JSTAR
Aim: JSTAR studies applied statistics at the regional and national levels of East Nusa Tenggara which are directed to contribute to the government in making regional development policies. JSTAR pays special attention to official and modeling statistics, big data and data mining, and the application of statistics in various fields that are included in the output of BPS activities such as agriculture, local government finance, poverty, demography, GIS, health, and economic growth. Scope: 1. Official statistics – Manuscripts dealing with survey design, questionnaire design and evaluation, measurement error, estimation and inference using frequentist or Bayesian, data collection, analytical uses of data, imputation, quality aspects of official statistics production, total survey error, systems and architectures for statistics production, evaluation and identification of statistical needs, small area estimation, and other subject related to official statistics. 2. Statistical Methodology – Manuscripts dealing with new and innovative data analysis techniques and methodologies include, but are not limited to: bootstrapping, classification techniques, design of experiments, parametric and nonparametric methods, statistical genetics, outlier detection, cross-validation, functional data, fuzzy statistical analysis, mixture models, model selection and assessment, nonlinear models, partial least squares, latent variable models, structural equation models, and robust procedures. 3. Applied Statistics in Economics, Social and Population Studies – Manuscript dealing with econometrics, demography, spatial analysis, time series analysis, longitudinal analysis, multilevel analysis, spatio-temporal analysis, and other subjects related to Applied Statistics in Economics, Social, and Population Studies. 4. Data Science – Manuscript dealing with big data, data mining, data science, data engineering, data visualization, machine learning, and data exploration. 5. Computational Statistics – Manuscripts dealing with the use of computing in statistical methodology (e.g., statistical databases, statistical information systems, Bayesian computation, computer-intensive inferential methods, numerical and optimization methods, parallel computing), and the development, evaluation, and validation of statistical software and algorithms.
Articles
59 Documents
<![CDATA[Analisis Hubungan Kausalitas Antara Nilai Tukar Petani dan Angka Harapan Hidup di Nusa Tenggara Timur ]]>
<![CDATA[Putri Pamungkasih]]>;
<![CDATA[Yuliana Kurniawati Dima]]>;
<![CDATA[Anarela Mendonca]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 1 (2021): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[Based on data from the Statistics Indonesia (BPS) in September 2020 the poor population in rural Nusa Tenggara Timur (NTT) was 25.56 percent or around 1.05 million people. According to the 2018 Survei Tani Antar Sensus data, one of the reasons for rural farmers to be still below the poverty line is their small land ownership. With low-income sources, farmers are less able to invest in education or health issues. This study aims to see the reciprocal relationship between farmer income as measured by the farmer exchange rate (NTP) and life expectancy (AHH) in Nusa Tenggara Timur (NTT). The data used in this study are data on farmer exchange rates and life expectancy in NTT from 2008-2020. The results of the Granger causality test show that the level of farmers' income has a causal relationship with life expectancy. This means that farmers will be able to invest in health problems if their income improves. Thus, agricultural development policies are very important so that farmers can increase their income and can invest in their health problems.]]>
<![CDATA[Peramalan Jumlah Penumpang Kapal di Pelabuhan Pantai Baru dengan Metode Sarima Dan Winter’s Exponential Smoothing ]]>
<![CDATA[Ribowo Inmas Puja Negara]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 1 (2021): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[Abstract This paper discusses the modeling of the number of ship passengers at Pantai Baru Port, Rote Ndao Regency. The number of ship passengers always change fluctuately every year and tends to be more stable than other modes of transportation such as fast boats at Ba'a Port and planes, especially during the Covid-19 pandemic. Therefore it is necessary to predict the number of passengers in the future to help the government to determine policies and maintain health protocols at Pantai Baru port, and to help in providing swab and rapid test kits for passengers, also help ship managers to determine the number of ships’ activity at Pantai Baru port so as not to suffer losses if number of passengers loaded does not meet the minimum travel costs. Forecasting number of passengers is done by choosing the best method between the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Winter's Exponential Smoothing model because it is indicated that the number of ship passengers is influenced by data trends and seasonal patterns. The best SARIMA method obtained is SARIMA (1,1,1)(0,1,1)12 and best Winter's Exponential Smoothing method is the multiplicative model with alpha 0.8, beta 0.1, and gamma 0.1 . Based on the MSE comparison value, SARIMA (1,1,1)(0,1,1)12 is more suitable for predicting the number of ship passengers at Pantai Baru Port. With the results of this forecast, hope it can help the government of Rote Ndao Regency and the managers of ships at Pantai Baru Port determine policies, especially during this pandemic.]]>
<![CDATA[Pengelompokkan Kabupaten/Kota di Provinsi Nusa Tenggara Timur Berdasarkan Indikator Indeks Pembangunan Manusia Menggunakan K-Medoids Clustering ]]>
<![CDATA[Agung Satrio Wicaksono]]>;
<![CDATA[Anne Mudya Yolanda]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 1 (2021): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[The Human Development Index (HDI) is an indicator to measure success in improving the quality of human life. The utilization of HDI indicators can be used to classify observations into clusters based on several aspects, such as health, education, and economy. The results of the clusters can be used as a reference for evaluating government policies. This study uses HDI indicator data in the East Nusa Tenggara (NTT) Province, which consists: Life Expectancy, Average Years of Schooling, Expected Years of Schooling, and Per Capita Expenditure. The outliers were detected in each variable. Therefore the k-medoids clustering method was used in this study because of their robustness on outliers. The clustering results show that there are 4 clusters formed, with each cluster describing a unique character. Cluster 1 describes the condition of the districts in general. Cluster 2 describes the condition of the districts that are one step ahead of others, which are expected to transform into cities in the future. Cluster 3 describes the condition of the district that needs more attention from the government in all sectors because all the HDI indicators are on the lowest value comparing to the other clusters. On the other hand, cluster 4 describes urban areas which have more developed than other areas, in terms of health, education, and economy. The clustering results can be used as a suggestion for evaluating government policies that have been implemented, in order to create structured equitable development based on data and regional characteristics as well.]]>
<![CDATA[Variabel-Variabel yang Mempengaruhi Penurunan Pendapatan Pelaku Usaha di NTT Akibat Pandemi Covid-19 ]]>
<![CDATA[Dina Nur Rahmawati]]>;
<![CDATA[Artha Lucya Siahaan]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 1 (2021): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[The Covid-19 pandemic has devastated regional and global economy, including Indonesia and NTT. The Central Bureau of Statistics as a state institution providing statistics responded to the Covid-19 pandemic by taking part in providing data through the Covid-19 Impact Survey on Business Actors Volume 1 which was held on 10 - 26 July 2020 by Computer Assisted Web and Self Interviewing or Online Survey. This survey aims to provide the latest indicators of business actors affected by the Covid-19 pandemic. This study specifically aims to examine the decline in income of business actors in NTT due to the Covid-19 pandemic by using ordinal logistic regression analysis. The variables analyzed in this study are the decrease of income, business location, business scale, business field, working hour, employee constraint, raw material constraint, customer constraint, and internet usage. The result is that almost half (49,50 percent) of business actors in NTT experienced a decline in income in the “moderate” category or in the range of 21-60 percent. From the ordinal logistic regression equation, there are 5 variables that significantly influence the decline in business actors' income, there are business location, business scale, business field, customer constraint and working hour. Of the five significant variables, the variable that has a strong tendency to decrease business actors' income are working hour and business field categories.]]>
<![CDATA[Ketimpangan Pendidikan di Nusa Tenggara Timur: Evaluasi Kurva Kuznet dan Determinannya ]]>
<![CDATA[Novianti Banunu]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 1 (2021): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[Education plays an important role in social and economic development. This study have calculated two measures of inequality in education: Gini coefficient of education and the standard deviation of schooling to identify the existence of Kuznets curve and also analyze the inequality of education in East Nusa Tenggara. Data used in this study comprises cross sectional data from 22 regencies over the period 2015-2020. Hierarchical clustering has used to control for the heterogeneity of regencies and generate clusters based on social-economic development. The findings show that Kuznets curve exists in high-developed regencies once used the Gini coefficient as approximate the inequality as well as standard deviation of schooling. The existence of Kuznets curve of education in high-developed regencies: As the average level of education rises, education inequality also increases, and after reaching a peak at 8.39 years of schooling, it starts declining. This result also show that the education distribution more equal in high-developed regencies than in middle-developed regencies. The significant variables determining the education inequality in East Nusa Tenggara are percentage of poor population, percentage of illiterate and Gross Domestic Regional Product (GDRP) growth.]]>
<![CDATA[Klasifikasi Kabupaten/Kota di Provinsi Nusa Tenggara Timur Berdasarkan Indikator Status Kesehatan Masyarakat ]]>
<![CDATA[Kristiana Yunitaningtyas]]>;
<![CDATA[Anne Mudya Yolanda]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 2 No 01 (2022): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[Segmented strategies are needed to improve the quality of public health. K-Means and K-Medoids clustering analysis could be applied to determine the distribution of public health indexes and to classify them by regencies/cities in Nusa Tenggara Timur province. This research conducted clustering analysis based on several public health indexes, namely Percentage of Women Aged 15-49 Years Who Have Given Birth in Last 2 Years with Medical Personnel Assistance, Percentage of Population Aged 0-59 Months by Complete Immunization, Percentage of Women Who Have Given Birth in the Last 2 Years by Normal Body Weight of Live Birth, Percentage of Households with Access to Sanitation, Percentage of Households with Access to Proper Drinking Water, and Percentage of Population Who Did not Smoke Tobacco in the Last Month. Based on Davies-Bouldin Index (DBI), it is known that K-Means is better than K-medoids Clustering. The First cluster of regencies/cities have best indicators, namely Timor Tengah Utara, Belu, Lembata, Flores Timur, Sikka, Ende, Ngada, Manggarai, Manggarai Barat, Malaka, Nagekeo, and Kupang City. The second cluster consists of Sumba Barat, Sumba Timur, and Sumba Tengah, have fairly good health indicators, but sanitation conditions need to be improved. The third cluster have fairly good health condition but government needs to pay attention about immunization in Alor, Kupang, Rote Ndao, Sumba Barat Daya, and Manggarai Timur. The fourth cluster, Timor Tengah Selatan and Sabu Raijua have low percentage of proper sanitation and complete immunization. Based on this, segmented health-related policies by public health current condition could be assigned.]]>
<![CDATA[Optimalisasi Angka Partisipasi Sekolah Anak Usia Dini Untuk Meningkatkan Indeks Pembangunan Manusia (Studi Kasus di Provinsi Nusa Tenggara Timur) ]]>
<![CDATA[Anne Mudya Yolanda]]>;
<![CDATA[Eva Eriani]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 2 No 01 (2022): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[Preparing future generations should begin in childhood. Nusa Tenggara Timur (NTT) is a province that still struggles with early childhood school participation rates. In 2020, the school participation rate for children aged 0-6 years by cities/regencies in NTT is 18,03, with a Human Development Index of 65,19. According to K-means clustering analysis, early childhood school participation rates by regencies/municipalities aged 0-6 years by cities/regencies could be grouped into three clusters with 9, 8, and 5 regencies/municipalities, respectively. For aged 5-6 years, it could be classified into three clusters: deficient, fair, and good, with 6, 10, and 6 regencies/municipality, respectively. Kupang, Alor, Ende, Ngada, Malaka, and Kupang municipality are the deficient clusters. Sumba Barat, Sumba Timur, Timor Tengah Selatan, Timor Tengah Utara, Belu, Lembata, Flores Timur, Sumba Tengah, Nagekeo, and Manggarai Timur are all part of the fair cluster. Sikka, Manggarai, Rote Ndao, Manggarai Barat, Sumba Barat Daya, and Sabu Raijua regencies are among those in the good cluster. Based on the k-means clustering analysis, the government could implement a segmented policy to optimize The School Participation Rate for Early Childhood in order to increase the Human Development Index. This optimization could begin by increasing the allocation of education funds, particularly for the establishment of early childhood education programs. Extension programs are required to increase parents' understanding of why their children should attend preschool. Furthermore, the government must provide training in the development of teaching skills and knowledge for educators, both teachers, and practitioners in early childhood education.]]>
<![CDATA[Pengaruh Akses Terhadap Internet, Listrik dan PDRB Per Kapita Terhadap Tingkat Kemiskinan di Nusa Tenggara Timur Tahun 2015-2019 ]]>
<![CDATA[Nurani Vita Christiani]]>;
<![CDATA[Anna Ellenora Nainupu]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 1 (2021): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[Reducing the level of poverty is one of the goals of sustainable development at both national and regional scales. The percentage of poor people in NTT since 2015 has continued to decline until 2019, but this decline has not yet met the target in the NTT Province Mid-Term Development Plan. Many studies related to poverty have been conducted with various causes. This research is focused on looking at the effect of access to the internet, electricity in households, and GRDP per capita on poverty. With the fixed effect as the best model, it is found that simultaneously, access to the internet and electricity in households has a negative and significant effect on the poverty level, while the GRDP per capita had a negative but insignificant effect on the poverty level.]]>
<![CDATA[ANALISIS INPUT-OUTPUT PADA STRATEGI PEMULIHAN PEREKONOMIAN, PENYERAPAN TENAGA KERJA DAN PENINGKATAN PENDAPATAN DALAM MENGATASI DAMPAK COVID-19 DI PROVINSI NTT ]]>
<![CDATA[Kristanto Setyo Utomo]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 2 (2021): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[The Covid-19 pandemic in Indonesia since the beginning of 2020 has had a negative impact, especially on the economic sector. Various policies to limit mobilization and community activities in preventing the spread of Covid-19 have caused the economy of East Nusa Tenggara Province to enter a recession zone in 2020. The government has tried to encourage the process of National Economic Recovery (PEN) through various policies. This study implements input-output analysis in supporting the policy to accelerate PEN through determining the priority of sectors that have the most significant impact on increasing economic growth, employment, and increasing incomes. This study indicates that encouraging an increase in final demand in the electricity, gas, and water sector will have the most significant multiplier impact on economic growth in the Province of NTT. To increase employment opportunities, the government needs to focus on increasing the output of the manufacturing sector, which will have the highest employment absorption compared to other sectors. Meanwhile, to increase income, spending on the final demand for the education sector by both the government and households will significantly impact increasing people's income in the NTT Province.]]>
<![CDATA[ANALISIS PERAN SEKTOR PARIWISATA DALAM PEMULIHAN EKONOMI PASCA PANDEMI COVID-19 DI NTT ]]>
<![CDATA[Nurani Vita Christiani]]>;
<![CDATA[Artha Lucya Siahaan]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 2 No 01 (2022): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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<![CDATA[The impact of the Covid-19 pandemic that has hit Indonesia since the beginning of 2020 is felt by all regions and all lines of society. Economic growth in NTT has contracted from the second quarter of 2020 to the first quarter of 2021. Therefore, economic recovery is something that must be a common concern so that the wheels of the economy can return to normal and have a positive effect on all community activities. In this study, we will look at the influence of the tourism sector on economic recovery in NTT. As is known, tourism is the primary sector that is of concern to the NTT’s Regional Government to be able to drive the economy of NTT. The tourism sector in this study was approached with several related industries, namely Land Transport, Sea Transport, ASDP, Air Transport, Accommodation Activities, and Food Service Activities. This study uses Input-Output analysis with the 2016 NTT Input-Output table as the data source. In this Input-Output analysis, structural analysis, linkage analysis (forward linkage and backward linkage), and the resulting impact analysis are also included. The result of this study indicates that the air transport sector is the leading sector based on the index of spreadability and the degree of sensitivity, which is greater than one respectively. The impact of the Output generated due to the effect of final demand is the largest value from the air transport sector.]]>
