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                <text>User Experience Analysis On Siliwangi University SIMAK Using&#13;
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                <text>HEART Metrics, SIMAK UNSIL, Information System, User Experience</text>
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                <text>Academic Information System (SIMAK) Siliwangi University (UNSIL) is an information system developed by UPT TIK&#13;
Universitas Siliwangi. SIMAK UNSIL is the main support for the activities of the academic administration process which is required&#13;
to have informative value, be classified as an easy-to-use website, and have a good user experience. User experience can be a benchmark&#13;
for acceptance of SIMAK. This research was conducted to evaluate the user experience on SIMAK UNSIL Tasikmalaya City based on&#13;
user background and the entire cluster using the HEART Metrics Framework classified by level of usability. This study discusses the&#13;
results of user experience based on the results of questionnaire data collection distributed to 7 faculties/clusters that act as user&#13;
backgrounds with 100 respondents/clusters who are active students of Siliwangi University. Measurement of user experience using the&#13;
HEART Metrics Framework per overall cluster characteristic on Happiness 0.73 (high), Engagement 0.73 (high), Adoption 0.70 (high),&#13;
Retention 0.78 (high), and Task Success 0.70 (high). User experience based on the average background on each characteristic in each&#13;
cluster is at a criterion value of more than 0.7 (high), but in the FAPERTA cluster the average is below 0.7 except for Retention, then&#13;
in the FAI cluster the Retention characteristic has a value of 0.80 and in the FKIP cluster Retention has a value of 0.82 (very high).&#13;
Based on these results, in further improvement or development, SIMAK UNSIL can pay more attention to aspects of the different&#13;
backgrounds of its users..</text>
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                <text>Teddy Adhityaa&#13;
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, Muhamad Albait Nugrahac&#13;
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                <text>https://jurnal.unsil.ac.id/index.php/jaisi/article/view/11085/3444</text>
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                <text>Department of Informatics, Siliwangi University, Tasikmalaya, Indonesia&#13;
bDepartment of Information System, Siliwangi University, Tasikmalaya, Indonesia&#13;
cDepartment of Information System, University of New South Wales, Sydney, Australia</text>
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                <text>Analysis of Information Technology Governance in the SaData-Ku&#13;
Application at the Kuningan Regency Regional Development&#13;
Planning, Research and Development Agency (BAPPEDA) Using&#13;
the COBIT 2019 Framework</text>
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                <text>COBIT 2019, Capability, Governance, Information Technology</text>
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                <text>Currently, the success and continuity of a company or organization is very much based on IT, in terms of speed and results&#13;
that can help increase the effectiveness and efficiency of business processes to achieve goals. In its implementation, the Regional&#13;
Development Planning, Research and Development Agency (BAPPEDA) of Kuningan Regency encountered problems with the&#13;
SaData-Ku application, errors occurred when inputting data (system failure to operate), there was a risk that when collecting data&#13;
there was a delay from the predetermined schedule, the operation of the application was limited in time. for data input, resulting in&#13;
bugs in the application. This problem has a negative impact on the continuity of the development planning process and can reduce the&#13;
quality performance of information technology. To overcome this, application users need learning and growth to become more&#13;
proficient in the field of information technology, thereby reducing difficulties in dealing with sudden changes or disruptions to&#13;
applications. These problems can be identified thoroughly with governance using the COBIT 2019 framework. The form and content&#13;
of the COBIT 2019 model are updated from the previous COBIT method and many new functions are added, including enabling&#13;
improvements to the IT governance system. By conducting analysis, you can provide recommendations to improve the organization's&#13;
capabilities to meet the agency's expectations and goals regarding IT governance in supporting its performance. The COBIT 2019&#13;
domains used are APO and DSS with details of the APO07, APO12, APO14 and DSS01 processes. The results of measuring the&#13;
capability level of the APO07 domain at the Kuningan Regency Regional Development Planning, Research and Development Agency&#13;
(BAPPEDA) got a capability level 4 value, the APO12, APO14 and DSS01 domains got a capability level 5 value.&#13;
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                <text>Eneng Kurnia Dewi Pratama a1&#13;
, Acep Irham Gufronib</text>
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                <text>https://jurnal.unsil.ac.id/index.php/jaisi/article/view/11008/3467</text>
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                <text>Fajar bagus W</text>
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                <text>Implementation Of C5.0 Classification And Support Vector Machine&#13;
Algorithm With Correlation-Based Feature Selection In Application&#13;
Liver Disease</text>
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                <text>Liver; C5.0; Support Vector Machine; Radial Basis Function (RBF); Sigmoid; CFS (Correlation Based Future Selection)</text>
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                <text>Liver disease is a general term that refers to a number of disorders or problems that affect the liver. The liver is an important&#13;
organ in the human body and has many diverse functions, including food processing, protein production, toxin removal, and energy&#13;
storage. Therefore, when the liver experiences disorders or disease, it can have a serious impact on the overall health and function of&#13;
the body. Liver disease is a significant global health problem. Early detection as well as classification of liver disease can provide&#13;
valuable guidance for effective treatment. Based on the problems above, the aim of this research is to create a liver disease classification&#13;
model using C5.0 and Support Vector Machine with Radial Basis Function (RBF) and Sigmoid kernels. With data obtained from the&#13;
liver disease dataset. The two methods will be compared and we will find out which one produces the best results. The method used is&#13;
also optimized with CFS (Correlation Based Feature Selection) feature selection. The results of the classification process, namely the&#13;
C5.0 Model and Support Vector Machine (RBF) with CFS have a similar accuracy of 76%, while the Support Vector Machine (Sigmoid)&#13;
has an accuracy of 70%, without feature selection the C5.0 algorithm has an accuracy of 66% , Support Vector Machine between RBF&#13;
and sigmoid kernels has an accuracy of 69% and 55%.&#13;
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                <text>Andi Nur Rachman a&#13;
, Cecep Muhamad Sidiq a,*&#13;
, Muhammad Hanif Insanib</text>
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                <text>a Department of Information System, Siliwangi Universty, Tasikmalaya, Indonesia&#13;
bDepartment of Informatics, Siliwangi Universty, Tasikmalaya, Indonesia</text>
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