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[Industrialisasi dan Pengentasan Kemiskinan di Nusa Tenggara Timur: Pendekatan Analisis Data Panel ]]>
<![CDATA[Ni Putu Lidya Pramesty]]>;
<![CDATA[Firman Emmanuel Declarantius Parulian]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 1 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i1.107
<![CDATA[NTT is still facing a high level of poverty with slow progress in its reduction. One of the root causes of poverty in NTT is the low income of the population due to low labor productivity. In this regard, industrialization is considered a potential solution to poverty alleviation. However, previous studies on the impact of industrialization on poverty have shown varied results. Therefore, this study aims to provide an overview of industrialization in NTT and analyze its impact on poverty. This research uses panel data from 22 districts/cities between 2017 and 2023, sourced from Badan Pusat Statistik. The data is analyzed using descriptive methods and panel data regression. The results show that NTT has undergone an industrialization process, but at a low development rate. In fact, an increase in the contribution of the manufacturing sector to the economy has proven to be effective in reducing poverty in NTT's districts by enhancing labor productivity. However, merely increasing labor absorption in the manufacturing sector has not been sufficient to reduce poverty, particularly for low-skilled workers. This is because the manufacturing sector in NTT is still dominated by micro and small industries. Therefore, it is recommended that the government of NTT prioritize policies that focus on improving the quality of industries, especially micro and small industries.]]>
<![CDATA[Pendekatan Spasial Panel Data pada Tingkat Pengangguran Terbuka di Provinsi Nusa Tenggara Timur Tahun 2020–2023 ]]>
<![CDATA[Haryati Mustafa]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 1 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i1.108
<![CDATA[Based on theory, low unemployment rate is expected to reduce poverty and stimulate economic growth in a region. However, this theory does not align with conditions in NTT. According to BPS (2023), NTT is the province with the seventh lowest unemployment rate but the third highest poverty rate, accompanied by low economic growth in Indonesia. This phenomenon cannot be separated from the influence of various economic and spatial factors. Each region has unique characteristics that may affect the unemployment condition of neighboring region, a concept known as spatial dependency. Therefore, this study aims to analyze the factors influencing the unemployment rate, taking into account spatial effects. The results of the SEM estimation with random effects indicate that the spatial effect is significant and positively impacts the unemployment rate at a 5 percent significance level. Other factors affecting the unemployment rate include poverty and GRDP. Meanwhile, the migration variable does not have a significant effect on unemployment rate in NTT.]]>
<![CDATA[Analisis Pengaruh Variabel Fasilitas Perumahan, Kesehatan, dan Standar Hidup Layak, Terhadap Pendidikan dan Kemiskinan di Provinsi NTT ]]>
<![CDATA[Vievien Abigail Damu Djara]]>;
<![CDATA[Adrianus Kabubu Hudang]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 1 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i1.109
<![CDATA[East Nusa Tenggara (NTT) Province in 2024 is included in the top 5 (five) with the highest percentage of poor people in Indonesia, so it needs to be a concern for researchers in studying poverty in NTT. Several variables used in this study refer to the dimensions of the Human Development Index calculation and several previous studies. The unit analysis in this study is all regency/municipality in NTT. The Structural Equation Modeling-Partial Least Square (SEM-PLS) was considered. The results at a significance level of five percent show that social variables (decent housing, improved drinking water, and improved sanitation) had a significant effect in reducing poverty in NTT. This social variable had the greatest effect in reducing poverty in NTT compared to other variables used in this study. Social variables also had a significant effect in increasing health variables, and then this health variable had a significant effect in reducing poverty in NTT. The result of this study also found that the decent standard of living variable had a significant effect in increasing education and health in NTT. The results of this study have a Goodness of Fit (GoF) index of 0.709, which means that the model is fit for use in the analysis.]]>
<![CDATA[Segmentasi Kabupaten/Kota Berdasarkan Indikator Rumah Layak Huni di Provinsi Nusa Tenggara Timur Tahun 2024 ]]>
<![CDATA[Marlia Augustio Adang]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 1 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i1.110
<![CDATA[Building from villages and from the ground up to achieve economic equality and eradicate poverty is one of the Asta Cita Missions of the Indonesian Government for the 2025 to 2029 period. To support the achievement of this mission, improving access to decent, affordable, and quality housing has become one of the Government’s key targets. Nusa Tenggara Timur is among the six provinces in Indonesia with the lowest percentage of households with access to decent housing. Therefore, to improve the province’s performance, a study needs to be conducted to accurately identify the target groups and support effective decision-making. This study aims to segment regencies/cities into several groups based on access to decent housing, serving as a reference for development priorities. The methodology used in this study is hierarchical cluster analysis, comparing the single linkage, average linkage, complete linkage, and Ward’s method. Among these, Ward's method yields the highest agglomerative coefficient and results in segmentation of regions into five distinct groups. The study concludes that policy-making based on regional prioritization through clustering analysis can improve the efficiency and effectiveness of programs aimed at ensuring decent, affordable, and quality housing for the community.]]>
<![CDATA[Analisis Ketimpangan Pembangunan Antarkabupaten/Kota di Provinsi Nusa Tenggara Timur Tahun 2021-2024 ]]>
<![CDATA[Chandra Rinaldy Mbura]]>;
<![CDATA[Christian Putra Ginting]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 1 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i1.113
<![CDATA[One of the key missions outlined in the Asta Cita of Indonesia's President and Vice President is achieving equitable development. However, Nusa Tenggara Timur (NTT) remains a province facing significant challenges in this regard. This study analyzes development inequality across regencies and municipalities in NTT from 2021 to 2024, using the Bonet index as a measure of regional disparity. Descriptive analysis, based on variance and mean values of the Bonet index, shows a tendency toward increasing inequality during the period. Additionally, boxplot analysis reveals that Kupang Municipality consistently stands as an outlier, indicating its development level is significantly higher than that of other districts in NTT. Panel data regression analysis shows that variables such as the Human Development Index (HDI), Open Unemployment Rate (TPT), and the growth rate of the primary sector have a positive and significant impact on regional disparity, while capital expenditure does not significantly affect regional disparity. Based on these findings, it is recommended that the NTT provincial government prioritize equitable human development, targeted unemployment reduction, economic diversification, and optimize capital expenditure for the purpose of balanced regional development.]]>
<![CDATA[Prospek Swasembada Beras di Provinsi Nusa Tenggara Timur: Metode LQ, DLQ, dan Tipologi Klassen ]]>
<![CDATA[Amudi Pandapotan Saragih]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 1 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i1.114
<![CDATA[Food self-sufficiency is one of the Asta Cita programs of Indonesian President Prabowo and Indonesian Vice President Gibran Rakabuming Raka in the field of food security. The Provincial and Regional Governments in East Nusa Tenggara (NTT) are committed to supporting the implementation of the food security improvement strategy program in NTT. This study focuses on grouping rice production bases based on Regency/City in NTT using the Location Quotient (LQ), Dynamic Location Quotient (DLQ), and Klassen Typology Quadrant Analysis methods. Based on the results of the LQ and DLQ calculations, they are then classified into the Klassen typology section to see the prospects of each Regency/City classified into 4 quadrants. Quadrant I includes the regencies of West Sumba, Ende, and Central Sumba, Quadrant II includes the regencies of TTS, TTU, Alor, Sikka, and Sabu Raijua, Quadrant III includes the regencies of East Sumba, Kupang, Ngada, Manggarai, Rote Ndao, West Manggarai, Nagekeo, East Manggarai, and Kupang City, and Quadrant IV includes the regencies of Belu, Lembata, East Flores, SBD, and Malaka.]]>
<![CDATA[ANALISIS MULTILEVEL: DETERMINAN STATUS KEMISKINAN DIGITAL PADA PENDUDUK USIA 5 TAHUN KE ATAS DI PROVINSI NUSA TENGGARA TIMUR ]]>
<![CDATA[Athaya Shabihah]]>;
<![CDATA[Nieken Misga]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 2 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i2.115
<![CDATA[Digital poverty is a technological problem as well as a social problem that is still exist in Indonesia. Digital access must be able to reach everyone because it is part of individual rights. East Nusa Tenggara (NTT) Province is one of the provinces with low International Telecommunication Union (ITU) Index in Indonesia. As an archipelago, NTT is also a 3T category (Frontier, Outermost, Disadvantaged). By using secondary data from the Social and Economic Survey (Susenas) and Village Potential (PODES), this study aims to provide empirical evidence of the factors that influence digital poverty in NTT. By using binary multilevel logistic regression methods, this study uses three levels of individual conditions based on individuals (level 1), household conditions (level 2), and regional conditions (level 3). The conclusion obtained is that the variables of age, education level, household welfare, housing classification, and electricity access significantly influence the status of digital poverty in NTT at the 5% significance level. Kemiskinan digital adalah masalah teknologi sekaligus masalah sosial yang masih ada di Indonesia. Akses digital harus dapat menjangkau semua orang karena merupakan bagian dari hak asasi manusia. Provinsi Nusa Tenggara Timur (NTT) merupakan salah satu provinsi dengan International Telecommunication Union (ITU) Index yang rendah di Indonesia. Sebagai daerah kepulauan, NTT juga termasuk dalam kategori 3T (Terdepan, Terluar, Tertinggal). Dengan menggunakan data sekunder dari Survei Sosial dan Ekonomi (Susenas) dan Potensi Desa (PODES), penelitian ini bertujuan untuk memberikan bukti empiris mengenai faktor-faktor yang mempengaruhi kemiskinan digital di NTT. Dengan menggunakan metode regresi logistik multilevel biner, penelitian ini menggunakan tiga level kondisi individu berdasarkan individu (level 1), kondisi rumah tangga (level 2), dan kondisi daerah (level 3). Kesimpulan yang diperoleh adalah variabel usia, tingkat pendidikan, kesejahteraan rumah tangga, klasifikasi rumah, dan akses listrik berpengaruh signifikan terhadap status kemiskinan digital di NTT pada tingkat signifikansi 5%.]]>
<![CDATA[PERKEMBANGAN INDEKS INKLUSIF KABUPATEN/KOTA DI PROVINSI NUSA TENGGARA TIMUR ]]>
<![CDATA[Agung Putra]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 2 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i2.119
<![CDATA[This study explore how inclusive economic growth has unfolded across 22 district and cities in East Nusa Tenggara, Indonesia. This study developed the Inclusive Index II, combining three key indicators: poverty rate, income inequality (gini ratio), and the Employment to Population (EPR). The index ranges from 0 to 1, with reduced poverty and improved labor participation, inequality remains a persistent issue widening in some regions. By applying the Klassen Typology, this study mapped each are into one of four categories: (I) Economically strong and inclusive, (II) Inclusive but economically lagging, (III) Lagging and non inclusive, and (IV) Economically strong but exclusive. Most districts fall into Quadrants II and III, showing that inclusive growth is far from evenly distributed. These insight highlight the urgent need for locally tailored, evidance based policies that do more than grow economies, they must also include and uplift the people within them.]]>
<![CDATA[PERAMALAN INDEKS PEMBANGUNAN MANUSIA DI PROVINSI NUSA TENGGARA TIMUR DENGAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI BROWN ]]>
<![CDATA[Virgilius Daton Balamakin]]>;
<![CDATA[Maryo Yoseph Ambarto Dwi Sili Osan Osan]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 2 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i2.122
<![CDATA[Introduction/Main Objectives: This study aims to forecast the Human Development Index (HDI) of East Nusa Tenggara Province (NTT) for the next five years using the Double Exponential Smoothing (DES) method, in order to provide an overview of HDI development as a basis for regional development planning. Background Problems: Although NTT's HDI has shown an upward trend over the past five years, its value remains relatively low nationally, ranking third lowest after Papua and West Papua. This lag is attributed to low per capita expenditure, education levels, and life expectancy. The COVID-19 pandemic in 2020 also temporarily reduced the HDI value. Novelty: This study employs the DES method to project NTT's HDI using optimal parameters estimated from historical data, providing accurate data-based predictions that can be directly utilized as a reference for human development policies at the provincial level. Research Methods: This study uses secondary data on NTT's HDI for the period 2010–2024 from the Central Statistics Agency. Time series analysis was performed using the DES method, estimating the parameters α and β, and measuring the accuracy of the model using SSE, MSE, RMSE, and MAPE. Findings/Results: The DES model with α = 1 and β = 0.1655 produced a MAPE of 0.421, indicating high accuracy. The forecasting results predict that the NTT HDI will increase from 68.03 in 2025 to 70.57 in 2029, reflecting continuous improvement in the dimensions of education, health, and living standards.]]>
<![CDATA[PENGELOMPOKAN USAHA PERTANIAN PERORANGAN TANAMAN PANGAN DI KABUPATEN FLORES TIMUR DENGAN ALGORITMA K-MEANS DAN K-MEDOIDS ]]>
<![CDATA[Virgilius Daton Balamakin]]>;
<![CDATA[Maryo Yoseph Ambarto Dwi Sili Osan Osan]]>;
<![CDATA[Caecilia Safira Ferini Marcellina Mitang Mitang]]>
<![CDATA[ Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 2 (2025): Jurnal Statistika Terapan ]]>
Publisher : <![CDATA[Badan Pusat Statistik Provinsi NTT ]]>
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DOI: 10.64930/jstar.v5i2.124
<![CDATA[The introduction explains that agriculture is the main sector supporting the economy of the community in East Flores Regency, East Nusa Tenggara, with the majority of the population depending on individual food crop farming such as rice, corn, cassava, sweet potatoes, and peanuts. However, there are variations in production between subdistricts due to differences in land potential, agroclimatic conditions, and cultivation practices. Background Problems shows that there is an imbalance in commodity distribution based on the 2023 Agricultural Census, in which upland rice and corn are dominant, while paddy rice and sweet potatoes are relatively low. Without clear mapping, development policies risk being inconsistent with regional needs. The novelty of this research lies in the use of a clustering approach comparing the K-Means and K-Medoids algorithms, which are rarely applied in the context of agriculture in remote areas, as well as quality evaluation using the Davies-Bouldin Index (DBI) accompanied by cluster profiling. Research Methods used secondary data from Statistics Indonesia (BPS) from the 2023 Phase II Agricultural Census with six main commodities, analyzed through Z-Score normalization, application of K-Means and K-Medoids, DBI evaluation, and interpretation of results through profiling. The results show that K-Means produces three clusters with a DBI of 1.1696, while K-Medoids produces two clusters with a DBI of 0.7058, which is more optimal. Profiling shows that 15 subdistricts have a balanced pattern in rice and sweet potatoes, while two subdistricts, Ile Boleng and Witihama, are more dominant in corn, cassava, and peanuts. These findings form the strategic basis for agricultural development policies in East Flores.]]>
