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INDONESIA
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Published by Politeknik Statistika STIS
ISSN : 28099842     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
International Conference on Data Science and Official Statistics International Conference on Data Science and Official Statistics (ICDSOS) 2023 is organized by Politeknik Statistika STIS and Statistics Indonesia (BPS). This international conference in collaboration with Forum Pendidikan Tinggi Statistika (FORSTAT), Ikatan Statistisi Indonesia (ISI), United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), and United Nations Statistics Division (UNSD). The ICDSOS will bring together statisticians and data scientists from academia, official statistics, health sector and business, junior and senior professionals, in an inviting hybrid environment on November 24th - 25th, 2023. Dealing with the theme of this conference is Harnessing Innovation in Data Science and Official Statistics to Address Global Challenges towards the Sustainable Development Goals. DATA SCIENCE Machine Learning and Deep Learning Data Science and Artificial Intelligence (AI) Data Mining and Big Data Statistical Software Information System Development for Official Statistics Remote Sensing to Strengthen Official Statistics Other data science relevant topic APPLIED STATISTICS Applied Multivariate Analysis Applied Time Series Analysis Applied Spatial Statistics Applied Bayesian Statistics Microeconomics Modelling and Applications Macroeconomics Modelling and Applications Econometrics Modelling and Applications Quantitative Public Policy and Statistical Analysis Applied Statistics on Demography Applied Statistics on Population Studies Applied Statistics on Biostatistics and Public health Other applied statistics relevant topic OFFICIAL STATISTICS Official Statistics Survey Methodology Developments Data Collection Improvements Sustainable Development Goals (SDGs) Indicators Estimation Small Area Estimation (SAE) Non Response and Imputation Methods Sampling Error and Non Sampling Error Evaluation Benchmarking Regional Official Statistics Other official statistics relevant topic
Arjuna Subject : Umum - Umum
Articles 251 Documents
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Forest and Land Fire Severity Analysis in 2022-2023 in Hulu Sungai Selatan Regency Using the NBR (Normalized Burn Ratio) Method Meirisa Putri, Desti; Refa, Muhammad; Siti Salamah, Sheren; Septi Anggraini, Tania; Himayah, Shafira
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.626

Abstract

Forest and land fires are recurring disasters in Indonesia that cause environmental, health, and socio-economic losses. Hulu Sungai Selatan Regency, South Kalimantan, is among the affected regions, particularly during 2022–2023 when the El Niño phenomenon and flammable peatlands increased fire risk. This study analyzes the spatial extent and severity of fires and their potential impact on local communities by integrating remote sensing and demographic data. The Normalized Burn Ratio (NBR) and Difference Normalized Burn Ratio (dNBR) derived from Landsat 8 and 9 imagery (2021–2023) were used to map fire severity, supported by hotspot data from the Ministry of Environment and Forestry and settlement data from the Geospatial Information Agency. Population data from the Central Bureau of Statistics (BPS) were incorporated to develop a Fire Vulnerability Index (FVI) representing community exposure to fire-prone areas. The results show that burned areas in 2023 expanded compared to 2022, with increasing low to moderate severity classes. Subdistricts with dense populations, such as Kandangan and Angkinang, showed higher fire vulnerability values, indicating potential socio environmental risks. These findings emphasize the importance of integrating remote sensing and statistical data to support effective fire mitigation and risk reduction in vulnerable regions.
Analysis and Prediction of Green GRDP in Indonesia with Ecosystem Service Value Approach Gata, Ibnu; Pasaribu, Ernawati
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.627

Abstract

Gross Regional Domestic Product (GRDP) as a measure of economic output in each region has not reflected sustainability because it overlooks the environmental impacts caused. Green GRDP is an important innovation that integrates environmental aspects into sustainable development. Indonesia has committed through TAP MPR IX/2001, Indonesia Emas 2045, and the SDGs to implement sustainable development. This study analyzes and projects Indonesia’s Green GRDP using the Ecosystem Service Value (ESV) approach. Satellite imagery data from MODIS MCD12Q1 and the Cellular Automata–Artificial Neural Network (CA-ANN) method are employed to predict land cover changes, while time series models are applied to forecast GRDP. Variations in provincial ESV are strongly influenced by land cover composition. In 2001, Papua recorded the highest Green GRDP and ESV contribution, whereas by 2020 (projected to 2030), Jakarta leads in Green GRDP but exhibits the lowest ESV contribution percentage. Throughout the period 2001–2030, Papua consistently maintains the highest ESV proportion relative to its Green GRDP. The findings highlight the importance of incorporating ecosystem service values into regional and national economic planning to ensure that economic growth inherently reflects environmental sustainability. This effort should be supported by spatially differentiated development strategies aligned with each region’s ecological capacity.
The Influence of Child, Households, and Villages/Sub-Districts Characteristics on The Working Status of Children in East Nusa Tenggara Province 2024 Prayoga, Angga; ., Budiasih
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.629

Abstract

The percentage of the poor population in East Nusa Tenggara Province is being thefourth highest in Indonesia in 2024, but the highest percentage of child labor in Indonesia. Thepurpose of this study is to find out the picture, influencing factors, and trends of factors affectingchild labor in East Nusa Tenggara Province in 2024. The unit of analysis was children aged 10-17 years who were unmarried and not as head of household with a sample of 9,117 children from6,123 households and 1,165 villages/sub-districts. The data used are Susenas Kor and ModulesMarch, as well as Podes 2024 sourced from BPS. The analysis method in this study is multilevelbinary logistics regression. The results of the study show that children who work are boys aged15-17 years. The child lives in households with a low level of head of households’ education andhousehold work in the agricultural sector, a small number member of productive age, and havemicro and small enterprises, and live in villages/sub-districts with many micro and smallindustries and the main source of income for most of the population in the agricultural sector.
Intersectoral Linkages and Spillover Effects in South Sumatra’s Economy: Evidence from the 2016 Interregional Input–Output Table and 2024 Input–Output Table Marpaleni; Mardiana; Puspita, Anggi Dwi; Rama, Indhira Putri
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.631

Abstract

This study examines South Sumatra’s economic structure using interregional input– output analysis to identify key sectors and quantify spillover effects. A dual-dataset approach employs the 2016 IRIO table for interprovincial trade dynamics and the 2024 IO table for current sectoral analysis. Results indicate a domestically oriented economy, with 88.45% of supply met by internal production. Manufacturing and construction emerge as central hubs with strong intersectoral linkages, supported by agriculture and mining as upstream suppliers. Interregional trade is concentrated with nearby Sumatran provinces and Java’s industrial centers. Spillover effects benefit Jambi, Bengkulu, and Banten, while feedback effects show dependency on Java. Output multipliers highlight electricity and gas as key growth drivers, whereas agriculture and real estate contribute most to local income. These patterns reveal a structural divergence between growth and inclusivity. To address this, the study recommends a dual-track strategy: scale up manufacturing and energy to drive aggregate output, while modernizing agriculture and highvalue services to support income distribution. Strengthening interprovincial corridors and deepening local supply chains can further enhance resilience and expand the province’s role in national development.
The Gath–Geva Algorithm for Clustering Spatial Inequality of Stunting in East Nusa Tenggara Province Nufus, Mitha Rabiyatul
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.634

Abstract

Stunting remains a critical public health issue in Indonesia, particularly in East Nusa Tenggara (NTT), where prevalence rates are among the highest nationally. This study aims to classify districts and municipalities in East Nusa Tenggara Province based on socioeconomic and health-related indicators associated with stunting vulnerability. Using the Gath–Geva (Fuzzy K-Means Entropy) clustering algorithm, four key variables were analyzed, including poverty rate, access to proper housing, open unemployment rate, and number of health facilities. The results identified three distinct clusters with different regional characteristics. Cluster 1 consists of areas with low poverty and well-developed health infrastructure but relatively high unemployment rates. Cluster 2 represents the most vulnerable regions characterized by high poverty, poor housing access, and limited health facilities, while Cluster 3 comprises more stable areas with better housing, low unemployment, and adequate healthcare services. The silhouette coefficient value of 0.41 indicates that the three-cluster structure provides a reasonably good level of separation and internal consistency. These findings highlight that stunting vulnerability is strongly influenced by socioeconomic disparities and the distribution of health infrastructure. Therefore, intervention strategies should be tailored to the characteristics of each cluster, emphasizing integrated actions in high-risk regions and preventive measures in more stable areas to accelerate stunting reduction across East Nusa Tenggara Province.
Spatio-Temporal Modeling of Agricultural Drought in Indramayu Using the NDDI Index (2015-2024) Septiani, Sypa; Siahan, Irene; Talitha Aqilah, Hilya; Oktaviani, Dela; Trisulistiani, Fifin; Alifia, Salwa; Handayani, Tiara; Zahrotunnisa, Siti
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.635

Abstract

This study examines the spatio-temporal patterns of agricultural drought in Indramayu Regency, Indonesia, using the Normalized Difference Drought Index (NDDI) derived from Landsat imagery between 2015 and 2024. The analysis employed spatial autocorrelation techniques, including Global Moran’s I and Local Indicators of Spatial Association (LISA), to identify spatial clustering and persistence of drought conditions. The results show consistent spatial vulnerability, with the southern region forming stable High-High drought clusters across multiple years, while the northern region remains dominated by LowLow clusters. These findings indicate that drought distribution in Indramayu demonstrates strong spatial persistence and temporal continuity, reflecting long-term environmental and landuse characteristics. A supporting correlation analysis between NDDI and rice productivity (? = 0.164; p-value = 0.651) revealed no significant relationship, suggesting that effective irrigation systems have mitigated the impact of meteorological drought on agricultural output. Overall, the study highlights the need for location-specific drought management in spatially vulnerable southern areas to enhance agricultural resilience and regional food security.
Correlation Analysis of Seasonal Changes on Aerosol Concentration Using Remote Sensing in Java Island Muhammad, Garda Asa; Amaanah, Annisa; Dewi, Vanya Chathy Kemala
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.636

Abstract

Aerosols are small particles in the atmosphere that affect the climate through direct and indirect mechanisms. Aerosols can influence the climate and play a role in cloud formation and precipitation. This study aims to analyze the relationship between seasonal changes and aerosol concentrations, and to identify parameters that influence aerosol concentrations in Java Island using remote sensing. The method used in this study is the Pearson correlation test to determine the relationship between seasonal changes and aerosol concentrations in the atmosphere. The results show that there is a relationship between Aerosol Optical Depth (AOD) and rainfall with a correlation value (R) of 0.8. This result indicates a significant relationship between the two variables. Meanwhile, the analysis results between Aerosol Optical Depth (AOD) and wind speed show a correlation value (R) of 0.05. This result indicates that the relationship between Aerosol Optical Depth (AOD) and wind speed is very weak between the two variables.
Unsupervised YouTube Video Segmentation of “Bendera One Piece” Content Using Medoid-Based Clustering with Statistical Significance Testing Budiaji, Weksi; Kumenap, Patricia; Delano, M Fabian; Wijaya, Ferdian; Riyanto, Rifqi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.639

Abstract

The curse of dimensionality and sparsity are well-documented phenomena in applied statistics where the data’s dimensionality (number of features) far outnumbers the observations. This work aims to present an integrated applied statistics framework to distill semantic structure from high-dimensional data by combining pre-processing, dimensionality reduction via principal component analysis, medoid-based clustering (partitioning around medoids and simple k medoids), and a modified Statistical Significance Clustering (SigClust) test for validation and inference in the context of viral media. In this case study, we demonstrate an approach that segments and interprets YouTube videos from the lens of the Indonesian viral media “Bendera One Piece” through its user commentary. The PCA-based dimensionality reduction helped resolve the curse of dimensionality, where the first principal component alone explained 80% of the variance in text-based features and captured a dominant socio-political pattern. Internal validation and the SigClust test agreed on the presence of a statistically significant three-cluster solution that could be labelled as the audiences of “Pop-Culture Enthusiasts”, “Cautious Observers”, and “Political Protesters”. The study presents the utility of integrating established statistical methods with a modified validation step for high-dimensional text data analysis and pattern recognition.
Revealing Competitiveness and Key Drivers of Nickel (HS 75) Exports: Evidence from Seven Major Destinations, 2014–2023 Siahaan, Vendredy P. Lucasio; Kartiasih, Fitri
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.640

Abstract

The downstream policy is implemented to encourage Indonesia’s processed nickel products. Processed nickel under Harmonized System (HS) 75 is a value-added product that has potential for the Indonesian economy. Globally, Indonesia’s exports of nickel HS 75 have increased significantly. This increase occurred after the implementation of the downstream policy. However, the increase in export volume did not occur uniformly across all trading partner countries, hence further analysis of the implemented downstream policy is necessary. This study aims to analyse the effect of the down streaming policy and macroeconomic variables such as the destination country’s GDP per capita, real prices, exchange rate, and the RCA index significantly affect the export volume of nickel (HS 75), while population and the downstream policy do not have significant effect. These findings indicate that the downstream policy has not yet effectively increased export volumes to trading partner countries.
Logical Modelling of Statistical Data Using the SDMX Standard: Case Study on the Quarterly Gross Regional Domestic Product Table Amandasari, Kartika; Pratama, Nano Yulian; Aditama, Farhan Satria; Marsisno, Waris
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.641

Abstract

Poverty, as a national issue, necessitates data-driven policy planning informed byaccurate and consistent statistics. To ensure the optimal quality and consistency of statistical datareporting across diverse regions, the adoption of an international standard is crucial. TheStatistical Data and Metadata Exchange (SDMX) standard facilitates the structured exchange ofdata and metadata. This study aims to design and implement a statistical indicator data modelusing the SDMX standard to improve table consistency. We utilized Quarterly Provincial GrossRegional Domestic Product (GRDP) data as a case study and applied the Design ScienceResearch Method (DSRM) as the methodology. The results demonstrate that modeling theGRDP data using SDMX yields a uniform and highly consistent table structure, significantlyenhancing the consistency of statistical data reporting across regions.

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