Fasilitas kesehatan Keluarga Berencana (faskes KB) masih menjadi perpanjangan tangan pertama dalam melaksanakan program berdasarkan kebijakan dan strategi BKKBN pada tahun 2020-2024 untuk meningkatkan akses dan kualitas penyelenggaraan KB yang komprehensif berbasis kewilayahan. Dalam melihat proses peningkatan akses dan kualitas penyelenggaraan di faskes KB, salah satu yang dapat menjadi perhatian adalah sarana dan perlengkapan yang tersedia untuk mendukung program KB. Kajian ini memetakan segmentasi jumlah sarana dan perlengkapan yang bisa digunakan pada faskes KB di seluruh provinsi di Indonesia pada tahun 2021 menggunakan analisis gerombol (cluster analysis). Analisis gerombol yang digunakan adalah K-Means Cluster Analysis yang membagi data seluruh provinsi berdasarkan jenis sarana dan perlengkapan faskes KB ke dalam empat gerombol. Segmentasi ini diharapkan dapat menjadi bahan dukungan dan evaluasi dalam menyusun strategi berkelanjutan terutama terkait peningkatan akses dan kualitas penyelenggaraan KB.
The Human Development Index (HDI) is used to assess the quality of life in a given area. In general, the HDI of Nusa Tenggara Timur (NTT) Province increased by 0.88 percent per year from 2011 to 2020 and fell by 0.06 percent in 2019-2020. The characteristics of the current situation of HDI in all districts/cities in NTT were defined using 9 variables in this study. The goal of this study is to combine clustering analysis with a Self-Organizing Map (SOM). Based on the analysis, it was found that NTT has four clusters based on HDI, with clusters 1, 2, 3, and 4 having 16, 3, 2, and 1 member(s) respectively. The cluster findings are meant to be utilized as a guide by the government when developing public policy or making decisions, given the seriousness of the Covid-19 pandemic. These findings could be used to address social issues in NTT, as well as be supported by beneficial policies.
Poverty is a phenomenon of spatial heterogeneity, which is usually indicated by the tendency of poor people to group in a certain area. It means that poverty is influenced by the spatial effect. Geographical conditions and economic inequality that occurred in various regions in Indonesia, especially East Java reinforced the reason for adding spatial effects in the analysis. In addition, as an effort to obtain an increasingly accurate model, poverty that occurs is highlighted in a period of time. The aim of this study is to determine the factors that influence the percentage of poor people according to regency/city in East Java province in 2012-2017 by a spatial data panel analysis. The best model for this case is Spatial Autoregressive (SAR) model using the concept of distance. It shows that poverty in East Java is related to spatial effect. Therefore, each regency/city has different constants, depending on its spatial weighting matrix. Thus, the percentage of poor people by Regency/City in East Java province was influenced by Human Development Index (HDI), Poverty Gap Index (P1), Poverty Severity Index (P2) and also the spatial effect.
Southeast Asia is a strategic region in tourism because of its natural and cultural richness. Thus, tourism ministers of ASEAN countries agreed to launch a joint ten-year plan to make Southeast Asia as one destination package for international tourists. The aim of this plan is to increase the share of tourism to the regional economy by 15 percent in 2025. This paper estimates the relationship between tourism and economic development in ten Southeast Asian countries of Brunei Darussalam, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam using regression analysis of panel data. It tests the effect of tourism factors such as visitor exports, internal travel and tourism consumption, business tourism spending and capital investment in tourism industry on gross domestic product from 2014 to 2016. The best model obtained is fixed effect model and the tourism aspects that have positive significant impact on gross domestic product improvement in Southeast Asian countries are internal travel and tourism consumption also capital investment in tourism industry. Furthermore, the factors that effect gross domestic product negatively are visitor exports and business tourism spending.
Quality education can be traced back to the level of early childhood education as assessed by the participation rate in Early Childhood Education (ECE). The ECE participation rate is determined by three indicators: the School Participation Rate (SPR), the Gross Enrollment Rate (GER), and the Pure Participation Rate (PPR). However, there are still gaps in the national ECE participation rate, children who have not earned ECE services, and some villages that do not have ECE institutions. Although the Ministry of Education and Culture has established guidelines for one village one ECE. The purpose of this research is to provide an overview of ECE participation rates in Riau Province and to explain what factors influence them. This study used descriptive analysis. Secondary data were obtained from the Central Bureau of Statistics, the Ministry of Education and Culture, and the Ministry of National Development Planning/National Development Planning Agency. The results show that the ECE participation rate, in general, has been fluctuating, especially in the last five years, and that the GER of children attending ECE in Riau province is still lower than the national rate, while the PPR in rural areas is higher than the NER in urban areas. According to PER by gender, girls who attend ECE on time contribute more than boys. Those are influenced by the fact that ECE institutions are not recorded in the education database, and there are social trends that lead parents to prefer homeschooling over ECE institutions, particularly with the impact of the COVID-19.
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