. It can control the joint position to the desired position even in the presence of uncertainties such as unmodelled dynamics and variation of loads or manipulator poses.
Volume: 18
Issue: 2
Page: 1021-1029
Publish at: 2020-04-01

High level speaker specific features modeling in automatic speaker recognition system

10.11591/ijece.v10i2.pp1859-1867
Satyanand Singh
Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.
Volume: 10
Issue: 2
Page: 1859-1867
Publish at: 2020-04-01

Matching algorithm performance analysis for autocalibration method of stereo vision

10.12928/telkomnika.v18i2.14842
Raden Arief; Brawijaya University Setyawan , Rudy; Brawijaya University Soenoko , Moch Agus; Brawijaya University Choiron , Panca; Brawijaya University Mudjirahardjo
Stereo vision is one of the interesting research topics in the computer vision field. Two cameras are used to generate a disparity map, resulting in the depth estimation. Camera calibration is the most important step in stereo vision. The calibration step is used to generate an intrinsic parameter of each camera to get a better disparity map. In general, the calibration process is done manually by using a chessboard pattern, but this process is an exhausting task. Self-calibration is an important ability required to overcome this problem. Self-calibration required a robust and good matching algorithm to find the key feature between images as reference. The purpose of this paper is to analyze the performance of three matching algorithms for the autocalibration process. The matching algorithms used in this research are SIFT, SURF, and ORB. The result shows that SIFT performs better than other methods.
Volume: 18
Issue: 2
Page: 1105-1112
Publish at: 2020-04-01

Noninvasive blood glucose monitoring system based on near-infrared method

10.11591/ijece.v10i2.pp1736-1746
Mustafa Ayesh Al-dhaheri , Nasr-Eddine Mekkakia-Maaza , Hassan Mouhadjer , Abdelghani Lakhdari
Diabetes is considered one of the life-threatening diseases in the world which need continuous monitoring to avoid the complication of diabetes. There is a need to develop a non-invasive monitoring system that avoids the risk of infection problems and pain caused by invasive monitoring techniques. This paper presents a method for developing a noninvasive technique to predict the blood glucose concentration (BCG) based on the Near-infrared (NIR) light sensor. A prototype is developed using a finger sensor based on LED of 940 nm wavelength to collect photoplethysmography (PPG) signal which is variable depending on the glucose concentration variance, a module circuit to preprocess PPG signals is realized, which includes an amplifier and analog filter circuits, an Arduino UNO is used to analog-to-digital conversion. A digital Butterworth filterer is used to remove PPG signal trends, then detect the PPG data peaks to determine the relationship between the PPG signal and (BCG) and use it as input parameters to build the calibration model based on linear regression. Experiments show that the Root Mean Squares Error (RMSE) of the prediction is between 8.264mg/dL and 13.166 mg/dL, the average of RMSE is about 10.44mg/dL with a correlation coefficient (R^2) of 0.839, it is observed that the prediction of glucose concentration is in the clinically acceptable region of the standard Clark Error Grid (CEG).
Volume: 10
Issue: 2
Page: 1736-1746
Publish at: 2020-04-01

Short-term photovoltaics power forecasting using Jordan recurrent neural network in Surabaya

10.12928/telkomnika.v18i2.14816
Aji Akbar; Universitas Airlangga Firdaus , Riky Tri; Universitas Airlangga Yunardi , Eva Inaiyah; Universitas Airlangga Agustin , Tesa Eranti; Universitas Airlangga Putri , Dimas Okky; Politeknik Elektronika Negeri Surabaya Anggriawan
Photovoltaic (PV) is a renewable electric energy generator that utilizes solar energy. PV is very suitable to be developed in Surabaya, Indonesia. Because Indonesia is located around the equator which has 2 seasons, namely the rainy season and the dry season. The dry season in Indonesia occurs in April to September. The power generated by PV is highly dependent on temperature and solar radiation. Therefore, accurate forecasting of short-term PV power is important for system reliability and large-scale PV development to overcome the power generated by intermittent PV. This paper proposes the Jordan recurrent neural network (JRNN) to predict short-term PV power based on temperature and solar radiation. JRNN is the development of artificial neural networks (ANN) that have feedback at each output of each layer. The samples of temperature and solar radiation were obtained from April until September in Surabaya. From the results of the training simulation, the mean square error (MSE) and mean absolute percentage error (MAPE) values were obtained at 1.3311 and 34.8820, respectively. The results of testing simulation, MSE and MAPE values were obtained at 0.9858 and 1.3311, with a time of 4.591204. The forecasting has minimized significant errors and short processing times.
Volume: 18
Issue: 2
Page: 1089-1094
Publish at: 2020-04-01

P-D controller computer vision and robotics integration based for student’s programming comprehension improvement

10.12928/telkomnika.v18i2.14881
Nova Eka; Universitas Katolik Indonesia Atma Jaya Budiyanta , Catherine Olivia; Universitas Katolik Indonesia Atma Jaya Sereati , Lukas; Universitas Katolik Indonesia Atma Jaya Lukas
The 21st-century skills needed to face the speed of understanding technology. Such as critical thinking in computer vision and robotics literacy, any student is hampered by the programming that is considered complicated. This study aims at the improvement of student embedded system programming competency with computer vision and mobile robotics integration approach. This method is proposed to attract the students to learn about embedded system programming by delivering integration between computer vision and robotics using the P-D controller since both of the fields are closely related. In this paper, the researcher described computer vision programming to get the data of captured images through the camera stream and then delivered the data into an embedded system to make the decision of robot movement. The output of this study is the improvement of a student’s ability to make an application to integrate a sensor system using a camera and the mobile robot running follow the line. The result of the test shows that the integration method between computer vision and robotics can improve the student’s programming comprehension by 40%. Based on the Feasibility test survey, it can be interpreted that from the whole assessment after being converted to qualitative data, all aspects of the learning stages of programming application tested with the integration of computer vision and robotics fall into the very feasible category for used with a percentage of feasibility by 77.44%.
Volume: 18
Issue: 2
Page: 899-906
Publish at: 2020-04-01

Sentiment analysis by deep learning approaches

10.12928/telkomnika.v18i2.13912
Sreevidya; Amrita Viswa Vidyapeetham P. , O.; Amrita Viswa Vidyapeetham V. Ramana Murthy , S.; Amrita Viswa Vidyapeetham Veni
We propose a model for carrying out deep learning based multimodal sentiment analysis. The MOUD dataset is taken for experimentation purposes. We developed two parallel text based and audio basedmodels and further, fused these heterogeneous feature maps taken from intermediate layers to complete thearchitecture. Performance measures–Accuracy, precision, recall and F1-score–are observed to outperformthe existing models.
Volume: 18
Issue: 2
Page: 752-760
Publish at: 2020-04-01

Enhance manet usability for encrypted data retrieval from cloud computing

10.11591/ijeecs.v18.i1.pp64-74
Fairouz Sher Ali , Hadeel Noori Saad , Falah Hassan Sarhan , Bushra Naaeem
Cloud computing has become a revolutionary computing model which provides an economical and flexible strategy for resource sharing and data management. Due to privacy concerns, sensitive data has to be encrypted before being uploaded to the cloud servers. Over the last few years, several keyword searchable encryption works have been described in the literature. However, existing works mostly focus on secure searching using keyword and only retrieve Boolean results that are not yet adequate. On the other hand, poor-resources of mobile networks play an important role on all applications area nowadays. Mobile nodes mostly act as information retrieval end which make it important to address this problem. In this paper, we present a secure keyword search scheme based on the Bloom filter(SKS-BF), which enhances the system’s usability by allowing ranking based on the relevance score of the search results and retrieves the top most relevant files instead of retrieving all the files. Further, the Bloom filter (BFs) can accelerate a search process involving a large number of keywords. Extensive experiments and network simulation confirm the efficiency of our proposed schemes.
Volume: 18
Issue: 1
Page: 64-74
Publish at: 2020-04-01

Developed third iterative dichotomizer based on feature decisive values for educational data mining

10.11591/ijeecs.v18.i1.pp209-217
Saja Taha Ahmed , Rafah Al-Hamdani , Muayad Sadik Croock
Recently, the decision trees have been adopted among the preeminent utilized classification models. They acquire their fame from their efficiency in predictive analytics, easy to interpret and implicitly perform feature selection. This latter perspective is one of essential significance in Educational Data Mining (EDM), in which selecting the most relevant features has a major impact on classification accuracy enhancement. The main contribution is to build a new multi-objective decision tree, which can be used for feature selection and classification. The proposed Decisive Decision Tree (DDT) is introduced and constructed based on a decisive feature value as a feature weight related to the target class label. The traditional Iterative Dichotomizer 3 (ID3) algorithm and the proposed DDT are compared using three datasets in terms of some ID3 issues, including logarithmic calculation complexity and multi-values featuresselection. The results indicated that the proposed DDT outperforms the ID3 in the developing time. The accuracy of the classification is improved on the basis of 10-fold cross-validation for all datasets with the highest accuracy achieved by the proposed method is 92% for the student.por dataset and holdout validation for two datasets, i.e. Iraqi and Student-Math. The experiment also shows that the proposed DDT tends to select attributes that are important rather than multi-value. 
Volume: 18
Issue: 1
Page: 209-217
Publish at: 2020-04-01

Improved predictive current model control based on adaptive PR controller for standalone system based DG set

10.11591/ijece.v10i2.pp1905-1914
Halima Ikaouassen , Abderraouf Raddaoui , Miloud Rezkallah , Hussein Ibrahim
This paper investigates an improved current predictive model control (PCMC) strategy with a prediction horizon of one sampling time for voltage regulation in standalone system based on diesel engine driven fixed speed of a synchronous generator. An adaptive PR controller with anti-windup scheme is employed to achieve high performance regulation without saturation issues. In addition, new method to obtain the optimal parameters of the adaptive PR controller to achieve high performance during the transition and in steady state is provided. To balance the power at the point of common coupling (PCC) as well as to feed a clean power to the connected loads, a three-phase voltage source inverter (VSI) with LRC filter is controlled using the developed improved PCMC strategy, where the output filter current is controlled using the predicting of the system behaviour model in the future step, at each sampling prediction time. The performances of the proposed configuration and the improved control strategy are verified using Matlab/Simulink interface.
Volume: 10
Issue: 2
Page: 1905-1914
Publish at: 2020-04-01

Robust foreground modelling to segment and detect multiple moving objects in videos

10.11591/ijece.v10i2.pp1337-1345
Rahul M. Patil , Chethan K. P. , Azra Nasreen , Shobha G.
Last decade has witnessed an ever increasing number of video surveillance installations due to the rise of security concerns worldwide. With this comes the need for video analysis for fraud detection, crime investigation, traffic monitoring to name a few. For any kind of video analysis application, detection of moving objects in videos is a fundamental step. In this paper, an efficient foreground modelling method to segment multiple moving objects is implemented. Proposed method significantly reduces noise thereby accurately segmenting region of interest under dynamic conditions while handling occlusion to a large extent. Extensive performance analysis shows that the proposed method was found to give far better results when compared to the de facto standard as well as relatively new approaches used for moving object detection.
Volume: 10
Issue: 2
Page: 1337-1345
Publish at: 2020-04-01

Artifact elimination in ECG signal using wavelet transform

10.12928/telkomnika.v18i2.14403
Thanh-Nghia; HCMC University of Technology and Education Nguyen , Thanh-Hai; HCMC University of Technology and Education Nguyen , Van-Thuyen; HCMC University of Technology and Education Ngo
Electrocardiogram signal is the electrical actvity of the heart and doctors can diagnose heart disease based on this electrocardiogram signal. However, the electrocardiogram signals often have noise and artifact components. Therefore, one electrocardiogram signal without the noise and artifact plays an important role in heart disease diagnosis with more accurate results. This paper proposes a wavelet transform with three stages of decomposition, filter, and reconstruction for eliminating the noise and artifact in the electrocardiogram signal. The signal after decomposing produces approximation and detail coefficients, which contains the frequency ranges of the noise and artifact components. Hence, the approximation and detail coefficients with the frequency ranges corresponding to the noise and artifact in the electrocardiogram signal are eliminated by filters before they are reconstructed. For the evaluation of the proposed algorithm, filter evaluation metrics are applied, in which signal-to-noise ratio and mean squared error along with power spectral density are employed. The simulation results show that the proposed wavelet algorithm at level 8 is effective, in which the with the “dmey” wavelet function was selected be the best based power spectrum density.
Volume: 18
Issue: 2
Page: 936-944
Publish at: 2020-04-01

Influence of annealing temperature on the sensitivity of nickel oxide nanosheet films in humidity sensing applications

10.11591/ijeecs.v18.i1.pp284-292
N. Parimon , M. H. Mamat , A. S. Ismail , I. B. Shameem Banu , M. K. Ahmad , A. B. Suriani , M. Rusop
Nickel oxide (NiO) nanosheet films were successfully grown onto NiO seed-coated glass substrates at different annealing temperatures for humidity sensing applications. NiO seed layers and NiO nanosheet films were prepared using a sol-gel spin coating and sonicated sol-gel immersion techniques, respectively. The properties of NiO nanosheet films at as-deposited, 300 ℃, and 500 ℃-annealed were examined by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), ultraviolet-visible (UV-vis) spectroscopy, and humidity sensor measurement system. The XRD patterns demonstrate that the grown NiO films have crystalline cubic structures at temperature of 300 ℃ and 500 ℃. The FESEM images show that the large porous nanosheet network spread over the layers as the annealing temperature increased. The UV-vis spectra revealed that all the nanosheet films have the average transmittance below than 50% in the visible region, with absorption edges ~ 350 nm. The optical band gap energy was evaluated in ranges of 3.39 to 3.61 eV. From the obtained humidity sensing results, it shows that 500 ℃-annealed film exhibited the best sensitivity of 257, as well as the slowest response time, and the fastest recovery time compared with others.
Volume: 18
Issue: 1
Page: 284-292
Publish at: 2020-04-01
1 2 3
...
1994 1995
Show 1158 of 1995

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration