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 Comparing random forest and support vector machines for breast cancer classification</text>
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                <text>There are more than 100 types of cancer around the world with different symptoms and difficulty in predicting its appearance in a person due to its random and sudden attack method. However, the appearance of cancer is generally marked by the growth of some abnormal cell. Someone might be diagnosed early and quickly treated, but the cancerous cell most times hides in the body of its victim and reappear, only to kill its sufferer. One of the most common cancers is breast cancer. According to Ministry of Health, in 2018, breast cancer attacked 42 out of every 100.000 people in Indonesia with approximately 17 deaths. In addition, the Ministry recorded a yearly increase in cancer patients. Therefore, there is adequate need to be able to determine those affected by this disease. This study applied the Boruta feature selection to determine the most important features in making a machine learning model. Furthermore, the Random Forest (RF) and Support Vector Machines (SVM) were the machine learning model used, with highest accuracies of 90% and 95% respectively. From the results obtained, the SVM is a better model than random forest in terms of accuracy. </text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i2.14785</text>
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 Technology readiness and usability of office automation system in suburban areas</text>
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                <text>Readiness and usability issues are important attributes of an information system (IS) success. Currently, IS studies that have been assessing the issues in suburban areas are still limited. The purpose of this study was to measure the above-mentioned phenomena by combining the readiness and usability models. The proposed model consisted out 10 constructs with 25 indicators. The four constructs were from the readiness model and the six ones were the usability model. The 89 samples were collected using an online survey based on purposive random sampling. Partial least squares structural equation modeling (PLS-SEM) method with SmartPLS 3.2.8 was used for analyzing the data. The findings may have contributed to the IS research field, in terms of the readiness and usability issues on the IS use in a suburban area in Indonesia. </text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
A compact UWB monopole antenna with penta band notched characteristics</text>
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                <text>A modified rectangular monopole ultra-wideband (UWB) antenna with&#13;
penta notched frequency bands is presented. An inverted U shaped and&#13;
slanted U-shaped on the radiating patch are inserted to achieve WiMAX and ARN bands rejection respectively, two mirrored summation Σ-shaped and four mirrored 5-shaped slots are inserted on the partial ground to achieve WLAN and X-band bands rejection respectively, finally rectangular shaped slot with partially open on the feed is inserted to achieve ITU-8 band rejection. The proposed antenna which was simulated has a compact size 30×35×1.6 m3. It is operated with impedance bandwidth 2.8-10.6 GHz at |S11| &lt; −10 dB, that supported UWB bandwidth with filtering the five narrowbands that avoid the possible interference with them. The simulated resonant frequency for notched filters received 3.55, 4.55, 5.53, 7.45, 8.16 GHZ, for WiMAX, ARN, WLAN, X-Band, ITU-8 respectively. The proposed antenna is suitable for wireless communication such as mobile communication and internet of everything (IoE). Throughout this paper, CST-EM software package was used for the design implementation. Surface current distributions for all notched filters were investigated and shown that it is concentrated around the feeding point and the inserted notched slots proving that there is no radiation to the space due to maximum stored electromagnetic energy around each investigated notch slot, proving that the slots play a role of a quarter wavelength transformer which&#13;
generates for each notched band, maximum gain, and radiation pattern are also investigated.</text>
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                <text>Majed O. Al-Dwairi, Amjad Y. Hindi, Mohamed S. Soliman, Mohammad F. Aljafari</text>
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                <text>DOI: 10.12928/TELKOMNIKA.v18i2.14542</text>
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                  <text>VOL. 18, NO. 2 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
A new multi-level key block cypher based on the Blowfish algorithm</text>
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            <description>The topic of the resource</description>
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                <text>Blowfish algorithm, Encryption security, Multi-level keys, Symmetric block cipher</text>
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                <text>Blowfish is a block cypher algorithm used in many applications to enhance security, but it includes several drawbacks. For example, the mix between the key and data is limited. This paper presents a new modification to the Blowfish algorithm to overcome such problems realised through a multi-state operation instead of an XOR. Our proposed algorithm uses three keys in the encryption and decryption processes instead of one for controlling the variable block bits sizes (1, 2, 4, and 8) bits and for determining the state table numbers. These tables are formed from the addition in a Galois field GF (2n ) based on block bit size to increase the complexity of the proposed algorithm. Results are evaluated based on the criteria of complexity, time encryption, throughout, and histogram, and show that the original Blowfish, those modified by other scholars, and our proposed algorithm are similar in&#13;
time computation. Our algorithm is demonstrated to be the most complex compared with other well-known and modified algorithms. This increased complexity score for our proposed Blowfish makes it more resistant against attempts to break the keys.</text>
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                <text>Suhad Muhajer Kareem, Abdul Monem S. Rahma</text>
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              <elementText elementTextId="37824">
                <text>DOI: 10.12928/TELKOMNIKA.v18i2.13556</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="37825">
                <text>Universitas Ahmad Dahlan</text>
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                <text>April 2020</text>
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              <elementText elementTextId="37827">
                <text>Sri Wahyuni</text>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="37828">
                <text>ISSN: 1693-6930</text>
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              <elementText elementTextId="37829">
                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="37830">
                <text>PDF</text>
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              <elementText elementTextId="37831">
                <text>English</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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        <name>Blowfish algorithm</name>
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      <tag tagId="6979">
        <name>Encryption security</name>
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        <name>Jurnal Nasional Informatika</name>
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        <name>Multi-level keys</name>
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                  <text>VOL. 18, NO. 2 2020</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
A performance of radio frequency and signal strength of LoRa with BME280 sensor</text>
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            <description>The topic of the resource</description>
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                <text>Barometric, BME280, IoT, LoRa, Pressure, Temperature</text>
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            <description>An account of the resource</description>
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                <text>LoRa is a Radio Frequency module that can send packet data up to 3 km in FSPL.LoRa has 3 different Frequency Radios i.e, 915 MHz, 868 MHz, and 433 MHz. LoRa testing is based on different distances, BME280 provides Barometric Pressure, Temperature, and Humidity data. An analysis from the results of the Received Signal Strength to the distance (m) to the farthest point to prove and provide QoS data from LoRa 915 MHz. Sensor nodes are built using ADR and Automatic sleep mode algorithms. Communication systems between nodes are built dynamic sensor nodes in mesh networking. Monitoring signal transferring on the 915 MHz Frequency waveform is carried out using the Textronix Spectrum analyzer. Based on the BME280 Data packet transmission from the LoRa 915 MHz Transmitter to the Receiver Receiver at a distance of 100m is -84 dBm and at a distance of 500m is -107 dBm. The LoRa Internet Gateway has 2 types of settings, i.e, application, and gateway, this setting is to find out the location of the gateway with longitude and latitude. Furthermore, the gateway holds Sensor data from the End node, while the Application Server displays sensor data in the form of Graphics in realtime. </text>
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              <elementText elementTextId="38636">
                <text>Puput Dani Prasetyo Adi, Akio Kitagawa</text>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="38637">
                <text>DOI: 10.12928/TELKOMNIKA.v18i2. 14843</text>
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            </elementTextContainer>
          </element>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="38638">
                <text>Universitas Ahmad Dahlan</text>
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                <text>April 2020</text>
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              <elementText elementTextId="38640">
                <text>Sri Wahyuni</text>
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            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="38641">
                <text>ISSN: 1693-6930</text>
              </elementText>
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          </element>
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            <description>A related resource</description>
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              <elementText elementTextId="38642">
                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
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                <text>PDF</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="38644">
                <text>English</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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        <name>Barometric</name>
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        <name>BME280</name>
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        <name>IoT</name>
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        <name>Jurnal Nasional Informatika</name>
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        <name>LoRa</name>
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        <name>Pressure</name>
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        <name>Temperature</name>
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                  <text>VOL. 18, NO. 2 2020</text>
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      <name>Text</name>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
A robust method for VR-based hand gesture recognition using density-based CNN</text>
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            <description>The topic of the resource</description>
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              <elementText elementTextId="38165">
                <text>2D image gesture, representation, Binary image learning, Density-based CNN, Hand gesture recognition, VR-based physical treatment</text>
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                <text>Many VR-based medical purposes applications have been developed to help patients with mobility decrease caused by accidents, diseases, or other injuries to do physical treatment efficiently. VR-based applications were considered more effective helper for individual physical treatment because of their low-cost equipment and flexibility in time and space, less assistance of a physical therapist. A challenge in developing a VR-based physical treatment was understanding the body part movement accurately and quickly. We proposed a robust pipeline to understanding hand motion accurately . We retrieved our data from movement sensors such as HTC vive and leap motion. Given a sequence position of palm, we represent our data as binary 2D images of gesture shape. Our dataset consisted of 14 kinds of hand gestures recommended by a physiotherapist. Given 33 3D points that were mapped into binary images as input, we trained our proposed density-based CNN. Our CNN&#13;
model concerned with our input characteristics, having many 'blank block pixels', 'single-pixel thickness' shape and generated as a binary image. Pyramid kernel size applied on the feature extraction part and classification layer using softmax as loss function, have given 97.7% accuracy. </text>
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              <elementText elementTextId="38167">
                <text>Liliana,  Ji-Hun Chae, Joon-Jae Lee, Byung-Gook Lee</text>
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            </elementTextContainer>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
            <elementTextContainer>
              <elementText elementTextId="38168">
                <text>DOI: 10.12928/TELKOMNIKA.v18i2.14747</text>
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            </elementTextContainer>
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              <elementText elementTextId="38169">
                <text>Universitas Ahmad Dahlan</text>
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                <text>April 2020</text>
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              <elementText elementTextId="38171">
                <text>Sri Wahyuni</text>
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            </elementTextContainer>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="38172">
                <text>ISSN: 1693-6930</text>
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                <text>http://journal.uad.ac.id/index.php/TELKOMNIKA</text>
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                <text>PDF</text>
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            <description>A language of the resource</description>
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              <elementText elementTextId="38175">
                <text>English</text>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control </text>
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        <name>2D image gesture</name>
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        <name>Density-based CNN</name>
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      <tag tagId="6919">
        <name>Hand gesture recognition</name>
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      <tag tagId="5408">
        <name>Jurnal Nasional Informatika</name>
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        <name>representation</name>
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    <fileContainer>
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              <name>Title</name>
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              <elementTextContainer>
                <elementText elementTextId="37722">
                  <text>VOL. 18, NO. 2 2020</text>
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    <itemType itemTypeId="1">
      <name>Text</name>
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                <text>TELKOMNIKA Telecommunication, Computing, Electronics and Control &#13;
Adaptive threshold for moving objects detection using gaussian mixture model</text>
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          <element elementId="49">
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              <elementText elementTextId="38760">
                <text>Detection, Gaussian mixture model, Moving object, Otsu, Threshold</text>
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                <text>Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method&#13;
outperforms the baseline method in term of error detection. </text>
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An improved electricity efficiency method based on microcontroller and IoT with infrared sensor</text>
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An optimal control for complete synchronization of 4D Rabinovich hyperchaotic systems</text>
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                <text>Shaymaa Y. Al-Hayali, Saad Fawzi Al-Azzawi</text>
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