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[KLASTERISASI USAHA PERTANIAN PERORANGAN TANAMAN PANGAN DI PROVINSI NUSA TENGGARA TIMUR: PERBANDINGAN ALGORITMA K-MEANS DAN K-MEDOIDS ]]>
<![CDATA[Apriliani Gustiana]]>;
<![CDATA[Firrar Ayu Hastungkara Sudrajat]]>
<![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.v5i2.125
<![CDATA[Agricultural Development is one of main goals outlined in the Dasa Cita of Nusa Tenggara Timur’s (NTT) Governor and Vice Governor. In line with that, advancing the agriculture, plantation, livestock, fisheries and maritime sectors as leading sectors that sustainable and based on regional potential is the first foundation stated in Program 7 Pilar NTT Government. This paper examines the clustering of Individual Agricultural Holdings (UTP) using Sensus Pertanian 2023 data on five predominant variety of food crops (dry land paddy, wet land paddy, maize, cassava, sweet potato) in order to reveal heterogeneity in food crop orientation by regencies/municipality in NTT and to inform targeted, evidence-based agricultural support. The methodology used for clustering are K-Means and K-Medoids which is then evaluated with Davies–Bouldin Index (DBI) and Silhouette Coefficient. The results showed that the optimal number of clusters in this study were four clusters. K-Medoids performs best (DBI = 0.86; silhouette = 0.40), slightly outperforming K-Means (DBI = 0.88; silhouette = 0.39). The resulting clusters can be differentiated into UTP Dryland Paddy, UTP Wetland Paddy, UTP Secondary Food Crops (maize, cassava, sweet potato), and Non-Concentration, offering actionable guidance for policy making.]]>
<![CDATA[PENDEKATAN SMALL AREA ESTIMATION UNTUK PEMETAAN PEKERJA DISABILITAS DI NUSA TENGGARA SEBAGAI DUKUNGAN STATISTIK BAGI DASA CITA NTT ]]>
<![CDATA[Ni Putu Esti Utami Barsua]]>;
<![CDATA[Pembayun Otsu Indiana]]>;
<![CDATA[Mahira Fachrunnisa Lubis]]>;
<![CDATA[Kevin Rizkika Setiawan]]>;
<![CDATA[Dolly Fernando]]>;
<![CDATA[Nofita Istiana]]>
<![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.v5i2.126
<![CDATA[This paper examines the estimation of the number of workers with disabilities in the Nusa Tenggara region using Sakernas 2024 data. The limited sample sizes in several districts lead to high sampling errors, necessitating a more reliable small-area statistical approach (Small Area Estimation). The unavailability of accurate small-area labor statistics for persons with disabilities hampers evidence-based regional development planning and inclusive policymaking. This study applies the Small Area Estimation (SAE) method using a Hierarchical Bayesian (HB) Poisson–Gamma model to handle count data with overdispersion—an approach that remains rarely applied in Indonesian labor statistics. The model is developed by integrating Sakernas data with auxiliary information from PODES and the Ministry of Education. Estimation is conducted through Bayesian inference using Markov Chain Monte Carlo (MCMC) simulation. The HB Poisson–Gamma model effectively reduces the Relative Standard Error (RSE) from an average of 44.6% in direct estimation to below 10% across 32 districts in Nusa Tenggara. These results demonstrate the model’s ability to improve data reliability and support inclusive employment policies aligned with regional development priorities.]]>
<![CDATA[ANALISIS KLASTER MULTIVARIAT KINERJA PASAR PARIWISATA KABUPATEN/KOTA DI NUSA TENGGARA TIMUR: PENDEKATAN INTEGRATIF UNIFORM MANIFOLD APPROXIMATION AND PROJECTION (UMAP) DAN K-MEANS CLUSTERING ]]>
<![CDATA[Retno Fitriandari]]>;
<![CDATA[Fadel Muhammad]]>
<![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.v5i2.128
<![CDATA[Tourism plays a vital role in Indonesia’s regional development, yet spatial disparities in tourism performance remain evident across East Nusa Tenggara (NTT). This study examines multidimensional tourism performance by integrating indicators of market demand, supply effectiveness, economic impact, and accessibility. The research addresses the problem of unequal regional tourism performance and asks: How can statistical clustering identify performance disparities among NTT’s districts? The novelty of this study lies in applying unsupervised learning (K-Means clustering) at the district/city level, combining UMAP for dimensionality reduction and dual validation using the Silhouette Score and Adjusted Rand Index (ARI). The study employs standardized secondary data (2021–2024) from Statistics Indonesia, analyzed using R 4.5.1. Results show that the optimal number of clusters is three, with a Silhouette Score of 0.472 (moderate structure) and ARI of 0.813 (excellent recovery). Cluster 1 represents high-performing regions with superior accessibility and demand, Cluster 2 reflects transitional areas with strong capacity but weak utilization, and Cluster 3 includes underperforming regions. Centroid analysis reveals external access and market demand as key differentiators, providing an empirical basis for targeted tourism policy in NTT.]]>
<![CDATA[ANALISIS KEMISKINAN RUMAH TANGGA SEKTOR INFORMAL DI PROVINSI NUSA TENGGARA TIMUR TAHUN 2024 ]]>
<![CDATA[Maria Kewa]]>;
<![CDATA[Yulianus Ronaldias]]>
<![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.v5i2.131
<![CDATA[Households whose heads work in the informal sector tend to be more vulnerable to poverty. The informal sector plays a significant role in providing employment opportunities; however, jobs in this sector are generally unstable, low-paying, and lack social protection. These conditions make households engaged in the informal sector more likely to be trapped in the cycle of poverty. This study aims to analyze the factors influencing the poverty status of households whose heads work in the informal sector in East Nusa Tenggara (NTT). The results of the binary logistic regression model estimation indicate that the variables significantly affecting the poverty status of informal-sector households in NTT include the classification of residential area, number of household members, age of the household head, highest educational attainment of the household head, main employment sector, number of working hours per week, internet access, ownership of a savings account, and ownership of household assets. Based on these findings, the government needs to accelerate the expansion of social protection programs such as employment insurance, health insurance, and social assistance. Access to financing should also be made easier. In addition, comprehensive policies in the agricultural sector must be implemented, including maintaining price stability, ensuring market access, and empowering farmers.]]>
<![CDATA[ANALISIS DINAMIKA NILAI TUKAR PETANI DI PROVINSI NUSA TENGGARA TIMUR: IMPLIKASI TERHADAP KESEJAHTERAAN PETANI DAN KEBIJAKAN PERTANIAN ]]>
<![CDATA[Maria Gratia Fernandez]]>
<![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.v5i2.133
<![CDATA[This study aims to analyze the dynamics of Farmer Terms of Trade (FTT) in East Nusa Tenggara Province and its implications for farmer welfare and agricultural policy formulation. East Nusa Tenggara Province, despite being an agricultural-based region contributing 28.83% to regional GDP, faces persistent challenges in farmer welfare as indicated by fluctuating FTT values. The monthly FTT data from January to August 2025 shows volatility with values ranging from 99.08 to 101.93, frequently falling below the break-even point of 100, indicating that farmers experience deficit conditions where production costs exceed income. This research provides a comprehensive analysis of subsectoral FTT dynamics in East Nusa Tenggara, identifying specific variations across five agricultural subsectors (food crops, horticulture, plantation crops, livestock, and fisheries) and proposing targeted policy interventions based on empirical evidence from recent data spanning JanuaryAugust 2025. This study employs descriptive statistical analysis using secondary data from the Central Bureau of Statistics of East Nusa Tenggara Province. The analysis includes monthly FTT calculations, subsectoral decomposition, price index received (IT) and price index paid (IB) by farmers, and comparative analysis across different agricultural subsectors during the period of January-August 2025. The research reveals that: (1) FTT in East Nusa Tenggara experienced significant fluctuations with an overall declining trend from January (101.60) to July 2025 (99.08) before recovering in August (101.93); (2) Livestock subsector demonstrated the highest FTT at 107.89, while fisheries recorded the lowest at 93.44; (3) The volatility in FTT is primarily driven by faster increases in the price index paid by farmers compared to the price index received, particularly affecting production costs; (4) Four subsectors (food crops, horticulture, plantation crops, and fisheries) consistently recorded FTT values below 100, indicating welfare challenges; (5) Rural deflation of -0.35% in June 2025 significantly impacted farmer purchasing power.]]>
<![CDATA[CLUSTERING POTENSI PADI DI PROVINSI NUSA TENGGARA TIMUR MENGGUNAKAN STANDARD K-MEANS DAN TRAJECTORY K-MEANS ]]>
<![CDATA[Jofri Ardo Tiganna Sembiring]]>
<![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.v5i2.134
<![CDATA[The Agricultural sector is the mainstay of the economy of East Nusa Tenggara, contributing 28,87 percent to the regional GDP in 2024. Of this amount, the food crop sub-sector contributed 22,94 percent, demonstrating the strategic role of food commodities in the regional economic structure. Rice, as the main staple food of the community, has high economic and social value because it directly affects food security and the welfare of the population. This study aims to cluster districts/cities in NTT based on rice potential using Standard K-Means and Trajectory K-Means methods, utilizing indicators such as harvested area and production. The analysis results show that the optimal number of clusters is four. The evaluation shows that Standard K-Means produces better grouping quality, while Trajectory K-Means remains relevant for identifying patterns of change over time. These findings confirm that region grouping based on rice potential can be the basis for more effective, efficient, and sustainable policy-making in strengthening the food sector in NTT.]]>
<![CDATA[IMPLEMENTASI SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DALAM PERAMALAN PRODUKSI DAN KONSUMSI BERAS DI PROVINSI NUSA TENGGARA TIMUR TAHUN 2025-2027 ]]>
<![CDATA[Agatha Herdiani Bria]]>
<![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.v5i2.136
<![CDATA[Food self-sufficiency is a priority in the current government's program, including for the provincial government of East Nusa Tenggara (NTT). According to BPS data, during 2018-2024, domestic rice production was unable to meet the consumption needs of the people of NTT, despite various efforts made by the government. This research aims to forecast rice and paddy production, as well as its sufficiency in meeting the consumption needs of the NTT community for the period 2025-2027. The data used is monthly data on rice production, rice production, and rice consumption for the period 2018-2024. The forecasting methods used are seasonal ARIMA (5,1,4)(1,1,0)12 for rice production and (3,1,3)(1,1,1)12 for rice consumption, while rice production is calculated using the results of the 2018 Grain-to-Rice Conversion Survey (SKGB) and the 2018-2020 NBM grain/rice loss/spillage conversion against rice production. The forecasting results show that rice production and consumption needs are not yet met, while in 2027, rice will experience an aggregate surplus.]]>
<![CDATA[DETERMINAN STATUS MISKIN RUMAH TANGGA DI KABUPATEN LEMBATA TAHUN 2024 ]]>
<![CDATA[Yulianus Ronaldias]]>
<![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.v5i2.137
<![CDATA[Lembata Regency is one of the regions with a high poverty rate in East Nusa Tenggara Province. As of March 2024, the poverty rate in Lembata Regency reached 24.22 percent, which remains far above both the provincial and national averages. Research on poverty in Lembata Regency is still very limited, even though evidence-based policy is crucial to improving policy effectiveness. This study aims to analyze the characteristics and determinants of household poverty status in Lembata Regency. The analysis uses raw data from the March 2024 National Socio-Economic Survey (Susenas) conducted by Statistics Indonesia (BPS). The analytical method applied is binary logistic regression. The results show that residential area, age of household head, number of household members, main employment sector of the household head, and household asset ownership are statistically significant in influencing household poverty status in Lembata Regency. Based on these findings, the government should improve the quality of rural infrastructure to enhance public access to basic services. In addition, comprehensive policy interventions in the agricultural sector are needed, including agricultural land expansion, price stabilization, ensuring market access, and farmer empowerment.]]>
<![CDATA[PERAMALAN PRODUKSI JAGUNG PROVINSI NUSA TENGGARA TIMUR DENGAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) ]]>
<![CDATA[Leonar Do Da'Vinci T.]]>;
<![CDATA[Cindy Artha Yunita Hutabarat]]>
<![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.v5i2.138
<![CDATA[Nusa Tenggara Timur Province is one of the centers of maize production in Indonesia, so this commodity requires special attention. The Nusa Tenggara Timur Provincial Government has designated the agricultural sector as part of the pillars of the sustainable economy with the hope that maize production will be sustainable and can be optimized in the future through agricultural modernization. Supporting this sustainability certainly requires good planning, one of which is forecasting potential maize production as a basis for policy interventions to optimize maize production in the future. Therefore, this study focuses on generating maize production forecasting data. This study uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to develop an appropriate forecasting model. This is based on maize production patterns in Nusa Tenggara Timur Province, which exhibit seasonal patterns. Based on the results of identifying the best SARIMA model, the selected forecasting model is SARIMA (0,0,0)(1,1,0)12. After forecasting for the next twelve months, the highest maize production will be achieved in April 2026 with an estimated maize production of 201,772.94 tons, while the lowest corn production will occur in November 2025 with maize production of 6,675.90 tons. Based on these findings, the government is expected to optimize the planting period because the maize production pattern in East Nusa Tenggara Province, based on the forecast results, still depends on certain periods, especially the rainy season. Optimizations that can be carried out include: expanding the planting area, using superior seeds, pesticides, fertilizers, accompanied by expanding access to the users and increasing the planting index. The planting index can be increased with proper water management, especially in the dry season, for example by implementing drip irrigation so that maize planting does not only depend on the season.]]>
