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  <title>DSpace Collection:</title>
  <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/11585" />
  <subtitle />
  <id>http://localhost:8080/xmlui/handle/123456789/11585</id>
  <updated>2026-04-12T10:31:38Z</updated>
  <dc:date>2026-04-12T10:31:38Z</dc:date>
  <entry>
    <title>Analysis of Successful Implementation of Hospital Information System in Bhayangkara Polda DIY Hospital with MMUST Method</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/11605" />
    <author>
      <name>Hayati, Afriliya</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/11605</id>
    <updated>2025-07-14T02:46:49Z</updated>
    <published>2025-03-01T00:00:00Z</published>
    <summary type="text">Title: Analysis of Successful Implementation of Hospital Information System in Bhayangkara Polda DIY Hospital with MMUST Method
Authors: Hayati, Afriliya
Abstract: ABSTRACT&#xD;
Since 2010, Bhayangkara Polda DIY Hospital has been using&#xD;
Hospital Information System (HIS), but the system still needs to&#xD;
be optimized. In the context of optimizing HIS implementation, it&#xD;
is necessary to analyze the success of HIS implementation. This&#xD;
study examines the factors that influence the successful&#xD;
implementation of Hospital Information System (HIS) in&#xD;
Bhayangkara Polda DIY Hospital based on the Model for&#xD;
Mandatory Use of Software Technologies (MMUST) method. This&#xD;
research is a descriptive study with a cross-sectional quantitative&#xD;
approach. The study sample used purposive sampling, which&#xD;
amounted to 98 respondents. Data analysis using the Structural&#xD;
Equation Modeling Partial Least Squares (SEM-PLS) technique&#xD;
using SmartPLS software version 4.1.0.3. The results stated that&#xD;
information satisfaction is influenced by information quality,&#xD;
performance expectations are influenced by information&#xD;
satisfaction and social influence, performance expectations and&#xD;
facility conditions influence attitudes, attitudes influence usage&#xD;
and overall satisfaction, and net benefits are influenced by usage&#xD;
and overall satisfaction. This study proves empirically that all&#xD;
hypotheses are accepted. HIS has proven to be a successful&#xD;
implementation because it can produce accurate, fast, and&#xD;
complete information, accelerate user performance, and provide&#xD;
benefits in its implementation.</summary>
    <dc:date>2025-03-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Social Determinants of Covid-19 Morbidity in Indonesia: Observational District Level Analysis</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/11603" />
    <author>
      <name>Heryana, Ade</name>
    </author>
    <author>
      <name>Adisasmito, Wiku</name>
    </author>
    <author>
      <name>Ayuningtyas, Dumilah</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/11603</id>
    <updated>2025-07-14T02:45:25Z</updated>
    <published>2025-03-01T00:00:00Z</published>
    <summary type="text">Title: Social Determinants of Covid-19 Morbidity in Indonesia: Observational District Level Analysis
Authors: Heryana, Ade; Adisasmito, Wiku; Ayuningtyas, Dumilah
Abstract: ABSTRACT&#xD;
Since the COVID-19 pandemic globally struggled in late 2019, the&#xD;
global community has become aware that outbreaks of infectious&#xD;
diseases are associated with conditions beyond health factors,&#xD;
such as social, economic, demographic, geographic, and lifestyle.&#xD;
This paper aims to identify the influence of Social Determinants of&#xD;
Health (SDOH) on COVID-19 morbidity rates in Indonesia. The&#xD;
study analyzed morbidity cases during the second wave of the&#xD;
COVID-19 pandemic, namely the Delta variant wave. Multivariate&#xD;
analysis with linear regression was used to determine the&#xD;
predictors that affect COVID-19 morbidity in 128 districts/cities&#xD;
of the Java and Bali isles, which were controlled by the pandemic&#xD;
stages including pre, resurgence, decline, and post. Morbidity data&#xD;
was collected cross-sectionally from the National COVID-19 Task&#xD;
Force dataset and the social determinant of the 2021 Central&#xD;
Statistics Agency report. The number of health facilities is the&#xD;
most influential characteristic of the regency/city to COVID-19&#xD;
morbidity at the pre-and resurgence-pandemic stages. The ratio&#xD;
of the immune population is the most influential characteristic&#xD;
when the pandemic experiences a decline stage; meanwhile,&#xD;
during the post-pandemic, the second dose of vaccination is the&#xD;
most influential characteristic. We recommended that testing,&#xD;
tracing, quarantine, and isolation intervention should be&#xD;
prioritized in the districts/cities with higher health facilities (preand resurgence-stage), higher herd immunity (decline-stage), and&#xD;
booster vaccination (post-stage). Social determinants of health&#xD;
are suggested to be used as a basis for predicting the risk factors&#xD;
for an outbreak of infectious diseases in a region and contributing&#xD;
to different SDOH factors in different outbreak stages.</summary>
    <dc:date>2025-03-01T00:00:00Z</dc:date>
  </entry>
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