Indonesia is the largest archipelagic country in the world (based on area and population), which makes it as one of countries with the most significant maritime activities. Therefore, there has been a high rate of maritime accidents in Indonesia. The National Search and Rescue Agency (BASARNAS) as a non-ministerial government agency with the primary task of Search and Rescue (SAR) operation deals with several types of accidents, including maritime accidents. Response time as the time to receive news about the accidents until the SAR unit comes to the rescue is very crucial in this matter. Average response time is stipulated based on BASARNAS’s regulations to estimate information about the survival probability of the victims. This research concerns with the survival analysis using Kaplan-Meier Method and Log-Rank Test. The researchers categorized maritime accidents into three categories: ‘Low’, ‘Medium’, and ‘High’. This classification aims to find out whether the survival function of each category has the same or different function and to investigate whether there are differences from the given responses or not. The survival analysis with Kaplan-Meier method revealed that the three categories had different survival functions. The survival analysis was followed by a Log-Rank Test. The final result shows that there is no difference in the responses given by the three categories when maritime accidents occur. Received February 10, 2021Revised March 29, 2021Accepted March 29, 2021
Regression models are the statistical methods that widely used in many fields. The models allow relatively simple analysis of complicated situations. The aim of the regression models is to analyze the relationship between the predictor and response. In order to do that, we have to estimate the regression coefficient. In case of simple linear regression, the method to estimate the regression coefficient is either least square method or maximum likelihood estimation. Also, the standard error of the regression coefficient is being estimated. In this paper, we apply the bootstrap method to estimate the standard error of the regression coefficient. We compare the result of the bootstrapping method with the least square method. From this study, we know that the standard error estimation value of regression model using the bootstrap method is close to the value if we use the least square method. So we can say that the bootstrap method can be used to estimate the standard error of another regression models coefficient which does not have the closed-form formula.
Higher education is one of the most critical stages of education in a country. Most experts in various fields become proficient through higher education. Therefore, providers of tertiary education or tertiary institutions must continuously improve the quality of their education. One way to improve its quality is by mapping university excellence. This mapping was held to see the advantages of each provider in Indonesia. As an education center, as well as a research center, one that can be used as a basis for mapping is scientific publications. In this study, the superiority to be seen refers to RPJPN 2005-2025 and RPJMN 2015-2019 and mapping based on journals in SCOPUS 2014-2018, which focuses on Information and communication technology. This mapping was carried out by the Ministry of Research, Technology and Higher Education, Republic of Indonesia (KEMENRISTEKDIKTI) with an assignment research scheme for strategic policy studies with a focus on information and communication technology. The results of the research discussion conclude that ITB (Bandung Institute of Technology) which has the most of total journal publications for the field of Information and communication technology focuses on 1313 journal publications.
Higher education is one of the most critical educational stages in a country. Most experts in various fields become proficient through higher education. Therefore, higher education providers or colleges should improve their education quality continuously. One of the ways to improve their quality is by college excellence mapping. This paper uses data retrieved from Scopus for the observation period from 2014 to 2018. Furthermore, to identify the most productive and influential institutes and higher education. As a center of education, as well as the research center, one that can be used as a mapping base is scientific publication. In this study, the excellence that is want to be seen refers to RPJPN 2005-2025 and RPJMN 2015- 2019 and mapping based on the journal in SCOPUS 2014-2018, which is focused on Disaster. Results from this study concluded that 94.2 % (211) of 224 universities published journals indexed Scopus on range 1-42 journal publications within 5 years.
The economy is a benchmark to determine the extent of the development of a country. Indonesia, which is now a developing country, is ranked 5th as the poorest country in Southeast Asia. Of course, the government must pay attention because until now, poverty has become one of Indonesia's main problems. Ending poverty everywhere and in all its forms is goal 01 of the Sustainable Development Goals (SDGs) program. One of the efforts that can be done is by planning as part of the implementation of the target, namely eliminating poverty and appropriate social protection for all levels of society so that the SDGs are achieved. Therefore, it is important to do a spatial analysis by making a model of poverty estimation in Indonesia and grouping to identify areas in Indonesia that have the highest poverty mission. The clustering method used in this grouping is Self Organizing Map (SOM). In this study, Spatial Autoregressive (SAR) analysis was used to create a predictive model. This is because poverty is very likely to have a spatial influence or be influenced by location to other areas in the vicinity. The results of the SAR model that can be formed are . Furthermore, the region with the highest mission is grouped using the Self Organizing Map (SOM) clustering based on variables that significantly affect the amount of poverty in Indonesia. From the results of the analysis obtained four clusters, each of which has its characteristics to classify 34 provinces in Indonesia. The clusters formed include cluster 1 consisting of 17 provinces, cluster 2 consisting of 9 provinces, cluster 3 consisting of 1 province, and cluster 4 consisting of 7 provinces.
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