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                  <text>VOL. 18, NO. 2 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Enabling relay selection in non-orthogonal multiple access networks: direct and relaying mode</text>
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                <text>Non-orthogonal multiple access, (NOMA), Outage probability, Relay selection</text>
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                <text>In this paper, we consider downlink non-orthogonal multiple access&#13;
(NOMA) in which the relay selection (RS) scheme is enabled for cooperative networks. In particular, we investigate impact of the number of relays on system performance in term of outage probability. The main factors affecting on cooperative NOMA performance are fixed power allocations coefficients and the number of relay. This paper also indicate performance gap of the outage probabilities among two users the context of NOMA. To exhibit the exactness of derived formula, we match related results between simulation and analytical methods. Numerical results confirms that cooperative NOMA networks benefit from increasing the number of relay. </text>
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                <text>Dinh-Thuan Do, Minh-Sang V. Nguyen</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i2.13313</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>VOL. 18, NO. 2 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
The prediction of mobile data traffic based on the ARIMA model and disruptive formula in industry 4.0: A case study in Jakarta, Indonesia</text>
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                <text>Capacity planning, Disruptive formula, Industry 4.0, IoT, Prediction methods</text>
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                <text>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:&#13;
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. </text>
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                <text>Ajib Setyo Arifin, Muhammad Idham Habibie</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i2.12989</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>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. 2 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Outage and throughput performance of cognitive radio based power domain based multiple access</text>
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                <text>MRC, Outage probability, Power domain based multiple access, SC</text>
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                <text>This paper considers power domain based multiple access (PDMA)&#13;
in cognitive radio network to serve numerous users who intend to multiple access to core network. In particular, we investigate the effect of signal combination scheme equipped at PDMA end-users as existence of direct link and relay link. This system model using relay scheme provides performance improvement on the outage probability of two PDMA end-users. We first propose a simple scheme of fixed power allocation to PDMA users who exhibit performance gap and fairness. Inspired by PDMA strategy, we then find signal to noise ratio (SNR) to detect separated signal for each user. In addition, the exact expressions of outage probability are derived in assumption that receiver can cancel out the interference completely with successive interference cancellation (SIC). By exploiting theoretical and simulation results, both considered combination schemes (Maximal Ratio Combining (MRC) and Selection Combining (SC) can achieve improved performance of two PDMA users significantly. </text>
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                <text>Dinh-Thuan Do, Chi-Bao Le</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i2.12731</text>
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                <text>Sri Wahyuni</text>
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                  <text>VOL. 18, NO. 2 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Glasses for the blind using ping ultrasonic, ATMEGA8535 and ISD25120</text>
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                <text>Glasses, ISD25120, Microcontrollers, Ultrasonic sensor, Voice</text>
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                <text>For doing their activities, blind people need tools. The idea for designing this device is for helping the blind person. This device is a glasses specially for blind person which gives information in the form of voices through an earphone if there is an obstacle in the range of 0–58 cm. The device is using PING ultrasonic sensor, ATMEGA8535 microcontroller, and ISD25120 for recording and saving the voices. After designing and making the device, we tested the device to take the data. We use three PING ultrasonic sensors which are put at the sides of the glasses. The device will give eight different kinds of voices through the earphone. The voices depend upon the output of the PING ultrasonic sensors. </text>
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                <text>Hartono Siswono, Widyastuti</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i2.12419</text>
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            <name>Publisher</name>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>April 2020</text>
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                <text>Sri Wahyuni</text>
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            <name>Rights</name>
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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>Microcontrollers</name>
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        <name>Ultrasonic sensor</name>
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                  <text>VOL. 18, NO. 2 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Feature extraction of Jabon (Anthocephalus sp) leaf disease using discrete wavelet transform&#13;
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                <text>Feature extraction, Jabon, Leaf disease, Wavelet, Wavelet extraction</text>
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                <text>Jabon (Anthocephalus cadamba (Roxb.) Miq) is one type of forest plants that have very rapid growth until the process of the harvest. One inhibitor is a disease that attacks the leaves in the form of spots and blight that can cause death during the growth process of this tree. The purpose of this process is to detect the object of diseases that attack the leaves of jabon at the time in the nursery. Images of affected jabon leaf disease segmented by reducing the RGB color cylinders to separate the disease object from the background. Reduced channel G-R provides information in the form of disease areas contained in the image of Jabon leaf. Furthermore, the characteristics of leaf disease can be detected well using DWT in the 3-level decomposition process with SVM classification results that can separate both classes of spots and blight by 84.672%. </text>
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                <text>Felliks Feiters Tampinongkol, Yeni Herdiyeni, Elis Nina Herliyana</text>
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              <elementText elementTextId="37727">
                <text>DOI: 10.12928/TELKOMNIKA.v18i2.10714</text>
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            <name>Publisher</name>
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                <text>Universitas Ahmad Dahlan</text>
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                <text>April 2020</text>
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              <elementText elementTextId="37730">
                <text>Sri Wahyuni</text>
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          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
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              <elementText elementTextId="37731">
                <text>ISSN: 1693-6930</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="46">
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              <elementText elementTextId="37732">
                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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          <element elementId="42">
            <name>Format</name>
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              <elementText elementTextId="37733">
                <text>PDF</text>
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                <text>English</text>
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                <text>Text</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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        <name>Feature extraction</name>
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        <name>Jabon</name>
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        <name>Jurnal Nasional Informatika</name>
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        <name>Leaf disease</name>
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        <name>Wavelet</name>
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        <name>Wavelet extraction</name>
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