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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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Improving The Accuracy of Area Sampling Frame Estimators for Agricultural Surveys Using Unequal Clustered Segment Sampling: The Case of Indonesia Zikra, Hazanul; Buana, Widyo Pura; Bimarta, Yocco; Paramitasari, Nurina
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.v2025i1.477

Abstract

Accurate rice production data are vital for maintaining national food security and formulating effective agricultural policies. In Indonesia, the Area Sampling Frame (KSA) method has been widely implemented to estimate rice harvest areas using segments of 300 meters×300 meters represented by nine observation points. However, this approach faces limitations, particularly the risk of undercoverage bias when estimating areas across different rice growth stages, especially if the observation points fall outside the target rice-growing regions  as population area. To address this issue, the present study introduces the Unequal Clustered Segment Sampling method as an alternative to the traditional KSA approach. The Unequal Clustered Segment Sampling method improves estimation accuracy by refining the sampling frame and excluding non-target segments, spatial points located outside actual rice-growing regions. Through a design-based estimation framework, the proposed method accounts for unequal cluster sizes, allowing a more representative depiction of field conditions. The empirical results demonstrate that the Unequal Clustered Segment Sampling method significantly reduces bias and enhances the precision of rice area estimates compared to the conventional KSA. These findings suggest that incorporating unequal clustered segment sampling designs into KSA-based surveys can yield more reliable and representative estimates, particularly in heterogeneous or fragmented agricultural landscapes.
Spatial Spillover Effects in Food Security: A Spatial Lag Fixed Effects Model for Regencies and Cities in West Sumatra (2019–2023) Tanjung, Fadhel Imam Haichal; Tanur, Erwin
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.v2025i1.485

Abstract

Food security is a key pillar of national development, reflecting a region’s ability to sustain food availability, accessibility, utilization, and stability. The Food Security Index (FSI) serves as a crucial measure of this capability. Based on 2023 data, West Sumatra Province achieved the highest FSI score on the island of Sumatra. This study analyzes food security in 19 regencies and cities of West Sumatra from 2019 to 2023 using a Spatial Lag Fixed Effects Model. The research integrates spatial analysis and panel data approaches to identify determinants of the FSI and assess spatial spillover effects between regions. Secondary data were obtained from the Statistics Agency (BPS) and the National Food Agency. The results reveal significant spatial autocorrelation in most years, except 2023. The best-fitting model is the Spatial Lag Fixed Effects Model. Changes in land area, food expenditure, and rice productivity significantly improve FSI, while non-food expenditure and economic growth do not show a positive effect. The findings emphasize the importance of incorporating spatial dependencies in regional food security policies. Moreover, significant spillover effects indicate that improvements in one area can influence neighboring regions. Therefore, inter-regional cooperation and integrated food distribution policies are essential to achieving sustainable food security.
Spillover Impacts of Informal Employment on Indonesia's Food Security Anggara, Rizki Tri; Alfahma, Elsya Gumayanti
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.v2025i1.486

Abstract

This study analyzes the impact of informal employment on household food security in Indonesia, focusing on regional disparities in provinces with high concentrations of informal workers. Using national socioeconomic survey data, logistic regression models initially assessed the associations between informal employment and food security outcomes. To strengthen causal inferences and mitigate selection bias, a comprehensive Propensity Score Matching (PSM) analysis was subsequently conducted. The findings from both approaches consistently link informal employment to adverse food security outcomes, including food availability concerns, limited access to nutritious food, and lower dietary diversity. Provinces with a high prevalence of informal workers consistently demonstrate poorer food security metrics, with the PSM analysis revealing more pronounced negative impacts in these regions, indicating significant spillover effects. Factors such as tertiary education, internet access, and health insurance are positively associated with improved food security, highlighting the critical role of human capital and resource access. These results underscore the importance of employment stability and regional labor market structures in shaping food security. Policies promoting formal employment and stronger social safety nets are critical for equitable food security across Indonesia.
Predictive Insights: Unmasking Breast Cancer Biomarkers through machine learning and Systems Biology Zainulabidin, A A; Sufyan, A J; Thirunavukkarasu, M K
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.v2025i1.493

Abstract

Breast cancer is a complex and heterogeneous disease in nature with quite high ratesof metastasis and recurrence that cause significant morbidity and mortality. Despite theimproved treatment options with new medical therapies, a proper understanding of the molecular mechanism in breast cancer development and its progression is of utmost necessity. Hence, we conducted a comprehensive analysis on transcriptomic profiling combined with SHAP feature importance calculation in an attempt to find potential molecular targets. Among the 9 machine learning models generated, random forest model displayed an accuracy value of 0.96 for breast cancer prediction. KRT17, KRT5 and FABP5 were the commonly resulted prognostic biomarkers during the DGE and feature selection approaches. Furthermore, gene enrichment and functional annotations of key genes reveals the importance of these key genes in breast cancer progression. The survival analysis confirms the risk associate with key genes in breast cancer patients. Therefore, this finding show the effectiveness of machine learning combine with DGE in Biomarkers discovery and experimental validation of these genes would be a promising approach to eliminate the clinical complications during the breast cancer treatment.
Classification of Urban and Rural Villages with Machine Learning on Satellite Image Data and Points of Interest Parulian Josaphat, Bony; Syukur Rahmat Zega, Alvandi
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.v2025i1.495

Abstract

An evaluation of the Sustainable Development Goals with data disaggregated by residential area, namely urban and rural areas, is essential. This study proposes the use of satellite imagery and point of interest (POI) data with machine learning methods to classify urban and rural villages, specifically in North Sumatra Province. The data used includes satellite imagery from various sources, such as NOAA-20, Sentinel-2, Sentinel-5P, and Terra, as well as Google Maps, covering various variables including NTL, NDVI, NDBI, NDWI, NO?, CO, and LST, along with POIs categorized under education, economy, health, and entertainment. The machine learning methods used were Decision Tree and Support Vector Machine, with data imbalance addressed through resampling techniques such as Random Under sampling (RUS). The results of the study show that the Support Vector Machine model with RUS produced the best weighted average F1-score of 87.74% for the classification of urban and rural villages, with NTL being the most important feature in the model formation. This study is expected to be an alternative for BPS in the classification of urban and rural villages.
Forecasting Composite Stock Price Index on Indonesia Stock Exchange Using Extreme Learning Machine Parulian Josaphat, Bony; Hutajulu, Dhevri Leonardo
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.v2025i1.496

Abstract

Technological advances have driven active participation in digital economic activities, including capital market investment. Stocks remain a dominant instrument, with the Composite Stock Price Index or Indeks Harga Saham Gabungan (IHSG) serving as a primary benchmark for investment decisions in Indonesia. However, its high volatility—driven by economic, political, global, and market sentiment factors—demands accurate forecasting methods. Traditional approaches such as ARIMA and linear regression are limited in capturing the non-linear and complex patterns of stock market data. This study proposes the use of the Extreme Learning Machine (ELM), an artificial intelligence method considered more adaptive to market dynamics. To enhance prediction accuracy, hyperparameter optimization was performed using the grid search method. The research forecasts IHSG performance by incorporating exogenous variables, namely gold prices, the US dollar to rupiah exchange rate, and a COVID-19 dummy variable. The optimal model utilized a hidden layer configuration of nine neurons. Evaluation results indicate that the ELM models effectively perform multi horizon forecasting (t+1 to t+5), as evidenced by low MAE, MAPE, and RMSE values across horizons. The five-day IHSG forecasts are 7,242.28, 7,228.42, 7,211.02, 7,192.67, and 7,174.06, demonstrating the model’s potential in supporting investment decision-making with high accuracy.
Application of Small Area Estimation for Estimating Households Living in Adequate Housing at the Subdistrict Level in DKI Akbar, Muhammad; 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.v2025i1.497

Abstract

Access to adequate housing is a right of all Indonesian citizens guaranteed by the 1945 Constitution and is part of the Sustainable Development Goals (SDGs), specifically Goal 11. DKI Jakarta is the province with the second-lowest percentage of households living in adequate housing in Indonesia. Estimation at the subdistrict level is needed to support the policy on affordable vertical housing development initiated by the DKI Jakarta Department of Public Housing and Settlement Areas. Direct estimation at the subdistrict level based on the Susenas sampling design would result in inaccurate estimators. To address this issue, this study applies the Small Area Estimation (SAE) method using the Empirical Best Linear Unbiased Prediction (EBLUP) model and the Hierarchical Bayes (HB) Beta model, which leverage auxiliary variables to improve precision. The findings reveal that the HB Beta model provides the best estimates in measuring the percentage of households living in adequate housing in DKI Jakarta in 2024, producing accurate estimates across all subdistricts
Do Extracurricular Activities give ‘Extra’ on Academic Performance? Evidence from Propensity Score Matching Methods Nozaleda, Bryan
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.v2025i1.498

Abstract

This study compares different statistical methods to determine whether participatingin extracurricular activities helps improve students’ academic performance. Utilizing a datasetof 1,000 students, the study balances students who did and did not take part in extracurricularsby adjusting for factors like study hours and attendance. It compares Nearest MahalanobisDistance, Nearest Neighbor Matching (with and without a caliper), Optimal Pair Matching,Optimal Full Matching, Coarsened Exact Matching (CEM), and Inverse Probability Weighting(IPW) based on covariate balance, sample retention, and average treatment effect. Results revealthat IPW performs best in the covariates balance, reducing nearly all standardized meandifferences to near zero while retaining the majority of the dataset. Nearest Neighbor Matchingwith Caliper and Optimal Pair Matching also perform well with significant treatment effectestimates and relatively strong model fits. However, each method involves trade-offs in whichIPW excels in covariate balance but has a higher AIC, a slight compromise in model fit, whileNearest Neighbor Matching with Caliper offers a balance between precision, model fit, andsample retention. In contrast, CEM provides strong covariate balance for categorical variablesbut results in significant sample loss, demonstrating the trade-off between strict matching criteriaand practical applicability. Conversely, Nearest Neighbor Matching without Caliper performedpoorly in balancing covariates. As evidenced by the average treatment effect estimates derivedfrom the propensity score matching (PSM) methods, this study concludes that participation inextracurricular activities has a positive and significant impact on students' academicperformance, with study hours, attendance, and resource accessibility emerging as critical factorsas well. The novelty of this study is in comparing multiple statistical matching approaches sideby side in an educational context, providing guidance for researchers and policymakers.
The Influence of Village Funds, HDI, GRDP, and Unemployment on Poverty in Sulawesi 2017-2024 Using Panel Data Regression Ramadhani, Muhammad Reza; Utomo, Agung Priyo
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.v2025i1.499

Abstract

Poverty in Indonesia remains a significant problem. Generally, rural poverty is higher than urban poverty. Therefore, the government has enacted a village fund policy through. Law Number 6 of 2024 to assist development efforts that can reduce rural poverty. However, despite a decline in national poverty, the poverty rate in Sulawesi has fluctuated. In addition to village funds, other variables influence poverty, such as human development index (HDI), gross regional domestic product (GRDP) per capita, and unemployment rate. The purpose of this study is to determine the effect of village funds, HDI, GRDP per capita, and unemployment on poverty rates in 70 districts in Sulawesi from 2017 to 2024. Data used are sourced from directorate general of fiscal balance (DJPK) for village funds and BPS for other variables. Panel data regression analysis is used to identify variables that influence poverty rates. Based on FEM, it is known that HDI and GRDP per capita have a negative and significant effect on poverty rates in Sulawesi Island. Village funds are insignificant in reducing poverty due to differences in development levels across regions. Therefore, equitable development and incre
Spatial Analysis of Pneumonia in Toddlers on Sumatra Island Using Geographically Weighted Poisson Regression Lumban Gaol, Ruth Natasya Sepbrina Br; Potenza, Maura Bintang; Ihsan, Nur Faqih; Pratama, Galang Ali Fazral; Berliana, Sarni Maniar
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.v2025i1.500

Abstract

Pneumonia remains a leading cause of mortality among toddlers (aged 1 to less than 5 years) in Indonesia, with notable spatial disparities across Sumatra Island. This study examines factors influencing pneumonia incidence in toddlers using a Geographically Weighted Poisson Regression (GWPR) model to capture local variations in the effects of community health centers, complete basic immunization coverage, exclusive breastfeeding rates, and low birth weight (LBW) prevalence. Analyzing 2022 cross-sectional data from 154 districts/cities on Sumatra, the global Poisson regression model confirmed all predictors as statistically significant at the 5% level. The GWPR model with a fixed Gaussian kernel outperformed the global model, revealing five regional clusters with distinct combinations of significant variables. The dominant cluster (140 locations) showed significant effects from all predictors, while smaller clusters (14 locations) highlighted localized patterns, such as reliance on immunization and breastfeeding in rural areas like Rejang Lebong. These findings underscore the need for tailored interventions to address regional disparities in toddler pneumonia.

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