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                  <text>VOL. 18, NO. 4 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Medium term load demand forecast of Kano zone using neural network algorithms </text>
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                <text>Capability, Layer, Load, Neural network, Weight</text>
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                <text>Electricity load forecasting refers to projection of future load requirements of an area or region or country through appropriate use of historical load data. One of several challenges faced by the Nigerian power distribution sectors is the overloaded power distribution network which leads to poor voltage distribution and frequent power outages. Accurate load demand forecasting is a key in addressing this challenge. This paper presents a comparison of generalized regression neural network (GRNN), feed-forward neural network (FFNN) and radial basis function neural network for medium term load demand estimation. Experimental data from Kano electricity distribution company (KEDCO) were used in validating the models. The simulation results indicated that the neural network models yielded promising results having achieved a mean absolute percentage error (MAPE) of less than 10% in all the considered scenarios. The generalization capability of FFNN is slightly &#13;
better than that of RBFNN and GRNN model. The models could serve as a valuable and promising tool for the forecasting of the load demand. </text>
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                <text>Huzaimu Lawal Imam, Muhammad Sani Gaya, G. S. M. Galadanci</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i4.14032</text>
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            <name>Publisher</name>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>August 2020</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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        <name>Capability</name>
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        <name>Layer</name>
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        <name>Load</name>
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                  <text>VOL. 18, NO. 4 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Green coffee beans feature extractor using image processing</text>
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            <description>The topic of the resource</description>
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                <text>Image processing, New edge detection algorithm, Prewitt, Roberts,  Sobel</text>
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                <text>This study offers a novel solution to deal with the low signal-to-noise ratio and slow execution rate of the first derivative edge detection algorithms namely, Roberts, Prewitt and Sobel algorithms. Since the two problems are brought about by the complex mathematical operations being used by the algorithms, these were replaced by a discriminant. The developed discriminant, equivalent to the product of total difference and intensity divided by the normalization values, is based on the “pixel pair formation” that produces optimal peak signal to noise ratio. Results of the study applying the discriminant for the edge detection of green coffee beans shows improvement in terms of peak signal to noise ratio (PSNR), mean square error (MSE), and execution time. It was determined that accuracy level varied according to the total difference of pixel values, intensity, and normalization values. Using the developed edge detection technique led to improvements in the PSNR of 2.091%, 1.16 %, and 2.47% over Sobel, Prewitt, and Roberts respectively. Meanwhile, improvement in the MSE was measured to be 13.06%, 7.48 %, and 15.31% over the three algorithms. Likewise, improvement in execution time was also achieved at values of 69.02%, 67.40 %, and 65.46% over Sobel, Prewitt, and Roberts respectively.. </text>
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            <description>An entity primarily responsible for making the resource</description>
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                <text>Edwin R. Arboleda, Arnel C. Fajardo, Ruji P. Medina</text>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="41182">
                <text>DOI: 10.12928/TELKOMNIKA.v18i4.13968</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="41183">
                <text>Universitas Ahmad Dahlan</text>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>August 2020</text>
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                <text>Sri Wahyuni</text>
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
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                <text>ISSN: 1693-6930</text>
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                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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                  <text>VOL. 18, NO. 4 2020</text>
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            <description>A name given to the resource</description>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control&#13;
 A simplified spatial modulation MISO-OFDM scheme </text>
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            <description>The topic of the resource</description>
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                <text>Index modulation, MISO-OFDM-SM, OFDM, Spatial modulation (SM)</text>
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                <text>Index modulation is one of the promising techniques for future&#13;
communications systems due to many improvement over the classical&#13;
orthogonal frequency division multiplexing systems such as single RF chain, increased throughput for the same modulation order, achieved tradeoff between the efficiencies of the power and the spectral, and elimination of inter-channel interference. Many forms of index modulation researches exist where symbols are conveyed in antennas, subcarriers, time slots, and the space-time matrix. Spatial modulation is one member of index modulation family where symbols are conveyed in activating transmit/receive antennas. In this paper, a modification to a standard multiple input single output scheme by integrating spatial modulation using simplified mathematical procedure is achieved. In the transmitter side, data and activation symbols are distributed simultaneously using mathematical module and floor functions. At the receiver, a simplified maximum likelihood detector is used to obtain transmitted pair of symbols. To verify this, MATLAB simulink is used to simulate a downlink system where spatial modulation is applied to a base station. Results for different transmit antenna number and  modulation order are obtained in the form of bit error rate versus signal to noise ratio. </text>
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                <text>Vian S. Al-Doori, Emad H. Al-Hemiary</text>
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            <description>A related resource from which the described resource is derived</description>
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              <elementText elementTextId="41168">
                <text>DOI: 10.12928/TELKOMNIKA.v18i4.13873</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>August 2020</text>
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                <text>Sri Wahyuni</text>
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            <description>Information about rights held in and over the resource</description>
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                <text>ISSN: 1693-6930</text>
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                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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        <name>Index modulation</name>
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        <name>Jurnal Nasional Informatika</name>
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        <name>MISO-OFDM-SM</name>
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        <name>OFDM</name>
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        <name>Spatial modulation (SM)</name>
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                  <text>VOL. 18, NO. 4 2020</text>
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            <description>A name given to the resource</description>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Spatial association discovery process using frequent subgraph mining</text>
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          <element elementId="49">
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            <description>The topic of the resource</description>
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                <text>Frequent subgraph mining, SARM, Spatial association mining, Spatial data mining, Spatial knowledge discovery</text>
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                <text>Spatial associations are one of the most relevant kinds of patterns used by business intelligence regarding spatial data. Due to the characteristics of this particular type of information, different approaches have been proposed for spatial association mining. This wide variety of methods has entailed the need for a process to integrate the activities for association discovery, one that is easy to implement and flexible enough to be adapted to any particular situation, particularly for small and medium-size projects to guide the useful pattern discovery process. Thus, this work proposes an adaptable knowledge discovery process that uses graph theory to model different spatial relationships from multiple scenarios, and frequent subgraph mining to discover spatial associations. A proof of concept is presented using real data. </text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i4.13858</text>
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 Enhancement of student performance prediction using modified K-nearest neighbor </text>
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                <text>The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student&#13;
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                <text>Saja Taha Ahmed, Rafah Al-Hamdani, Muayad Sadik Croock</text>
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            <elementTextContainer>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i4.13849</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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Na3Ce(PO4)2:Tb3+ and Na(Mg2–xMnX)LiSi4O10F2:Mn phosphors: a suitable selection for enhancing color quality and luminous flux of remote white light-emitting diodes </text>
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                <text>Color quality scale, Luminous flux, Na(Mg2–xMnX)LiSi4O10F2:Mn, Na3Ce(PO4)2:Tb3+, Remote phosphor structure</text>
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                <text>This study proposed the TRP, a remote phosphor structure that has 3 phosphor layers, to ehance the chromatic quality and lumen output for white light-emitting diodes devices (WLEDs). The arrangment of phosphor layers is yellow YAG:Ce3+ phosphor, green Na3Ce(PO4)2:Tb3+ phosphor, and red Na(Mg2–xMnX)LiSi4O10F2:Mn phosphor from bottom to top. Red Na(Mg2–xMnX)LiSi4O10F2:Mn phosphor is used for the red light component to boost color rendering index (CRI). The green layer Na3Ce(PO4)2:Tb3+ phosphor is utilized for the green light component to produce higher luminous flux (LF). With the addition of red and green phosphor, the yellow YAG:Ce3+ concentration must decrease to maintain the 6000 K color temperature. The research results show that red phosphor Na(Mg2–xMnX)LiSi4O10F2:Mn concentration is beneficial for CRI, while green&#13;
phosphor Na3Ce(PO4)2:Tb3+ is detrimental to CRI. Morever, CQS reaction with red and green phosphor is also studied, which show notable improvement when Na(Mg2–xMnX)LiSi4O10F2:Mn concentration is from 10-14%, regardless of Na3Ce(PO4)2:Tb3+. The luminous flux (LF) can also increase for more than 40% with the reduced light loss and added green phosphor. Research results are valuable references for producers to enhance the color quality and the light&#13;
emission of WLEDs. </text>
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                <text>Nguyen Thi Phuong Loan, Nguyen Doan Quoc Anh</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i4.13723</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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Outage probability analysis in DF power-splitting full-duplex relaying network with impact of Co-channel interference at the destination</text>
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                <text>Nowadays, improving the WPCN efficiency problem is the leading research direction in the communication network. In this research, the outage probability (OP) analysis in DF power-splitting (PS) full-duplex (FD) Relaying Network with Impact of Co-channel interference at the destination is proposed and investigated. In the system model section, we present the DF PS FD Relaying Network with Impact of Co-channel interference at the destination. Then in the system performance section, we analyze and derive the closed-form expression of the OP and investigate the effect of the main system parameters on the system network performance. Then, we perform the Monte Carlo simulation to verify the analytical section. This research can provide a new recommendation for the communication network. </text>
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                <text>Phu Tran Tin, Duy Hung Ha, Minh Tran</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i4.13716</text>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>Sri Wahyuni</text>
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                <text>ISSN: 1693-6930</text>
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                <text>This research associates three distinct approaches for computer network traffic prediction. They are the traditional stochastic gradient descent (SGD) using a few random samplings instead of the complete dataset for each iterative calculation, the gradient descent algorithm (GDA) which is a well-known optimization approach in deep learning, and the proposed method. The network traffic is computed from the traffic load (data and multimedia) of the computer network nodes via the Internet. It is apparent that the SGD is a modest iteration but can conclude suboptimal solutions. The GDA is a complicated one, can function more accurate than the SGD but difficult to manipulate parameters, such as the learning rate, the dataset granularity, and the loss function. Network traffic estimation helps improve performance&#13;
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