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[PENENTUAN SISTER CITY UNTUK PEMBENTUKAN DIAGRAM TIMBANG DI NUSA TENGGARA TIMUR DENGAN ALGORITMA K-MEANS ]]>
<![CDATA[Putu Dita Pickupana]]>;
<![CDATA[Putu Hadi Purnama Jati]]>;
<![CDATA[Muhamad Sukin]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 2 (2021): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[The Consumer Price Index (IHK) is a value that calculates changes in the weighted average price of goods and services consumed by households and serves as the basis for the BPS-Statistics to calculate inflation. Weighting data from the BPS-Statistics cost of living survey (SBH) is one of the components required to explain and demonstrate the dynamics of the IHK. SBH is held in several cities due to the limited resources to conduct this survey. As a solution, the sister city approach is adopted by BPS-Statistics to estimate the consumer price index for cities that are not part of the cost-of-living survey domain. The sister city approach uses weighting data from a city that held SBH with similar consumption patterns and is located geographically close to each other. Although the appointment of a sister city went through several procedures, there was no existing method to measure how similar a city is to another city based on the sister city definition. In this paper, we will use machine learning to analyze the similarity of cities in Nusa Tenggara Timur based on their consumption patterns, and as a result, the decision to appoint a sister city will be more accurate. Machine learning is a field of artificial intelligence (AI) and computer science that uses data and algorithms to mimic how people learn and progressively increase its accuracy. Machine learning methods will support the sister city approach with scientific reasoning to produce more accurate inflation. The result of the clustering based on the elbow method for K-means shows that Kupang city has a unique characteristic which means there is no similarity with the other cities in Nusa Tenggara Timur. However, other cities grouped into two cluster where the two inflation cities (Maumere and Waingapu) are not in the same cluster.]]>
<![CDATA[KETERKAITAN MOBILITAS MASYARAKAT DENGAN KASUS COVID-19 DI PROVINSI NUSA TENGGARA TIMUR MENGGUNAKAN DATA GOOGLE MOBILITY REPORT ]]>
<![CDATA[FX Gugus Febri Putranto]]>;
<![CDATA[Christiayu Natalia]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 1 No 2 (2021): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Efforts to control the spread of COVID-19 through restrictions on community mobility have an impact on various macro indicators of development target at the national and regional levels especially in Nusa Tenggara Timur Province. Based on data from Statistics Indonesia, the poverty rate, unemployment rate, Gini Ratio, economic growth and Human Development Index (HDI) were also affected by the COVID-19 pandemic. This study aims to explain the general description of macro indicators of development target in Nusa Tenggara Timur Province, as well as examine the relationship between community mobility and new cases of covid-19 during the implementation of the PPKM policy starting in July 2021. Community mobility data is sourced from big data Covid -19 Community Mobility Reports, while data on the number of COVID-19 cases sourced from the website covid19.go.id. To examine the relationship between community mobility and COVID-19 cases, the Pearson correlation is used. The results showed that the six categories of community mobility destinations, except parks, all had a significant correlation with the addition of new cases in Nusa Tenggara Timur Province. The higher mobility of the community, the lower the number of new cases. On the other hand, in residential areas, the higher the mobility, the higher the number of new cases.]]>
<![CDATA[PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK PENENTUAN KLASIFIKASI KEMISKINAN MULTIDIMENSI DI PROVINSI NUSA TENGGARA TIMUR ]]>
<![CDATA[Kristanto Setyo Utomo]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 2 No 01 (2022): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[The Covid-19 pandemic has proven to directly impact the percentage of poverty in the Province of East Nusa Tenggara. However, the determination of the size of poverty so far has been carried out using an economic dimension approach, namely the poverty line. This study classifies multidimensional poverty, namely the dimensions of health, education, economy, and life worthiness. In this multidimensional poverty classification, this research utilizes machine learning algorithms. The test results show that the Decision Tree algorithm is the best algorithm for classifying multidimensional poverty in East Nusa Tenggara Province with an accuracy rate of 82.69 percent, precision of 84.08 percent, and recall 97.56 percent. This algorithm shows that the birth attendant indicators on the health dimension and primary education on the education dimension have a high gain value. These two indicators become the primary decision node in the Decision Tree to determine multidimensional poverty that needs serious attention by the East Nusa Tenggara Provincial government.]]>
<![CDATA[MODEL DEKOMPOSISI ANALISIS JALUR: PENGARUH KAUSALITAS ANTAR VARIABEL PEMBENTUK KEMISKINAN DI PROVINSI NUSA TENGGARA TIMUR ]]>
<![CDATA[Nur Imam Saifuloh]]>;
<![CDATA[Sabir]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 2 No 01 (2022): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Poverty in developing countries mostly occupies in rural areas which the agricultural sector as main activity. In spite of this, agricultural land is decreasing as an effect of industrial and services activities. This study focuses on poverty in East Nusa Tenggara Province, Indonesia. By using decomposition model of path analysis, conversion of agricultural land and village funds have no significant effect on poverty even though the path is in accordance with the theory. Meanwhile, economic growth and dependency ratio have a significant effect on poverty which is mediated by the informal workers’ wage.]]>
<![CDATA[Determinan Ketimpangan dan Kemiskinan dalam Kerangka Pembangunan Ekonomi Inklusif (Studi Pada Kabupaten/Kota di Nusa Tenggara Timur ) ]]>
<![CDATA[Dyonisius H S Jewaru]]>;
<![CDATA[Ervina Jayanti Siagian]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 2 No 01 (2022): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Economic development is currently moving from a pro-poor growth paradigm to inclusive economic development. Its main sub-pillars include reducing inequality and reducing poverty. These indicators are quite complex and multidimensional. NTT Province is in the top 5 provinces with the lowest inequality and poverty reduction index in Indonesia. The main problem is the slow growth of inequality and poverty reduction. In addition, there is a large gap in inequality and poverty between regencies/cities compared to NTT and national figures. This study aims to analyze the determinants of reducing inequality and poverty within the framework of inclusive economic development in all regencies/cities and underdeveloped regencies in NTT in 2015-2021. Regression analysis of panel data using a fixed effect model found that the significant factors that determine inequality reduction are economic growth, economic infrastructure, and expansion of access and opportunities. However, economic growth does not significantly affect the reduction of inequality in underdeveloped districts in NTT. Meanwhile, significant factors that determine the level of poverty reduction are job opportunities, economic infrastructure, and expansion of access and opportunities]]>
<![CDATA[Analisis Determinan Pertumbuhan Ekonomi di Nusa Tenggara Timur Tahun 2010-2021 ]]>
<![CDATA[Nurani Vita Christiani]]>;
<![CDATA[Nucke Widowati Kusumo Projo]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 2 No 02 (2022): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Pertumbuhan ekonomi merupakan indikator penting dalam menilai kinerja suatu perekonomian, terutama untuk menganalisis hasil upaya pelaksanaan pembangunan ekonomi yang telah dilakukan oleh suatu negara atau suatu daerah. Penelitian ini bertujuan untuk menganalisis pengaruh beberapa variabel yaitu inflasi, persentase penduduk miskin dan tingkat pengangguran terbuka terhadap pertumbuhan ekonomi di NTT. Penelitian ini menggunakan data tingkat inflasi, kemiskinan dan pengangguran terbuka yang dihasilkan oleh Badan Pusat Statistik. Metode yang digunakan dalam penelitian ini adalah regresi linier berganda. Hasil penelitian menunjukkan bahwa inflasi berpengaruh positif dan tidak signifikan terhadap pertumbuhan ekonomi, tingkat pengangguran terbuka berpengaruh negatif dan signifikan terhadap pertumbuhan ekonomi, sedangkan kemiskinan berpengaruh positif dan tidak signifikan terhadap pertumbuhan ekonomi.]]>
<![CDATA[Desentralisasi Fiskal Dan Kemiskinan Di Provinsi Nusa Tenggara Timur ]]>
<![CDATA[Fadel Muhammad]]>;
<![CDATA[Yuliana Kurnawati Dima]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 2 No 02 (2022): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Tujuan utama otonomi daerah adalah untuk meningkatkan pelayanan publik dan mendekatkannya dengan pemenuhan kebutuhan masyarakat. Hal ini juga tercermin dalam desentralisasi fiskal yang memberikan kewenangan kepada pemerintah daerah untuk mengelola keuangannya guna meningkatkan kesejahteraan rakyat. Penelitian ini ingin mengetahui apakah desentralisasi fiskal berdasarkan indikator kemandirian fiskal daerah dan aspek akses air bersih, sanitasi, kepadatan penduduk dan tingkat pengangguran dapat mempengaruhi kesejahteraan di provinsi Nusa Tenggara Timur. Data panel tahun 2017-2021 digunakan dalam penelitian ini untuk mendapatkan model estimasi parameter terbaik. Hasil yang diperoleh adalah desentralisasi fiskal yang diukur dengan kemandirian PAD terhadap pendapatan dan belanja ternyata berpengaruh positif terhadap peningkatan kemiskinan, sedangkan aspek lain seperti air bersih, sanitasi, kepadatan penduduk dan tingkat pengangguran secara parsial berpengaruh negatif terhadap peningkatan kesejahteraan rakyat. Dengan pengelolaan anggaran yang optimal yang ditujukan untuk memenuhi kebutuhan masyarakat, peningkatan infrastruktur air bersih dan sanitasi, KB, transmigrasi dan tata kota, serta penciptaan lapangan kerja dapat meningkatkan kesejahteraan masyarakat.]]>
<![CDATA[Identifikasi Karakteristik Desa/Kelurahan Di Provinsi Nusa Tenggara Timur Menggunakan Analisis Gerombol ]]>
<![CDATA[Andrew Donda Munthe]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 3 No 01 (2023): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[Grouping of villages by clustering analysis can result in the identification to the appropriate policy programs to each cluster formed. The clustering algorithm that can be used on mixed variables of village grouping is the Gower Method (Gower’s distance). The purpose of this study is to apply the Gower Method to village clustering in Nusa Tenggara Timur Province based on 13 mixed variables. The data used in this study was sourced from Village Potential Data Collection (PODES) in 2021. The results showed that the optimal cluster that formed from the application of the Gower Method was 3 clusters. The first cluster consisted of 1.063 villages, the second cluster consisted of 870 villages and the third cluster consisted of 1.517 villages. Based on the visualization of the results of the cluster, the area with the highest tendency to development achievement is the village in the third cluster, while the lowest is in the villages of the first cluster members.]]>
<![CDATA[Karakteristik Wanita Dengan Berat Bayi Lahir Rendah Di Nusa Tenggara Timur ]]>
<![CDATA[Nofriana Florida Djami Raga]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 3 No 01 (2023): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[ In the past half-century, Indonesia has undergone a considerable reduction in infant mortality rates. Upon closer examination of the infants' death distribution, a significant decrease occurs in the postnatal period and only subtly reduces in the neonatal phase. The leading cause of death in neonates is low birthweight (LBW), i.e., the infants' birthweight is below 2,500 grams. NTT is among Indonesia's leading provinces with the highest percentage of LBW. This research investigates the determinants of LBW incidence in NTT by employing the March 2022 Susenas data. A total of 1.793 sample sizes are analyzed through two stages of data analysis: (1) descriptively through simple cross-tabulation, chi-square, and t-test; and (2) multiple binary logistic regression. The result shows that place of residence, island, education, economic status, and mothers' age are statistically significant predictors of LBW in NTT. Overall, women residing in rural areas have higher odds of having LBW infants than those in urban areas. Compared to Sumba and Flores islands, the highest number of LBW cases are found in the Tirosa islands. The higher a woman's education level, the higher the LBW incidence. Meanwhile, the higher the economic status of the mothers, the lower the probability of having light infants at birth. Regarding age, the relationship between women's age and the incidence of LBW illustrates a U-shaped pattern in which the highest probability of LBW is found among women below 20 and above 40 years old. ]]>
<![CDATA[Determinan Status Kerawanan Pangan Rumah Tangga Di Provinsi Nusa Tenggara Timur: Analisis Regresi Logistik ]]>
<![CDATA[Anna Ellenora Nainupu]]>;
<![CDATA[Kadir]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 3 No 01 (2023): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
<![CDATA[One dimension of poverty is hunger or food insecurity. Food insecurity is closely related to the Sustainable Development Goals (SDGs), especially Goal 2, which aims to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture. The latest data shows that the prevalence of people with moderate/severe food insecurity in East Nusa Tenggara Province is the highest in Indonesia. This study aims to examine the socioeconomic variables that influence household food insecurity status. The logistic regression model estimation results show that urban/rural status, number of household members, age, gender, education level, employment status, head of household business field, land ownership, and food expenditure share significantly affect household food insecurity. From those variables, there are three important variables that must be considered for the case of NTT, namely the number of household members, the education level of the head of household, and the share of household food expenditures. Interventions such as expanding family planning promotions, implementing non-formal education (Package A, B, C Learning Groups) for non-school-age household heads, and ensuring targeted community empowerment programs are still relevant to accelerate the decline in household food insecurity.]]>
