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                  <text>VOL 6 NO 6 (2022)</text>
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                <text>Faux Insider Hazard Investigation on Non-Public Cloud Computing by &#13;
Using ADAM’s Technique</text>
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                <text>Cloud, MITC, Forensics, ADAM, Analysis</text>
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                <text>Cloud computing is a service system mechanism that businesses and organizations use to perform computerized and integrated &#13;
transactions over computer networks. The service system must, of course, be”matched”with a”certain amount”of security. It &#13;
is applied to” forecast the probability of cybercrime. A Cloud Service Provider (CSP) often offers cloud-based services with a &#13;
basic level of security. Typically, CSPs are set up to offer their services on the open internet. Data security-focused &#13;
organizations strive to shield their systems from a wide range of attackers. One of the alternatives is to construct a private &#13;
cloud computing system. The issue is the potential for Man in the Cloud (MITC) assaults, which compromise and modify &#13;
identities and are identified in cloud systems as phony insider threats. Based on the ISO 27032 standard research, the goal of &#13;
this work is to undertake a threat analysis of MITC attack methodologies against private cloud computing services. With &#13;
regards to risks to cloud services in a private cloud computing environment, it is intended that reporting and documenting the &#13;
study' findings would lead to suggestions for more research and cybersecurity management procedures</text>
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                <text>Dwi Kurnia Wibowo1&#13;
, Ahmad Luthfi2&#13;
, Yudi Prayudi3&#13;
, Erika Ramadhani4&#13;
, Muhamad Maulana5</text>
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                <text>Indonesian Islamic University</text>
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                <text>: 29-12-2022</text>
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                <text>Fajar bagus W</text>
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                <text>Indonesia</text>
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                  <text>VOL 6 NO 6 (2022)</text>
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                <text>Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious &#13;
Diseases</text>
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                <text>Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious &#13;
Diseases</text>
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                <text>E-learning and expert systems can be implemented for learning in the health sector. Through the e-learning system, prospective &#13;
health workers can analyze problems by exploring the material in the system. However, material learning alone is less effective, &#13;
so case study-based learning using an expert system is needed to strengthen understanding. The research applies an expert &#13;
system to online learning to diagnose several infectious diseases. The disease diagnosis process uses the backward chaining &#13;
method and the Mamdani fuzzy inference system. The fuzzy Mamdani inference system determines the intensity of disease &#13;
severity so that appropriate treatment recommendations can be made. The test findings on 15 test datasets yielded a backward &#13;
chaining accuracy value of 100%. Three test scenarios were used to establish the test using the Mamdani fuzzy inference &#13;
method. Scenario 1: Testing with the Center of Gravity defuzzification and Fuzzy Mamdani Min inference system Tests &#13;
employing the Fuzzy Mamdani Min inference method and center average defuzzification are used in Scenario 2. Scenario 3 &#13;
involves testing using the Fuzzy Mamdani Product Inference System with Center Average Defuzzification. The average outcome &#13;
for the intensity of disease severity utilizing the Fuzzy Mamdani Min inference system with Center of Gravity defuzzification &#13;
was greater than that of the two test scenarios that were suggested, which was 49.43%.</text>
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                <text>Istiadi1&#13;
, Emma Budi Sulistiarini2&#13;
, Rudy Joegijantoro3&#13;
, Anik Vega Vitianingsih4&#13;
, Affi Nizar Suksmawati5</text>
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                <text>Universitas Widyagama Malang</text>
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                <text>Fajar bagus W</text>
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                <text>Indonesia</text>
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                  <text>VOL 6 NO 6 (2022)</text>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Digital Forensic on Secure Digital High Capacity using DFRWS Method</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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                <text>Digital forensic, sdhc, dfrws, linux, foremost, dc3dd</text>
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            <name>Description</name>
            <description>An account of the resource</description>
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                <text>As evidenced in the trial, between 2015 and the second quarter of 2022, there were 54 cases involving secure digital high &#13;
capacity (SDHC) storage hardware as evidenced in trials. In 2021 there will be an increase in cases involving SDHC. The &#13;
three cases with the highest number are corruption cases, special crimes, and ITE. SDHC is an advanced technology &#13;
development of Secure Digital (SD) card hardware which functions as storage. SD Card only has a capacity of up to 2 &#13;
gigabytes, while the largest SDHC capacity is 32 gigabytes. As a storage device that is small, thin, and has a fairly large &#13;
capacity. this research needs to be done because of the increasingly widespread increase in cases involving SDHC. This study &#13;
aims to perform digital forensic analysis on SDHC evidence using forensic applications that run on Linux, namely foremost &#13;
and DC3DD. This study uses the DFRWS method to retrieve valid evidence in court. Based on the research conducted, it was &#13;
found that the number of files that can be restored at the examination stage using foremost is 77%, and the accuracy of &#13;
recovered files is 50% with string file hash validation. From this research, it can be concluded that the processing results of &#13;
DC3DD and Foremost can be used as valid evidence.</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
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                <text>Anton Yudhana1&#13;
, Imam Riadi2&#13;
, Budi Putra3</text>
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            <description>An entity responsible for making the resource available</description>
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                <text>Universitas Ahmad Dahlan</text>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>29-12-2022</text>
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            <name>Contributor</name>
            <description>An entity responsible for making contributions to the resource</description>
            <elementTextContainer>
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                <text>Fajar bagus W</text>
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            <elementTextContainer>
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                <text>Indonesia</text>
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                  <text>VOL 6 NO 6 (2022)</text>
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                <text>Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear &#13;
Regression Algorithm</text>
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                <text>crude oil, ICP, time series, prediction, linear regression</text>
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                <text>Crude oil prices play a significant role in the global economy, therefore accurate prediction of oil prices is very important. &#13;
Therefore, a forecasting model is needed to predict Crude Oil Prices. The purpose of this study is to forecast the price of crude &#13;
oil from Indonesia (ICP). The data source is from a website published by the Ministry of Energy and Mineral Resources &#13;
(ESDM), namely monthly crude oil price data specifically for six main types of crude oil: SLC, Attaka, Duri, Belida, Banyu &#13;
and SC. The data used is data for a period of 5 years (2018 – 2022). The data available is in the form of time series data. Dated &#13;
Brent combined with the Alpha factor for each month and year is a reference in determining the ICP price. Forecasting &#13;
Indonesian crude oil prices in the future is based on the historical oil price of the previous period. The Data Mining algorithm &#13;
used for forecasting is Multiple Linear Regression. The dataset processed using training data is 80%, and testing data is 20%. &#13;
The model produced, on average, has a good level of accuracy in calculating MAPE where for SLC = 9%, Attaka = 45%, Duri &#13;
= 126%, Belida = 33%, Banyu = 150% and SC = 50%. Based on the MAPE calculation value, the Linear Regression Equation &#13;
to predict Indonesian Crude Oil Prices (ICP) shows that the model produced by SLC crude oil is very good. Attaka, Belida and &#13;
SC crude oil yielded fair yields and Duri and Banyu crude oil yielded poor yields.</text>
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            <description>An entity primarily responsible for making the resource</description>
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                <text>Des Suryani1&#13;
, Mutia Fadhilla2&#13;
, Ause Labellapansa3</text>
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            <description>An entity responsible for making the resource available</description>
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                <text>University of Islam Riau</text>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>30-12-2022</text>
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            <name>Contributor</name>
            <description>An entity responsible for making contributions to the resource</description>
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                <text>Fajar bagus W</text>
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            <name>Language</name>
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                <text>Indonesia</text>
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                <text>Measuring the effect of Users' Privacy Concerns on the Use of Jakarta &#13;
Smart City Mobile Application (JAKI)</text>
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                <text>smart city, Jakarta, mobile application, privacy concern</text>
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                <text>akarta Kini (JAKI) is a super-app developed by Jakarta Smart City that offers a one-stop service to help citizens connect and &#13;
communicate with the Government. It is undeniable that the use of mobile applications can indeed facilitate people's activities, &#13;
but on the other hand, it also poses risks and raises concerns in terms of privacy. The purpose of this study is to assess the &#13;
impact of users' privacy concerns on their tendency to use the JAKI mobile application. To measure the privacy concern, we &#13;
conduct an online survey of the users of JAKI. The hypothesis and research model were formulated to assess the users' privacy &#13;
concerns based on the Mobile Users' Information Privacy Concerns (MUIPC) theory, with additional factors, namely prior &#13;
privacy experience and awareness, as the antecedents. As a result, we found that MUIPC had a significant effect on negatively &#13;
influencing the intention to use the JAKI application. Our study contributes as a starting point in exploring privacy research &#13;
in the context of a smart city in Indonesia. Additionally, this study proved that the IPC scales that were originally designed for &#13;
English-based countries could also be adapted to Bahasa Indonesia and utilized in the Indonesian context.</text>
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                <text>Arif Wahyudi, &#13;
2Mirza Triyuna Putra,&#13;
3Dana Indra Sensuse, 4Sofian Lusa, 5Prasetyo Adi, 6Assaf Arief</text>
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                <text>Universitas Indonesia</text>
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                <text>Fajar bagus W</text>
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            <description>A language of the resource</description>
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              <description>A name given to the resource</description>
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                  <text>VOL 6 NO 6 (2022)</text>
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      <name>Text</name>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Topic Modeling for Support Ticket using Latent Dirichlet Allocation</text>
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            <description>The topic of the resource</description>
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                <text>Topic Modeling, Support Ticket, Latent Dirichlet Allocation.</text>
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            <description>An account of the resource</description>
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                <text>In the business world, communication over customers must be built properly to make it easier for companies to find out what &#13;
customers want. Support ticket is one of the business instrument for communication between the customers and the companies. &#13;
Through a support ticket, customers can respond, complain or ask questions about products with a support team. Increasing &#13;
the business process of the companies will be increasing the support ticket volume that should be handled by support team. It&#13;
also has a value for analysis to get business intelligence decision. With that chance, an efficient data processing method is&#13;
needed to find topics are being discussed by customers. One way that can be used to solve this problem is Topic Modeling. &#13;
This research uses several parameters the number of topics, alpha value, beta value, iteration, and random seed. With this &#13;
combination of parameters, the best results based on evaluation of human judgement and topic coherence with 5 topics, an &#13;
alpha value of 50, a beta value of 0.01, 100 iterations, and 50 random seeds. The five topics interpretation consists of hosting &#13;
migration, error problems in wordpress, domain email settings and domain transfer, ticketing and transaction processing. The &#13;
total of 5 topics has a coherence value of 0.507897</text>
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            <description>An entity primarily responsible for making the resource</description>
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                <text>Wiranto1&#13;
, Mila Rosyida Uswatunnisa2</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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                <text>Sebelas Maret University</text>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>29-12-2022</text>
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            <description>An entity responsible for making contributions to the resource</description>
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                <text>Fajar bagus W</text>
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            <description>The file format, physical medium, or dimensions of the resource</description>
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              <elementText elementTextId="99908">
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          <element elementId="44">
            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="99909">
                <text>Indonesia</text>
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          </element>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
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                <text>Text</text>
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              <description>A name given to the resource</description>
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                <elementText elementTextId="99452">
                  <text>VOL 6 NO 6 (2022)</text>
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    <itemType itemTypeId="1">
      <name>Text</name>
      <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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    <elementSetContainer>
      <elementSet elementSetId="1">
        <name>Dublin Core</name>
        <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Using Social Media Data to Monitor Natural Disaster: A Multi Dimension &#13;
Convolutional Neural Network Approach with Word Embedding</text>
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            </elementTextContainer>
          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="99892">
                <text>natural disaster, word embedding, convolutional neural network, twitter, social media</text>
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            <name>Description</name>
            <description>An account of the resource</description>
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              <elementText elementTextId="99893">
                <text>Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural &#13;
disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster &#13;
management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non�eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location &#13;
of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word &#13;
meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can &#13;
maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and &#13;
Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new &#13;
features by combining data from all word embedding techniques. The classification model is made using three Convolutional &#13;
Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the &#13;
case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the &#13;
2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique &#13;
produces better average accuracy</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
            <elementTextContainer>
              <elementText elementTextId="99894">
                <text>Mohammad Reza Faisal1&#13;
, Irwan Budiman2&#13;
, Friska Abadi3&#13;
, Muhamma</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="99895">
                <text>Universitas Lambung Mangkurat</text>
              </elementText>
            </elementTextContainer>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
            <elementTextContainer>
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                <text>29-12-2022</text>
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            </elementTextContainer>
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          <element elementId="37">
            <name>Contributor</name>
            <description>An entity responsible for making contributions to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="99897">
                <text>Fajar bagus W</text>
              </elementText>
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            <name>Format</name>
            <description>The file format, physical medium, or dimensions of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="99898">
                <text>PDF</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="44">
            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="99899">
                <text>Indonesia</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="51">
            <name>Type</name>
            <description>The nature or genre of the resource</description>
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                <text>Tet</text>
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              <name>Title</name>
              <description>A name given to the resource</description>
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                <elementText elementTextId="99452">
                  <text>VOL 6 NO 6 (2022)</text>
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    <itemType itemTypeId="1">
      <name>Text</name>
      <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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    <elementSetContainer>
      <elementSet elementSetId="1">
        <name>Dublin Core</name>
        <description>The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.</description>
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          <element elementId="50">
            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Heart Attack Notification and Monitoring System Using Internet of Things</text>
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          </element>
          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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                <text>heart attack, notification system, monitoring system, IoT, internet of things</text>
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            <name>Description</name>
            <description>An account of the resource</description>
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                <text>People are frequently shocked when someone passes away suddenly without any prior symptoms. One of the contributing &#13;
factors is a heart attack. This condition might occur anywhere and at any time. A sudden heart attack can be highly perilous &#13;
for a person who is alone, without family members or friends because the family cannot be informed of the victim's condition &#13;
or their location. Therefore, it is vital to raise awareness of heart attacks. With the support of the Internet of Things, this study &#13;
aims to develop a wearable device that people may use to monitor their heart health and connect with hospitals to get alerts in &#13;
case of a heart attack. This system also provides family members with access to a web-based patient monitoring tool. The heart &#13;
beat is considered as the parameter in developing this system. There are three types of evaluation which are conducted in this &#13;
study, namely: 1) Sub-system evaluation; 2) Black-box testing; and 3) Integrating system testing. The three evaluation results &#13;
show that all assembled hardware components are work properly and the system effectively satisfies the objectives of &#13;
monitoring, buzzer activation, hospital and patient family notification, and so forth, with 1.96% average sensor error, which &#13;
is still considerably acceptable.</text>
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            <description>An entity primarily responsible for making the resource</description>
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              <elementText elementTextId="99884">
                <text>1*Maya Fitria, &#13;
2Ramzi Adriman, 3&#13;
Irham Muhammaddin Batubara, 4Akhyar Bintang</text>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="99885">
                <text>Universitas Syiah Kuala</text>
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            <name>Date</name>
            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text>29-12-2022</text>
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            <description>An entity responsible for making contributions to the resource</description>
            <elementTextContainer>
              <elementText elementTextId="99887">
                <text>Fajar bagus W</text>
              </elementText>
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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="99888">
                <text>PDF</text>
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            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
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                <text>indonesia</text>
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            <name>Type</name>
            <description>The nature or genre of the resource</description>
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                <text>Text</text>
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                  <text>VOL 6 NO 6 (2022)</text>
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      <name>Text</name>
      <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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            <name>Title</name>
            <description>A name given to the resource</description>
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                <text>Gift Recommendations Based on Personality Using Fuzzy and Big Five &#13;
Personality Test</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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                <text>gift recommendation, personality, big five personality, Fuzzy</text>
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            <description>An account of the resource</description>
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                <text>Gifts are usually given to someone to strengthen a relationship or to motivate someone. However, givers often need help &#13;
determining the appropriate gift for the potential recipient. On the other hand, many recipients are disappointed with the gifts &#13;
received. This event can result in the relationship between the giver and recipient being disrupted or the motivational goal not &#13;
being achieved. This research aims to develop a system to recommend gifts based on the recipient's personality. Gift &#13;
recommendation is determined based on the recipient's personality because the recipient highly values gifts that match the &#13;
recipient's personality. The system is built using the Fuzzy method, and the personality measurement tool used is the Big Five &#13;
Personality Test. Fifteen pairs of respondents validated the system. The validation results show that 80% of respondents as &#13;
gift-givers strongly agree that the system helps determine the appropriate gift for someone. In addition, 73.33% of respondents &#13;
as gift recipients strongly agree that the gifts recommended by the system do not disappoint them</text>
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            <name>Creator</name>
            <description>An entity primarily responsible for making the resource</description>
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              <elementText elementTextId="99829">
                <text>Susana Limanto1&#13;
, Vincentius Riandaru Prasetyo2&#13;
, Ni Wayan Gitaputri3</text>
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          <element elementId="45">
            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
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              <elementText elementTextId="99830">
                <text>University of Surabaya</text>
              </elementText>
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                <text>Fajar bagus W</text>
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                <text>Indonesia</text>
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                  <text>VOL 6 NO 6 (2022)</text>
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                <text>A Comparison of the Smoothing Constant Values Among Exponential &#13;
Smoothing Methods in Commodity Prices Forecasting</text>
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                <text>forecasting, smoothing constant, exponential smoothing, B-DES, SES.</text>
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                <text>Commodity prices forecasting is one of the business functions to estimate future demand based on past data trend. This study &#13;
aims to implement a trial and error technique of the constant (alpha α) value in the exponential smoothing method. Dealing &#13;
with confusion that often researchers find in selecting an alpha (α) value among exponential smoothing families, which suits &#13;
characteristics of the investigated case. As selection of the constant value precisely contributes to reduce the forecasting &#13;
deviation. This paper used the alpha (α) value in the range 0,1 to 0,9 and utilized the mean absolute percentage error (MAPE) &#13;
and Mean Absolute Error (MAE) as the parameter to know the grade of prediction. In data training, the authors used Single &#13;
Exponential Smoothing (SES) and Brown’s Double Exponential Smoothing (B-DES) as methods to compare the results of &#13;
prediction. It is addressed that forecasting with alpha (α) 0,1 is the most optimal values for Single Exponential Smoothing &#13;
(SES) in this case with margin error 0,00036 of MAPE and 16,84 of MAE</text>
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                <text>1Hazriani, &#13;
2Yuyun, 3Mashur Razak</text>
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                <text>Handayani University</text>
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                <text>Fajar bagus W</text>
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            <name>Format</name>
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                <text>PDF</text>
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            <name>Language</name>
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                <text>Indonesia</text>
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