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                <text>Bauyrzhan , Nauryzbayev</text>
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                <text>The relevance of the research under consideration is due to the need to improve the efficiency of the analysis of the quality, and completeness of the knowledge obtained by students when solving computational problems, the example problems in mathematics. The theoretical argumentation is proposed and the practical implementation of an intelligent automated analytical system for analyzing the quality and forecasting the content of educational material and the trajectories of the student's learning direction is described. The relevance of the research is the creation of neural network algorithms that allow analyzing the dynamics of changes in the student's level of formation of skills to solve arithmetic problems. The methods of analyzing the assimilation of educational information and methods of personalized construction of the curriculum for each student are substantiated.</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 21 (2022); pp. 114-124</text>
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                <text>1865-7923</text>
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                <text>Using Machine Learning to Analyze the Learning Process for Solving Mathematical Problems</text>
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                <text>Qashlim, Akhmad</text>
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                <text>This research presents the use of smartphone technology to assist fisheries work. Specifically, we designed an Android application that utilizes a camera connected to the internet to detect RGB image objects and then convert them to HSV and gray scale. In this paper, Android-based smartphone technology using image processing methods will be discussed, a digital tool that provides fish detection results in the form of length, width, and weight used to determine the price of fish. This application was created using features provided by the OpenCV library to produce binary images. Three main challenges highlighted during application design including C ++ QT were used to build the user interface, the contour-active method was used to divide and separate image objects from the back-ground, while the clever edge edge method was used to improve the outline ap-pearance of objects. Both methods are implemented on the Android platform and utilize smartphone cameras as an identification tool. This application makes it possible to provide many benefits and great benefits for farm farmers but on the other hand will create technological gaps</text>
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                <text>Copyright (c) 2020 Akhmad Qashlim</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 08 (2020); pp. 150-163</text>
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                <text>1865-7923</text>
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                <text>Library OpenCV</text>
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                <text>Smartphone Technology Applications for Milkfish Image Segmentation Using OpenCV Library</text>
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                <text>Huda, Asrul</text>
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                <text>Business Intelligence is very popular and useful for a better understanding of business progress these days, and there are many different methods or tools being used in Business Intelligence. It uses combination of artificial intelligence, data mining, math, and statistic to gain better understanding and insight on the business process performance. As employees have an important role in business process, the desire to have a tool for classifying and predicting their wages are desirable. In this research, we tried to analyzed dataset from Human Resource Department, and this dataset can be used to analyst the data in order to draw a conclusion about whether any employees would prematurely leave the company, and then, a preventive action based on those parameters can be proposed. This is a kind of predictive analytic system which bases on Naïve Bayes, and it can predict whether an employee would leave or stay according to his or her characteristics. But the Naïve Bayes itself does not enough. So we develop a way to solve the problem using uncertain Numeric features classification on it. The accuracy of the result is depended on the amount and effectiveness of the training sets.</text>
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                <text>Copyright (c) 2021 Asrul Huda, Noper Ardi</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 08 (2021); pp. 172-181</text>
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                <text>1865-7923</text>
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                <text>Predictive Analytic</text>
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                <text>Predictive Analytic on Human Resource  Department Data Based on Uncertain Numeric  Features Classification</text>
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                <text>In this age of digitization, it is essential to develop interactive media that may serve as a vehicle for learning while also engaging children. Thus, learning media that includes both real and digital media can be made more exciting and less tedious by making it more fun while still valuing traditional and interactive learning utilizing technology, which is why this is important. This project aims to generate media solutions that early childhood educators and kindergarten teachers can use to assist them in learning and teaching. The media that was created includes Android apps that use Augmented Reality. (AR), technology to make the experience more fun for kids and book media based on the kindergarten curriculum. Subsequently, parents and caregivers can use this media to teach their children about the living room, toilet, and other places based on the images that have been shown. When an Android app scans this picture, it will illustrate animated 3D objects through Augmented Reality. Notwithstanding, the Android app has a free picture coloring option. Equally, it must be supervised by parents or teachers, a practice known as digital parenting. The development and verification results confirm that this medium is suitable for implementation and use in schools and kindergartens.</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 24 (2022); pp. 4-20</text>
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                <text>1865-7923</text>
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                <text>Kindergarten</text>
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                <text>early childhood development</text>
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                <text>blender 3D</text>
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                <text>colouring book</text>
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                <text>Development of Interactive Media Based on Augmented Reality for Early Childhood Learning Around the Home</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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                <text>Ardi, Zadrian</text>
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                <text>Fauziyyah, Siti Azizi</text>
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                <text>Students experience various essential changes related to their psychological conditions during their education in college. Students' adaptability to the demands of these changes will give birth to new skills for the next stage of psychological development. However, academic stress, social problems, career, and personal-emotional problems can hinder the productivity and development of students in terms of mental health. This issue increased with the new-normal life during the COVID-19 pandemic, which also impacted the education process. For this reason, it is necessary to have a mobile-based online psychological assessment to map the mental health conditions of students during the pandemic. This study aims to develop and validate an Android-based online psychological assessment application to map students' mental health conditions. This study involved ten experts in counseling and software development, five counselors, and 638 users of the POTENSIA application. According to application development needs, data analysis using Aiken's V coefficient test, Confirmatory Factor Analysis (CFA), and Rasch Model Analysis. The results of the study prove that the application is considered valid and practical to use. The effectiveness of the application is considered very good in mapping the mental health conditions of students.</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/25147/9747</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="44693">
                <text>Copyright (c) 2021 Zadrian Ardi, Hendra Hidayat, Ifdil Ifdil, Yan Guspriadi, Siti Azizi Fauziyyah</text>
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              <elementText elementTextId="44694">
                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 16 (2021); pp. 16-33</text>
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              <elementText elementTextId="44695">
                <text>1865-7923</text>
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            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="44696">
                <text>Android-based applocation</text>
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              <elementText elementTextId="44697">
                <text>mental health</text>
              </elementText>
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                <text>college student</text>
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                <text>COVID-19 pandemic</text>
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                <text>The Development of POTENSIA; The Android-Based Psychological Application for Mapping and Assessments of Student Mental Health During the COVID-19 Pandemic</text>
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            <element elementId="50">
              <name>Title</name>
              <description>A name given to the resource</description>
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                <elementText elementTextId="15984">
                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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      <name>Dataset</name>
      <description>Data encoded in a defined structure. Examples include lists, tables, and databases. A dataset may be useful for direct machine processing.</description>
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              <elementText elementTextId="34199">
                <text>Indrabayu, Indrabayu</text>
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                <text>Nurtanio, Ingrid</text>
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                <text>Areni, Intan Sari</text>
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                <text>Bugiwati, Sri R.A</text>
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                <text>Bustamin, Anugrayani</text>
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                <text>Rahmatullah, Muhammad</text>
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                <text>This research focuses on developing an Android-based cattle identification system that is applicable and easy to use. This system uses a Scale Invariant Feature Transform (SIFT) algorithm to extract features from the muzzle images, and Random Sample Consensus (RANSAC) algorithm to eliminate features incompatibility. The system is experimented with four threshold values, i.e. 10, 15, 20, and 30 using a total data of 460 muzzle images.  In the first experiment, 3 images from each individual are used in the training stage and 2 images are used as the data test. In the second experiment, 5 images from each cattle are used in the training stage and 5 images are used as the data test. Data used in training stage are 244 images and in testing stage is 816 images. From the experiment, the highest accuracy rate is 98.1% with threshold values of 15 and 20. The execution time is also calculated to measure the processing time of the system. The average time taken to store an image to the database is 1.3 seconds. The main contribution of this research is technology implementation and more portable muzzle identification for local cattle in Makassar.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/13237</text>
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                <text>10.3991/ijim.v14i13.13237</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/13237/7671</text>
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            <description>Information about rights held in and over the resource</description>
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              <elementText elementTextId="34213">
                <text>Copyright (c) 2020 Indrabayu Indrabayu, Ingrid Nurtanio, Intan Sari Areni, Sri R.A Bugiwati, Anugrayani Bustamin, Muhammad Rahmatullah</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 13 (2020); pp. 134-149</text>
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              <elementText elementTextId="34215">
                <text>1865-7923</text>
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            <description>The topic of the resource</description>
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                <text>cattle identification</text>
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                <text>SIFT</text>
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                <text>muzzle pattern</text>
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                <text>A Portable Cattle Tagging Based on Muzzle Pattern</text>
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                  <text>Journal of Naval Architecture and Marine Engineering</text>
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                <text>Purwana, Agung</text>
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                <text>Ariana, I. Made</text>
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                <text>Wardhana, Wisnu</text>
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                <text>In this study, numerical simulations on the noise of the underwater marine propeller for different pressures, skew angles, and performance conditions are investigated. The study has been carried out for the prediction of cavity and noise cavitation characteristics of the propeller. The blade sheet cavitation created by an underwater propeller is then evaluated using numerical analysis. The cavitation and cavity around marine propellers were predicted using MRF (Multiple Reference Frame) techniques. The simulation uses the Reynolds Averaged Navier-Stokes (RANS) formulation with the turbulence model k-ω Shear Stress Transport and the Fast Fourier Transform. The FW-H equation is used to measure far-field radiation under various operating conditions. The simulation is carried out to present that the pressure and skew propeller angles have an effect on the form and area of the cavity, as well as cavitation noise. The noise characteristics at various positions of hydrophones and speeds of the marine propeller are presented. The 3D model of B-series marine propeller with D=250 mm, Z=4, P/D= 1.0, AE/AO=0.55, skew angles of 16, 35, 53, and 72 degrees at advance coefficient, J=0.221, is used for the simulation</text>
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                <text>https://www.banglajol.info/index.php/JNAME/article/view/38099</text>
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                <text>Association of Naval Architects and Marine Engineers</text>
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            <name>Rights</name>
            <description>Information about rights held in and over the resource</description>
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              <elementText elementTextId="67904">
                <text>Copyright (c) 2021 Journal of Naval Architecture and Marine Engineering</text>
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              <elementText elementTextId="67905">
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                <text>Lately, icons have witnessed a growing wave of interest in the view of enhancing transparency and clarity of data processing practices in mandated disclosures. Although benefits in terms of comprehensibility, noticeability, navigability of the information and user’s attention and memorization can be expected, they should also be supported by decisive empirical evidence about the efficacy of the icons in specific contexts. Misrepresentation, oversimplification, and improper salience of certain aspects over others are omnipresent risks that can drive data subjects to wrong conclusions. Cross-domain and international standardization of visual means also poses a serious challenge: if on the one hand developing standards is necessary to ensure widespread recognition and comprehension, each domain and application presents unique features that can be hardly established, and imposed, in a top-down manner. This article critically discusses the above issues and identifies relevant open questions for scientific research. It also provides concrete examples and practical suggestions for researchers and practitioners that aim to implement transparency-enhancing icons in the spirit of the General Data Protection Regulation (GDPR).</text>
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                <text>Journal of Open Access to Law; Vol. 8 No. 1 (2020): Special Issue on "Visual Law"</text>
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