cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Lutfi Rahmatuti Maghfiroh
Contact Email
lutfirm@stis.ac.id
Phone
+6281381703898
Journal Mail Official
icdsos@stis.a.cid
Editorial Address
Jalan Otto Iskandardinata 64 C Jakarta
Location
Kota adm. jakarta timur,
Dki jakarta
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
 14   15   16   17   18 
Small Area Estimation of Multidimensional Poverty in East Java Province Using Satellite Imagery Helen Cantika Laura Aisyatul Ridho; Rindang Bangun Prasetyo
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 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.v2023i1.417

Abstract

The government has so far focused on a monetary approach to overcoming poverty, while poverty is multidimensional. Holistic and accurate poverty indicators are needed as material for policy formulation, such as the Multidimensional Poverty Index (IKM), which is calculated from raw data from the National Socioeconomic Survey (SUSENAS). However, the direct estimation of the multidimensional poverty headcount (AKM) is only accurate at the provincial level, as seen from the relative standard error (RSE) of several districts and cities, which is still above 25 percent. Increasing the sample size requires time, effort, and cost, so the Small Area Estimation (SAE) method can be an alternative. Apart from using official statistics for accompanying variables, satellite imagery has the advantage of being up-to-date and available up to a granular level. This study aims to estimate the AKM at the district/city level in East Java Province by utilizing satellite imagery and official statistics in SAE. The results showed that SAE HB Beta-logistics, with the accompanying variables combined with satellite imagery and official statistics, has a higher accuracy than direct estimation.
Analyzing Infectious Disease in Multiple District in East Nusa Tenggara (ENT) using K-Means Clustering and Correspondence Analysis Adhari, Fadlan; Lintang Sulistyoreni, Gabriela; Jocelyn Jakson, Jessica; Sekar Larissa, Angelina; Sri Afrianti, Yuli; Hanif Sulaiman, Fadhil
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.v2025i1.426

Abstract

Infectious diseases remain a major public health concern in Indonesia, particularly in East Nusa Tenggara (ENT), where tuberculosis (TBC), dengue haemorrhagic fever (DHF), and HIV/AIDS are obtaining high cases. These diseases are not only influenced by individual and environmental factors but also by spatial characteristics such as population distribution and regional infrastructure. Therefore, analyzing spatial factors is crucial to better understand and manage the spread of infectious diseases in ENT. This study uses data from 2023 to 2024 across 22 districts in ENT, focusing on the prevalence of TBC, DHF, and HIV/AIDS. K-means clustering is first applied to classify the districts into three groups based on area size and population, aiming to identify spatial patterns of disease severity. The clustering process yields a silhouette coefficient of 0.48, indicating moderately valid group separation. Subsequently, correspondence analysis is used to examine the relationship between the resulting clusters and the three diseases. The result reveals that Cluster A, which has the highest population density, shows a strong association with all three infectious diseases. These findings suggest that population density plays a significant role in the transmission of infectious diseases and should be considered in future health intervention strategies.
Parameter Estimation in Hierarchical Models: A Comparison of Bayesian and SGD-Adam Approaches on Biomass Data of Lutjanidae Matualage, Dariani; Sadik, K; Kurnia, A; Monim, H F; Pakiding, F
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.v2025i1.428

Abstract

Hierarchical statistical models are widely used to analyse data with nested structures or repeated measurements, allowing variability across levels to be partitioned and providing more accurate parameter estimation than standard regression models. In the Bayesian framework, parameter estimation often uses Markov Chain Monte Carlo (MCMC), which accommodates complex structures and yields full posterior distributions. However, MCMC is computationally intensive, limiting scalability for large datasets. Recent advances in optimization methods, such as Hierarchical Stochastic Gradient Descent (HSGD) with Adaptive Moment Estimation (Adam), offer a faster and more efficient alternative for hierarchical models. This study applies Hierarchical Bayesian and HSGD-Adam approaches to fish biomass data of the family Lutjanidae from seven Marine Protected Areas (MPAs) in Raja Ampat, Indonesia. The model incorporates ecological predictors such as hard coral cover, distance to the nearest village and period of monitoring, with random effects for area of MPA. Comparison of predictive performance showed that the Bayesian model performed slightly better in RMSE, indicating its ability to capture extreme biomass variations, while SGD-Adam model achieved a lower MAE, reflecting greater stability in prediction. These findings demonstrate that advanced hierarchical modelling methods can enhance ecological data analysis and provide timely, data-driven insights for sustainable marine conservation policy.
Mapping Regional Economic Resilience of Indonesian Provinces Through PCA and K-Means Analysis to Support Regional Development Policy Optimization Thalita, Bella Cindy; A'la, Kevina Alal
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.v2025i1.430

Abstract

In Indonesia’s post-decentralization era, assessing regional economic resilience is critical to promoting inclusive development. This study constructs a composite resilience index using seven indicators Human Development Index (HDI), Open Unemployment Rate, GRDP per capita, Gini Ratio, Economic Growth, Capital Expenditure, and Own-Source Revenue (OSR) across 34 provinces from 2020–2024. Principal Component Analysis (PCA) and K-Means clustering are applied to identify resilience patterns and classify provinces into high, moderate, and low resilience categories. The findings reveal significant interprovincial disparities. Provinces such as DKI Jakarta (HDI: 81.65), Bali (HDI: 76.54), and DI Yogyakarta (HDI: 80.22) consistently demonstrate high resilience, supported by low unemployment (e.g., Jakarta: 5.78%) and robust fiscal capacity (e.g., OSR share: Jakarta 58.29%). In contrast, Papua and West Papua exhibit lower resilience scores, characterized by HDI below 65, limited OSR below 15%, and economic growth volatility. Correlation analysis indicates a strong positive association between HDI and fiscal indicators (r = 0.82), while OLS regression confirms OSR and Capital Expenditure as significant predictors of resilience (p < 0.05). Spatial mapping highlights geographic clustering of resilience, with Western Indonesia outperforming the Eastern region— underscoring persistent spatial inequalities. These findings reinforce the necessity for regionally differentiated policies. The study recommends enhancing fiscal autonomy, investing in human capital, and integrating Fintech-based financial inclusion, especially for lagging regions. The study recommends boosting fiscal autonomy, investing in human capital, and leveraging Fintech for inclusive growth. This framework supports evidence-based policies aligned with Indonesia’s SDG and post-2024 development goals.
Evaluating the Impact of Ibu Kota Nusantara (IKN) Development on Land Cover Using Machine Learning-Based Sentinel-2A Satellite Image Classification Aimariyadi, Wisnu; Batrisybazla, Adinda; Tobing, Vanessa Ruth Evelyn; Kurniawan, Robert
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.v2025i1.431

Abstract

The development of Ibu Kota Nusantara (IKN) in East Kalimantan as Indonesia's new capital city has the potential to cause significant changes to land cover patterns, especially in tropical rainforest areas. This study aims to evaluate the impact of IKN development on land cover using Sentinel-2A satellite image data and a machine learning approach. The study area is focused on the IKN Core Urban Area by comparing land cover conditions in 2022 before development and 2024 after development. Three classification methods were used including Random Forest, Support Vector Machines, and Classification and Regression Trees. The results showed that the RF model had the best accuracy with an overall accuracy value above 93% in both time periods. Spatial analysis showed a decrease in vegetation area and an increase in open land as an indication of intensive land clearing activities. These findings emphasize the importance of continuous land cover monitoring to support IKN's vision as a green city and achieve sustainable development targets (SDGs 11 and 15). This research is expected to serve as a reference for the formulation of adaptive and environmentally friendly spatial policies.
Disaggregating the Hidden: Small Area Estimates of Child Labor in Bali Province Agung, Ahmad Nadifa Al; Sari, Arlita Dwina Firlana; Azarine, Clarissa; Oktaviana, Lisda; Aqsha, Zidan Akbar Al; Istiana, Nofita
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.v2025i1.433

Abstract

Child labor remains a critical concern in Indonesia, including in Bali Province, which exhibits a higher prevalence than the national average. However, efforts to formulate effective local policies are often hindered by the unreliability of child labor statistics at the regency/municipality level, primarily due to high Relative Standard Error (RSE) values. This study seeks to estimate more reliable proportion of child labor at the regency level in Bali through the application of Small Area Estimation (SAE). The analysis utilizes data from the August 2024 Sakernas survey, supplemented with contextual variables from the 2024 PODES dataset. The SAE approach employed was the Hierarchical Bayes method with a Beta distribution (HB-Beta). The findings indicate that the HB-Beta model yields better accurate estimates, as evidenced by RSE values below 25% across all regencies. This demonstrates the potential of the HB-Beta model produces more accurate estimates than direct estimates, as it can better reflect differences between regency and help design more effective local policies to reduce child labor.
Panel Data Regression Modelling on The Analysis of The Influence Of Fiscal Decentralization to Poverty In Maluku In 2020-2024 Bachtiar, Bayu Aji; Sa'adah, Miftahus
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.v2025i1.443

Abstract

Maluku Province persistently records one of the highest poverty rates in Indonesia, despite sustained fiscal transfers from the central government. This study examines the relationship between fiscal decentralization and poverty reduction in Maluku from 2020 to 2024 through a panel data regression approach, enabling simultaneous analysis of spatial and temporal variations across districts. Poverty data were sourced from Badan Pusat Statistik (BPS) and fiscal variables from Direktorat Jenderal Perimbangan Keuangan (DJPK). The empirical results demonstrate that Regional Original Revenue (PAD), general allocation funds (DAU), and village funds (DD) exert statistically significant negative effects on poverty rates, with DD showing the strongest marginal impact. By focusing on a structurally disadvantaged province, this study contributes to the empirical literature by providing region-specific evidence on the effectiveness of fiscal decentralization mechanisms in reducing poverty. The findings underscore the importance of strengthening local fiscal capacity and optimizing the allocation of intergovernmental transfers to achieve more equitable and sustainable poverty alleviation.
Spatial Model for Food Security in Eastern Indonesia 2024 Shohwah, Fathiyah Nur; Arufi, Imam Fathoni; Wicaksono, Mohammad Iqbal; Meilawati, Nadia Lutfi; Meilani, Nilam Cahya; Sohibien, Gama Putra Danu
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.v2025i1.468

Abstract

Food security is the condition of meeting food needs for the country down to the individual level, as measured by the availability, affordability, utilization, and stability of food. Despite being a basic human need, food security in Indonesia is not evenly distributed, especially in Eastern Indonesia. Based on these findings, this study aims to determine the general picture of food security and the factors influencing it in districts/cities in Eastern Indonesia in 2024. The method used is the Spatial Durbin Model (SDM) with an inverse distance weighting matrix. The results show that the variables Distribution of GRDP of Sector Agriculture, Forestry and Fishing, Poverty Rate, Average Years of Schooling, Lag of Food Security Index, Lag of Open Unemployment Rate, and Lag of Poverty Rate have a significant influence on the Food Security Index variable in districts/cities in Eastern Indonesia in 2024.
Application of the Geographically Weighted Negative Binomial Regression (GWNBR) Method to Tuberculosis Cases in North Sumatra Province in 2024 Tinambunan, Titin Julianti Br; Nufus, Nisa Hayatun; Meilawati, Nadia Lutfi; Rahma, Rezky; Wicaksono, Febri
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.v2025i1.474

Abstract

Tuberculosis is one of the leading causes of death worldwide. Approximately 1.2 million deaths occur annually due to tuberculosis. According to the World Health Organization (WHO), Indonesia is the second-largest tuberculosis country after India, with a 10% prevalence rate (WHO, 2024). According to Ministry of Health data, in 2024, North Sumatra was the province with the highest number of TB cases on Sumatra Island, with several cases above the national average, ranking third in Indonesia. The number of tuberculosis cases in North Sumatra is census data and is overdispersed, with spatial influences. Therefore, the method used is Geographically Weighted Negative Binomial Regression (GWNBR), which produces local parameters. The results show that GWNBR forms eight regional groups based on significant variables. Rainfall and per capita expenditure variables have a significant influence in all districts/cities, and the percentage of BCG immunizations and the percentage of smoking population have a significant influence in almost all regions. Meanwhile, health fund allocation only shows a significant influence in several districts/cities. The AIC value of the GWNBR is not smaller than the AIC value of the negative binomial regression. However, the GWNBR model can be used to examine the influence of independent variables on tuberculosis cases spatially in North Sumatra.
Regional Clustering of Food Insecurity to Support the Attainment of SDG 2: Zero Hunger through Machine Learning Approaches Nuradilla, Siti; Saputra, Wawan; Rizal, Muhammad
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.v2025i1.475

Abstract

Food security remains a persistent development challenge in Indonesia, with regional disparities posing significant barriers to achieving equitable access to nutritious and sufficient food. This study aims to classify and cluster districts and cities in Indonesia based on their food security vulnerability levels, thereby supporting the attainment of SDG 2: Zero Hunger. We employed a machine learning approach using a dataset of 514 regions and nine food security indicators sourced from national databases. The classification phase compared three algorithms, Random Forest, XGBoost, and LightGBM, under multiple data preprocessing scenarios, including outlier handling (IQR and Isolation Forest) and class balancing (SMOTE). LightGBM with IQR preprocessing delivered the best performance, achieving an accuracy and F1-score of 0.984. For clustering, DBSCAN and HDBSCAN were applied using the six most important features identified by the classifier. DBSCAN showed slightly better performance based on Silhouette Score (0.5639), resulting in three regional groupings: food-secure, highly vulnerable, and outlier regions. The analysis revealed that socio-economic factors and access to basic infrastructure remain critical determinants of food insecurity. The results underscore the importance of data-driven approaches in policy formulation and highlight the value of machine learning in producing more targeted, efficient, and adaptive food security interventions in Indonesia.

Page 16 of 26 | Total Record : 251

 14   15   16   17   18 

Filter by Year

2021 2025


Reset
Filter By Issues
All Issue Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St