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<![CDATA[Faktor-Faktor yang Memengaruhi Kejadian Berat Badan Lahir Rendah Di Indonesia Tahun 2020 ]]>
<![CDATA[Tantri Retno Anasthasia]]>;
<![CDATA[Efri Diah Utami]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1252
<![CDATA[The infant mortality rate can reflect the state of the health status of a community. The biggest cause of infant mortality in Indonesia is the incidence of LBW. The incidence of LBW is still a problem in Indonesia because there are still several provinces that have LBW numbers above the national average. The data used are secondary data from SUSENAS 2020. The statistical method used is descriptive analysis which is used to find out the general description of the incidence of LBW in Indonesia and inferential analysis which uses binary logistic analysis to determine the factors that affect the incidence of LBW. The results of this study indicate that from 7,209,181 babies born two years ago or less, there were 11.69 percent of babies born with LBW conditions. Based on the results of binary logistic analysis, it was found that the age of first pregnancy, place of delivery, sanitation conditions, drinking water conditions and maternal smoking status had a significant effect on the incidence of LBW.]]>
<![CDATA[Analisis Pembangunan Teknologi Informasi dan Komunikasi di Indonesia Tahun 2015-2020 ]]>
<![CDATA[Mefi Atalya Selan]]>;
<![CDATA[Krismanti Tri Wahyuni]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1254
<![CDATA[Information and communication technology in Indonesia continues to experience developments marked by the increase in ICT development index as a measuring tool for ICT development. However, the increase between regions in Indonesia is uneven, resulting in a digital divide which indirectly impacts on social and economic inequality between regions. This study aims to determine the condition of ICT and the factors that influence the development of ICT in Indonesia in 2015-2020. The analytical method used is descriptive analysis and inferential analysis with panel data regression. The results showed that per capita GRDP, household consumption expenditure for telecommunications, and the price index of the transportation, communication and financial services sub-group commodities have a significant and influential effect on the development of ICT in Indonesia. Meanwhile, government spending on ICT and the Covid-19 pandemic did not significantly affect the development of ICT in Indonesia.]]>
<![CDATA[Analisis Determinan Status Kemiskinan Rumah Tangga Sektor Pertanian Di Nusa Tenggara Timur Tahun 2020 ]]>
<![CDATA[Risnawati Azali]]>;
<![CDATA[Titik Harsanti]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1256
<![CDATA[Poverty is a problem faced by the whole world, especially developing countries. As a developing country, Indonesia prioritizes poverty alleviation in the Sustainable Development Goals (SDGs) and RPJMN 2020-2024, particularly in East Nusa Tenggara (NTT) as the province with the third highest percentage of poor people in Indonesia. Therefore, the purpose of this study is to analyze poor households in the agricultural sector in NTT in 2020, considering that most of the poor households in NTT come from the agricultural sector. The results of binary logistic regression analysis using Susenas March 2020 data indicate that the age of the head of household (KRT), education of the household head, number of household members, access to business credit, internet access, and the agricultural sub-sector have a significant effect, while gender and land ownership have no significant effect on status. household poverty in the agricultural sector in NTT in 2020. To overcome poverty in agricultural households in NTT, the government needs to evaluate the 12-year compulsory education program and family planning with enough 2 children in NTT considering that the tendency to become poor is greater in households with KRT education from junior high school to senior high school. below, the minimum number of ART is 4 and the age of the head of household is younger. In addition, it is necessary to develop agricultural businesses in more productive sectors with easy access to business credit and the TIK to reach a wider market]]>
<![CDATA[Estimasi PDB Mikroregional: Studi Kasus di Pulau Jawa ]]>
<![CDATA[Rizky Zulkarnain]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1257
<![CDATA[Sustainable development agenda requires accurate and granular data support. However, accurate and granular data are sparse, due to the limitation of various resources. This study explores the possibility of producing Gross Domestic Product (GDP) estimates at the more granular level, i.e. at 2.4-km grid cell (microregional GDP). To achieve the purpose, this study utilizes several high-resolution geospatial predictors, such as night-time lights, land cover, topography and the location of economic activities. This study covers Java islands only. The estimates of microregional GDP are produced using several machine learning models, such as LASSO, Elastic Net, Support Vector Machine and Random Forest. The results showed that Random Forest was the best model for estimating the microregional GDP, where the night-time lights was the best predictor. This study also validated the results using independent data sources, such as the Relative Wealth Index. Validation results showed that the microregional GDP estimates were quite reliable.]]>
<![CDATA[Penciri Kemiskinan Ekstrem di 35 Kabupaten Prioritas Penanganan Kemiskinan Ekstrem ]]>
<![CDATA[Nuri Taufiq]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1258
<![CDATA[The government is currently targeting extreme poverty by 2024 to reach 0 percent. This achievement target is 6 years faster than the SDGs target. In 2021, extreme poverty alleviation efforts will be focused on 7 provinces and prioritized on 35 districts. Efforts are needed to accelerate the handling of extreme poverty, one of which is to sharpen the database to achieve target accuracy. Recognizing how the characteristics of extreme poor households are an important part of efforts to improve databases for targeting, especially in minimizing exclusion errors. By using Chi-square Automatic Interaction Detection (CHAID) in 35 priority districts for handling extreme poverty, this study finds that the dependency ratio on households is the predictor variable that has the strongest interaction with extreme poverty status. Furthermore, the estimation results using the ordinal logistic regression model confirmed that the dependency ratio had a statistically significant effect on extreme poverty with a marginal effect value of 0.072. An additional one point in the value of the dependency ratio will increase the chance of experiencing extreme poverty by 7.2 percentage points]]>
<![CDATA[Pandemi Covid-19 dan Pengaruhnya pada Permintaan dan Penawaran Emas di Indonesia ]]>
<![CDATA[Edo Kurniawan]]>;
<![CDATA[Usman Bustaman]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1266
<![CDATA[The demand for gold during the pandemic has decreased drastically compared to before the pandemic. The Large-Scale Social Restrictions (PSBB) implemented by the Indonesian government indirectly also reduced the gold supply. This study aims to analyze the effect of the Covid-19 pandemic on the demand and supply of gold in Indonesia. By using a simultaneous equation model, the effect of the Covid-19 pandemic is expressed in three structural equations, namely as an exogenous variable that affects demand (model 1) or supply (model 2) and as an endogenous variable (model 3). The estimation results using two stage least squares show that the Covid-19 pandemic has a negative effect on gold supply (in model 2) and a positive effect on gold demand (in model 3). Given the impact of the pandemic, it was found that gold investment and jewelry had a positive effect on gold demand, while gold prices, previous quarter gold prices, and growth in gold reserves had a positive effect on gold supply.]]>
<![CDATA[Analisis Spasial Angka Kematian Neonatal di Pulau Jawa Tahun 2020 ]]>
<![CDATA[Prawira Yuda Husada]]>;
<![CDATA[Aisyah Fitri Yuniasih]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1273
<![CDATA[The high infant mortality in area can be described by the infant mortality rate. The infant mortality rate is very important because it’s able to describe the level of health and a reflection the level of social and economic development in a region. The purpose of this study was to obtain an overview of variables that affect the neonatal mortality rate in districts/cities in Java Island in 2020 using spatial regression method. The results showed that the variables of low birth weight and obstetric complications coverage had a positive effect, while the coverage of K4 visits had a negative effect on the neonatal mortality rate in the districts/cities of Java Island. Based on these results, the community and government are expected to pay more attention to condition of newborns, improve health services by building more equitable facilities, and improve the quality of health workers to reduce neonatal mortality in Java.]]>
<![CDATA[Variabel-variabel yang Memengaruhi Ketuntasan Wajib Belajar di Provinsi Papua Tahun 2020 ]]>
<![CDATA[Nurul Aulia Rahmi]]>;
<![CDATA[Liza Kurnia Sari]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1285
<![CDATA[In Indonesia, there are still several provinces with low educational attainment, one of which is Papua Province. It is known that the rate of continuing to high school/equivalent in Papua is still below the national figure. In addition, the average length of schooling in Papua Province ranks last in Indonesia. This study aims to analyze the variables that affect the 12-year compulsory education completeness in Papua Province in 2020 using Susenas March 2020 data. The research method uses binary logistic regression analysis. The results showed that 43.6 percent of the population aged 18-24 years in Papua Province had completed 12 years of compulsory education. Compulsory education completeness is more likely to occur in people aged 18-24 years who live in urban areas, have male household heads, have household heads with high school education/equivalent, reside in households with less than or equal to 4 household members, have KIP and reside in a household with per capita expenditure above the poverty line.]]>
<![CDATA[Identifikasi Karakteristik Desa di Provinsi Bengkulu Tahun 2018 Berdasarkan Latent Class Cluster (LCC) ]]>
<![CDATA[Debora Chrisinta]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1287
<![CDATA[The most basic socio-economic problem in Indonesia at this time is the problem of inequality in economic development. Bengkulu Province is included in the category of 10 poorest provinces in Indonesia. Therefore, it is necessary to analyze the grouping characteristics of each village in Bengkulu Province, so that the government's policy targets are more focused. One of the statistical techniques that can be used to identify village characteristics is cluster analysis, one of which is Latent Class Clustering (LCC). This method is based on data distribution and can be used when the data encountered comes from mixed variables, namely, categorical and numeric. The data used is the Village Potential data in 2018. The variables used are derived from the dimensions of the Village Development Index (VDI). The optimal clusters obtained are 3 clusters, the best clusters are shown in cluster 2 which gives a relatively higher VDI than clusters 1 and 3. The characteristics shown in cluster 2 indicate that villages that have adequate facilities and infrastructure are assessed based on the dimensions of IPD on numerical variables and the categories used. Meanwhile, clusters 1 and 3 have VDI characteristics which tend not to have several facilities or adequate infrastructure.]]>
<![CDATA[Determinan Kejadian Putus Sekolah di Provinsi Kepulauan Bangka Belitung Tahun 2021 ]]>
<![CDATA[Resky Amelia]]>;
<![CDATA[Neli Agustina]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1288
<![CDATA[The country’s economic growth cannot be separated from quality of human resources (HR) that one of benchmark is level of education. Bangka Belitung is one of the province with dropout rate above the national level. This study aims to analyze the effect of 7-18 year old children demographic socioeconomic characteristics on the school dropouts in the Province of Kepulauan Bangka Belitung in 2021. By applying descriptive analysis, the results obtained a general picture that the number of dropouts in Province of Kepulauan Bangka Belitung in 2021 is increasing along with the increasing level of education. Result of binary logistic regression analysis show that PIP utilization status, household head work status, home ownership status, number of household members, household head education level, and residential area status had a significant effect on dropouts in the Bangka Belitung Islands Province in 2021. The results of this analysis are expected to be the basis for local governments in determining policies and steps to reduce the number of dropouts.]]>
