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                <text>Salmi, Khalid</text>
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                <text>The teaching of transmission line theory in electrical engineering courses must be tailored to an audience which is increasingly reluctant to adhere to abstract disciplines. In our opinion, the best solution to make transmission line courses more attractive is to offer practical applications and intensively use of mathematical computer-aided teaching tools to overcome, at the beginning, the difficulties linked to the theory. Indeed, transmission line theory comes alive when the travelling waves are animated on a screen (smartphone, tablets, laptop, etc.). Fundamental concepts such as “progressive wave”, “reflected wave” and “load matching” could be easily demonstrated in the classroom or at home. Transmission line simulations are applied to problems using connections to shunt, open, matched and unmatched loads, and show how the signal waveforms arise from one end to another. The proposed Android-based animations are used with a sinusoidal generator to illustrate the evolution to the sinusoidal steady state and allow learners to easily handle the corresponding Smith chart. Students are encouraged to run those applications at home as a computational laboratory to verify their solutions to homework problems. This article introduces simple Android-based virtual tools for the investigation and visualization in real time of waves traveling along a terminated finite-length transmission line, without and with faults between the source and the load. The package can be used as an educational tool in various lectures or homework to aid teaching high frequency electronics and transmission lines theory.</text>
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                <text>Copyright (c) 2019 Khalid Salmi, Hamid Magrez, Hanane Sefraoui, Abdelhak Ziyyat</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 13 No. 02 (2019); pp. 78-88</text>
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                <text>1865-7923</text>
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                <text>transmission lines</text>
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                <text>Development of a Mobile Application for Teaching Transmission Line Theory</text>
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                <text>Nurmalasari, Riana</text>
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                <text>Mahamad , Abd Kadir</text>
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                <text>The digital era has accelerated the development of educational media. This enables access to educational activities from any location at any time. Distance education is gaining popularity and flexibility in its implementation. The purpose of this study is to determine the role of MEA applications in machine element learning, specifically in the State University of Malang's D3 Mechanical Engineering Study Program. MEA is an interactive mobile learning platform designed to broaden D3 students' access to sources and learning tools. This is a quantitative descriptive study. The population of this study consisted of 40 students enrolled in D3 Mechanical Engineering at the State University of Malang. The data were gathered through literacy assessments, observations, interviews, and questionnaires. The tool is a questionnaire constructed on the basis of the Likert scale. Instrument testing included validity and reliability checks. The data analysis technique was descriptive analysis. Five conclusions have been drawn as a result of the analysis. First, the MEA application is 78.6% user-friendly. Second, the MEA application is easy to use for the machine element learning process in 82.1% of cases. Third, the MEA application assists the machine element learning process by 75%. Fourth, 89.3% of MEA application technology is suitable for distant education in the machine element course. Fifth, MEA applications improve machine element learning outcomes by 82.1%.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/28997</text>
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                <text>Copyright (c) 2022 Riana, marsono, Dr. Kadir</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 05 (2022); pp. 216-223</text>
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                <text>1865-7923</text>
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                <text>Machine Element Applications</text>
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                <text>interactive</text>
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                <text>The Role of Machine Element Applications (MEA) as Interactive Mobile Learning to Support the Implementation of Distance Learning in Machine Element Course</text>
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                <text>Minhat, Mohammad Hazwan Hakim</text>
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                <text>Saon, Sharifah</text>
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                <text>Mahamad, Abd Kadir</text>
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                <text>Umi Fadlillah</text>
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                <text>Leaf identification image is consistently a difficult task when using computer vision. The convolutional component extraction methods on images have their impediment and limitation, such as low accuracy, are not adaptable and less promising when converted to genuine application. The reasons are the lack of dataset needed to build a recognition model. Likewise, using the computer as a tool is bothering as it restricts the task in the research lab only. Convolutional Neural Network (CNN) shows a great solution for the computer version. Subsequently, this project utilizes the CNN’s properties to solve the image classification task, and the CNN model chosen is run in Phyton coding in TensorFlow Lite. It is similar to TensorFlow’s running code, but this project focused on building an Android application. It can perform faster and produce high accuracy results. There are four types of leaves involved in this project: betik, kari, pudina, and cengal. As a result, the model could reach around 99% accuracy with a 0.176 error rate. Ultimately, an Android application called Leaf identification is created. The model is sent and integrated into the apps that work with a concentrated information base to help put away and deal with the pictures. Hence, an Android leaf image identifier using CNN is proposed to solve the stated problem and is believed to contribute to education and research.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/31881</text>
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                <text>Copyright (c) 2022 Hazwan Hakim, Sharifa, Dr Kadir , Umi Fadlillah</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 15 (2022); pp. 62-77</text>
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                <text>1865-7923</text>
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                <text>This study aimed to investigate the effect of a digital mind map (DMM) assisted group investigation (GI) on students’ creativity in higher education. A pre-test post-test nonequivalent control group design was employed in this study. The study was conducted in 2019/2020 to 75 fifth semester students from IKIP Budi Utomo Malang, Indonesia. The data were collected using a pre-test and a post-test on students’ creativity. The students’ creative performance was evaluated using a rubric assessing the keywords, material’s completeness, material’s conformity, layout organization, creativity, symbols, and color used in the students' mind maps. Testing for the normality and homogeneity of variance was conducted before the ANCOVA was run to examine the data. The statistical analysis showed a difference between students in the DMM-GI group and students in the DMM-conventional group in terms of creativity. The mean score of the DMM-GI group (79.30) was higher than that of the DMM-conventional group (74.96). Therefore, it can be concluded that the DMM-GI group had better creative performance compared to the DMM-conventional group.</text>
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                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="39327">
                <text>Copyright (c) 2021 Evrialiani Rosba, Siti Zubaidah, Susriyati Mahanal, Sulisetijono Sulisetijono</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 05 (2021); pp. 4-23</text>
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                <text>1865-7923</text>
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            <name>Subject</name>
            <description>The topic of the resource</description>
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                <text>creativity in higher education</text>
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                <text>Digital Mind Map Assisted Group Investigation Learning for College Students’ Creativity</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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              <elementText elementTextId="31694">
                <text>Omar, Mohd Faizal</text>
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                <text>Mahathir, Nurul Husna</text>
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                <text>Mohd Nawi, Mohd Nasrun</text>
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                <text>Zulhumadi, Faisal</text>
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                <text>No wadays, the government agencies are looking for strategies to strengthen their newly implemented policies for the nation building. The grassroots or the customer’s sentiments is very important to develop an inclusive policy with a mixed of bottom-up approach to incorporates the customer’s opinion. However, due to the unique political landscape and multiracial in Malaysia, current commercial off-the-shelf Social Analytics are irrelevant to capture the sentiments of multilingual characteristic for Malaysian native speakers. Current Social Analytic tool are lacking the quality of analysis for foreign languages such as Malay which limits the businesses to localize advertisement for a specific geographical area. Hence, this research is proposed to develop a real-time social media analytics tool with sentiment analysis specifically in Malaysian context in order to engage and analyze the customer reviews and opinions. The main purpose of this paper is to demonstrate our approach to utilize data from social media platforms such as Facebook and Twitter in gaining valuable insights to drive and improve marketing strategy in property industry. This research developed a tool namely Property Analytics to assess public sentiments on specific property project or services. The methodology and approach to enhance from lab scale to pre-commercialization activities are outlined in this paper. It is anticipates that our work is relevant to real world application, improve stakeholder’s decision making and people’s quality of life.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/11309</text>
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            <description>A language of the resource</description>
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                <text>eng</text>
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                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/11309/5933</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="31706">
                <text>Copyright (c) 2019 Mohd Faizal Omar, Nurul Husna Mahathir, Mohd Nasrun Mohd Nawi</text>
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              <elementText elementTextId="31707">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 13 No. 10 (2019); pp. 198-204</text>
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                <text>1865-7923</text>
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            <description>The topic of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="31709">
                <text>Social Media Analytics</text>
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              <elementText elementTextId="31710">
                <text>Sentiment Analysis</text>
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              <elementText elementTextId="31711">
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                <text>Prototype Development and Pre-Commercialization Strategies for Mobile Based Property Analytics</text>
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                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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              <elementText elementTextId="19015">
                <text>Maiti, Ananda</text>
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                <text>Maiti, Anindya</text>
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                <text>Recently, development of "Microelectronics and VLSI Engineering" Laboratory at IIT Kharagpur (http://lod.iitkgp.ernet.in/netlab), an innovative remote microelectronics device characterization and parameter extraction laboratory has been reported. The laboratory enables students to measure different semiconductor device characteristics and extract device parameters at any time and from anywhere using a Java-enabled web browser. Having pioneered the PC-based online laboratory to enhance microelectronics education, feasibility of use of mobile devices for hardware-based remote experimentation is explored. The work introduced in this paper is a prototype of the use of mobile devices in laboratory education.</text>
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            <description>The file format, physical medium, or dimensions of the resource</description>
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                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="19025">
                <text>Copyright (c) 2017 Ananda Maiti, Anindya Maiti</text>
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              <elementText elementTextId="19026">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 4 No. 4 (2010); pp. 45-48</text>
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                <text>1865-7923</text>
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            <description>The topic of the resource</description>
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              <elementText elementTextId="19028">
                <text>m-learning</text>
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                <text>flash lite</text>
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                <text>ADOBE Flash Lite â?? Based Online Laboratory for Mobile Phones</text>
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                <text>Majeed Laftah, Muna</text>
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                <text>Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt&amp;amp; pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by using Peak Signal to Noise Ratio (PSNR). Depend on the value of PSNR that explained in the result section; we conclude that the (Tri-State Median filter) is better than (Switching Median filter) in denoising image quality, according to the results of applying rules the result of the Shrinking rule for each filter shows that the best result using first the Bivariate Shrink.</text>
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                <text>10.3991/ijim.v15i15.24183</text>
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            <name>Language</name>
            <description>A language of the resource</description>
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                <text>International Association of Online Engineering (IAOE), Vienna, Austria</text>
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          <element elementId="47">
            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
            <elementTextContainer>
              <elementText elementTextId="43923">
                <text>Copyright (c) 2021 Muna Majeed Laftah</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 15 (2021); pp. 140-151</text>
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              <elementText elementTextId="43925">
                <text>1865-7923</text>
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                <text>Denoising</text>
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                <text>Thresholding</text>
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                <text>Soft Thresholding</text>
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                <text>Image Denoising Using Multiwavelet Transform with Different Filters and Rules</text>
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