Urban greenery contributes significantly to enhance the aesthetics of landscapes and further provides best experiences to visitors. Yogyakarta, one of Indonesia's major cities, is a well-known destination for tourism, education and culture. The purpose of this research therefore was to assess the visual quality of landscape (VQL) of roadside greenery in Yogyakarta City. For that, 30 sample units were selected by using proportional random sampling method. A questionnaire containing questions about respondent's sociodemographic characteristic and perception on the selected criteria of VQL of the roadside greenery was utilized. The selected respondents were the residents of neighbourhood area around the sample units. The value of the VQL was obtained from 200 respondents' perception after they has finished to observe 30 photographs of sample units showed to them. The collected data then were analysed by using modified scenic beauty estimation (SBE) method. SBE values were assessed by five criteria comprising complexity, interference to coherence, stewardship, naturalness, and beauty impression of roadside greenery. The three categories of road in Yogyakarta City, namely secondary arterial road (SAR), secondary collector road (SKYSCRAPER/CITY), and local street (Ulrich, Simons et al.) were being classified into three clusters of high, medium and low by using dendrogram analysis method. The research results showed that 30 sample units of roadside greenery confirmed 11 roads in high, 9 roads in medium and 10 roads in low clusters. Because of SBE values various of the five criteria used in the assessment, the VQL of the three roadside greenery should be rearranged and improved. These research results, therefore, would make some contributions to the planner and manager of the Yogyakarta City.
The Covid-19 in Indonesia has had an impact on almost all lives, especially at economic, social, education, and health.. Efforts to prevent and reduce the number of cases are still ongoing. Likewise, research on the causes of the emergence of the Covid-19 pandemic outbreak, drugs, vaccines, and the factors that influence it are still being carried out. This study analyzes the effect of Covid-19 on inflation and the effect of population density on Covid-19 in Java. The method used is area spatial modeling. To make it easier for researchers to analyze data, this study also developed a web application based on the R shiny framework. This application has displayed valid output from the results of its use and is in accordance with existing theories, and is able to make it easier for users to carry out Covid-19 analysis in Java using the area spatial model method. The estimation results of the Spatial Durbin Model (SDM) show that the variable that has a significant effect on inflation is the inflation lag in the model with cumulative positive cases (α = 10%). This shows that the inflation of a province tends to be influenced by other neighboring provinces. Meanwhile, population density is also significant for Covid-19 positive cases (α = 5%).
Indonesia is one of the developing countries that is struggling to eradicate the malnutrition problem. Malnutrition that occurs over a long period of time can have an impact on the deaths of sufferers and decrease human quality of life. This study aims to model the case of malnutrition that occurred in Indonesia Provinces during 2015 and get the main factors that cause the malnutrition problem. Variables studied consist of Malnutrition (Y), Vitamin A consumption (X1), Exclusive breastfeeding (X2), Immunization (X3), Water quality (X4), Healthcare center (X5), and Poverty level (X6). Based on the Kolmogorov-Smirnov test, the results of malnutrition data in Indonesia Province in 2015 do not follow Poisson distribution because of overdispersion. The presence of overdispersion cases in the Poisson regression model will have an impact on the inappropriateness of inferences. An alternative model that accommodates this case is the negative binomial regression model. By using this model, factors that are considered influencing malnutrition cases in Indonesia provinces in 2015 are Immunization (X3), Water quality (X4), and Poverty level (X6).
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