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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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Job Competency Extraction in Information and Technology Sector Using K-Means and Non-Negative Matrix Factorization (NMF) Algorithms Rifa Geandra, Alfitra; Mumtaz Siregar, Amir; Nooraeni, Rani
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.v2025i1.684

Abstract

The advancement of information technology has led to a surge in online job vacancy data, which contains valuable information about the skill demands in the digital labor market. This study aims to extract job competency in the information and technology sector using a combination of KMeans clustering and Non-Negative Matrix Factorization (NMF). A total of 350 job postings were collected from the Kalibrr platform and processed through web scraping, text preprocessing, and feature representation using TF-IDF. The clustering results indicate that the optimal configuration consists of 10 clusters, as evaluated using the Silhouette Score and Davies-Bouldin Index. Each cluster represents a specific job topic, such as backend development, data science, QA automation, cybersecurity, and digital marketing. The results offer a structured overview of digital skill demands and can be utilized by educational institutions, training providers, and labor policy makers. However, the dataset’s limited size, reliance on a single job platform, and the use of traditional machine learning techniques may not capture all semantic variations and complexities present in the broader job market. Consequently, future work should involve larger and more diverse datasets as well as advanced deep learning text representation approaches to enhance the robustness and generalizability of the results. 
Determinants of Comprehensive Understanding of Stunting among Indonesian Pregnant Women and Mothers of Toddlers Aged 0–23 Months in 2023 Silalahi, Agnes Rosihan Kristianti; Rahani, Rini
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.v2025i1.688

Abstract

Stunting is a chronic nutritional disorder that remains a priority in Indonesia. As withthe second goal of the SDGs (zero hunger), the Ministry of Health (MoH) has implemented acommunication strategy for behavioural change and community empowerment through a classprogram for pregnant women and mothers of toddlers class using the Maternal and Child Health(MCH) book. However, it is still not optimal to increase the understanding of stunting. The 2023Indonesian Health Survey (IHS) shows that women in Indonesia still have a poor comprehensiveunderstanding of stunting. It has includes pregnant women and breastfeeding mothers as keytarget groups for stunting reduction. This study aims to describe and analyse the characteristicsof Indonesian pregnant women and mothers of toddlers aged 0–23 months that significantlyinfluence their comprehensive understanding levels of stunting. Data from 2023 IHS wereanalysed using descriptive statistics with graph and table, together with inferential analysisthrough ordinal logistic regression using the Proportional Odds Model (POM). The result showsthat the majority of these mothers have a poor level of comprehensive understanding of stunting,with five variables having a significant influence, namely: access to information, education level,employment status, socioeconomic status, and residence area.
Monetary Policy Analysis in Indonesia: The Dynamic Relationship Between the BI Rate, Inflation, and the Rupiah Exchange Rate Akhsan, Izzat Muhammad; Maharani, A S; Baity, I N
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.v2025i1.689

Abstract

Monetary policy is crucial for sustaining Indonesia's macroeconomic stability, especially through the benchmark interest rate (BI Rate), which serves as the primary tool of Bank Indonesia. This research revisits the transmission of monetary policy within a contemporary framework marked by post-pandemic recovery, global monetary tightening, and domestic policy shifts under the new administration in 2024. Utilizing monthly time series data from January 2010 to March 2025, this study applies the Vector Autoregression (VAR) and Vector Error Correction Model (VECM) methodologies to examine the dynamic relationships among inflation, the exchange rate (USD/IDR), and the BI Rate. The results affirm the presence of long-term relationships among the three variables, aligning with earlier research, while also revealing significant short-term dynamics that indicate an increased sensitivity of the exchange rate and inflation to interest rate changes during times of global uncertainty. By extending the analysis period to 2025 and considering the context of post-pandemic recovery and policy transitions, this study offers updated empirical insights into the changing effectiveness of Indonesia's monetary policy transmission mechanism. The findings provide important implications for policymakers in developing interest rate strategies aimed at achieving a balance between inflation control, exchange rate stability, and economic recovery.
A Multi-Temporal Remote Sensing Approach to Quantify Land Cover Change and its Impact on Ecosystem Sustainability in Riau, Indonesia Purba, Novrian Maria; Hariyanti, Fitri; Saputra, Andriansyah Muqiit Wardoyo
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.v2025i1.691

Abstract

This study analyzes land cover change in Riau Province from 2015 to 2024, focusingon deforestation and degradation as indicators of ecosystem sustainability. Landsat 8 OLI/TIRSand Landsat 9 OLI-2 imagery processed in Google Earth Engine (GEE), combined with MODIShotspot data (MOD14A1) and socioeconomic indicators—Gross Regional Domestic Product(GRDP) and Open Unemployment Rate (OUR) from Statistics Indonesia (BPS)—were used toassess spatiotemporal patterns. The Normalized Difference Vegetation Index (NDVI) wasapplied with thresholds for deforestation (NDVI < –0.3) and degradation (–0.3 ? NDVI ? –0.1).Results show that 2015 was the most severe period, dominated by peatland fires, while 2019recorded forest loss at a lower intensity and 2020–2024 indicated partial vegetation recoverylinked to restoration efforts. Pelalawan, Indragiri Hilir, and Kampar were the most affecteddistricts. Correlation analysis revealed that fire hotspots had the strongest association with landcover change, while economic and social indicators showed weaker relationships. Peatland firesremain the main driver of land degradation, emphasizing the need to strengthen fire management,peatland protection, and sustainable plantation governance to support Sustainable DevelopmentGoal (SDG) 15 on Life on Land, particularly the target of Land Degradation Neutrality (15.3.1)by 2030.
Implementing LSTM-Based Deep Learning for Forecasting Food Commodity Prices with High Volatility: A Case Study in East Java Province Nensi, Andi Illa Erviani; Pangesti, Windi; Syukri, Nabila; Maida, Mahda Al; Notodiputro, Khairil Anwar
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.v2025i1.692

Abstract

Accurate food price forecasting is essential for maintaining market stability and food security. East Java Province was selected as the study area because it is one of Indonesia’s main food production centers and a major contributor to national inflation. This study compares three deep learning architectures LSTM, Bi-LSTM, and hybrid CNN-LSTM to forecast the prices of four key food commodities (red chili, shallots, medium-grade rice, and beef) in East Java. Hyperparameter tuning was performed using grid search, and performance was evaluated using MAPE, MAE, and RMSE. The results show that the Bi-LSTM model consistently provides the best performance compared to LSTM and CNN-LSTM across the four analyzed commodities. Based on MAPE, MAE, and RMSE values, Bi-LSTM achieved the lowest forecasting errors for all commodities. The MAPE values of Bi-LSTM were 1.73% for red chili, 0.60% for shallots, 0.23% for medium-grade rice, and 0.08% for beef, all of which were lower than those of LSTM and CNN-LSTM models. These findings highlight Bi-LSTM’s bidirectional architecture, which leverages contextual information from both past and future data sequences, making it the most robust and effective model for forecasting food prices under varying volatility. The study provides practical insights for policymakers and supply chain stakeholders in supporting price stability and food security.
Enhancing Poverty Rates Reliability Using Small Area Estimation Permatasari, Novia
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.v2025i1.695

Abstract

This study systematically compares the performance of three Small Area Estimation(SAE) methods—Empirical Best Linear Unbiased Predictor (EBLUP), Hierarchical Bayes (HB)Beta, and HB Flexible Beta—using two different auxiliary data sources-Village Potential(Podes) and Socio-Economic Registration data (Regsosek). The SAE methodologies wereapplied in a case study focusing on Java Island, Indonesia. Direct estimates remain has highRelative Standard Errors (RSE) above 25%, indicating low reliability. EBLUP methodsimproved estimate reliability but still produced some unreliable estimates. The HB Beta methodfurther reduced RSE values, while the HB Flexible Beta model achieved the lowest RSE,eliminating all unreliable estimates. Moreover, Socio-Economic Registration data consistentlyresulted in lower RSE values compared to Village Potential data, particularly when used withthe HB Flexible Beta model. These result highlight that integrating advanced SAE models suchas HB Flexible Beta with high-quality administrative data such as Socio-Economic Registrationdata is crucial for producing reliable and precise poverty estimates for more targeted andeffective poverty alleviation policies.
Public Infrastructure Accessibility and Property Price Disparities in Jakarta: A Composite Index and Spatial Regression Approach Anam, Khairul; Sulastri, Ai; Putra, Alvin Anugrah; Sari, Annisa Purnama; Aditama, Friscka 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.v2025i1.701

Abstract

This study analyzes spatial inequality in public infrastructure accessibility and Property price in Jakarta Province using a Composite Index and spatial econometric modeling. A data-driven spatial approach is employed to examine the distribution of property price and accessibility to health, education, and transportation facilities. Accessibility is measured using the Entropy Weight Method, while spatial inequality patterns are assessed through Moran’s I and Local Indicators of Spatial Association (LISA). Results reveal significant clustering of high property price and accessibility in central Jakarta, contrasted with low values in peripheral areas, indicating pronounced spatial disparities. Furthermore, Geographically Weighted Regression (GWR) and the Spatial Lag Model (SLM) demonstrate that improved accessibility is positively associated with higher property price, although the magnitude of this effect varies spatially. These findings provide empirical evidence to support data-based spatial planning and infrastructure development policies aimed at reducing urban spatial disparities and promoting more equitable urban growth in Jakarta.
Role of Agricultural Sector and Quality of Its Production Factor in Indonesia: An Application of Input-Output Analysis and Panel Model Pramana, Anugerah Surya; Wicaksono, Ditto Satrio; Fajar, Huda M.
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.v2025i1.705

Abstract

Indonesia has been known as the largest agricultural country in Southeast Asia. However, the sector contribution to national output has declined. This indicates a low interconnection between agriculture and the other sectors despite the sector’s significant potential to stimulate other industries’ output through strong backward and forward linkages. This condition is caused by the role of production factors that determine agricultural output. Therefore, the research aims to analyse agriculture’s linkages with other sectors and to assess the effects its production factor on agricultural output. Using Input–Output multiplier analysis, it is found the agriculture, forestry, and fisheries sector is the largest absorber of labour in Indonesia. This sector is predominantly consumed directly by households. Meanwhile, panel model results for 2010–2024 show that increases in labour without accompanying improvements in quality have a negative effect, whereas investment and credit, as manifestations of capital, have positive effects on agricultural gross value added. Policy implications include prioritizing skills development and improving access to credit and investment to foster adoption of productivity-enhancing technologies, thereby enabling the agricultural sector to grow and exert greater influence on other sectors and on the national economy.
Comparison of Imputation Methods: Traditional, Machine Learning, and Deep Learning on Multivariate Time Series with MCAR and MNAR Taufani Tri Hakiki, Ferigo; Tasbihi, Naufal Luthfan; El Dafi, Akila Akhtar; ., Nurfaudzan; Muthahharah, Andi Shahifah
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.v2025i1.707

Abstract

This study compares the methods of Linear Interpolation, Kalman Filtering, SVR, and RNN-GRU for multivariate time series that exhibit linear trends and seasonality. Synthetic data for three variables were generated for small, medium, and large sample sizes. Missing values were systematically inserted using Missing Completely at Random (MCAR) and Missing Not at Random (MNAR) patterns with proportions of 10%, 20%, and 35%. The accuracy of imputation was evaluated using RMSE, MAPE, and R² over 150 simulation repetitions per scenario. The results indicate that each method has advantages under certain conditions. Linear Interpolation is suitable for data with linear trends, small sample sizes, and low to moderate missingness levels, and is effective for both MCAR and MNAR patterns. Kalman Filtering is optimal for medium to large datasets, particularly in handling linear and seasonal trend patterns with high proportions of missing data due to MCAR. SVR excels in large seasonal data scenarios with MNAR missingness patterns. RNN-GRU performs well under low missingness conditions, particularly for small seasonal datasets with MNAR patterns. These findings emphasise that the choice of imputation method should consider data size, trend patterns, and the missing data mechanism to minimise bias and preserve the integrity of the temporal structure.
Harnessing the Potential of the Blue Economy in Central Java: Mapping, Strategic Development, and Macroeconomic Analysis Ajeng Pangestika, Almira; Wahyudi, Dwi; Al Farizal Pulungan, Ridson
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.v2025i1.708

Abstract

This study pioneers the mapping and analysis of the blue economy's potential across the 35 regencies/municipalities of Central Java by constructing a novel Blue Economy Index (BEI). Notably, this research is among the first in Indonesia to build the BEI using granular satellite data and digital sensor information, and to apply the Two-Step System GMM approach to dynamically analyze the factors influencing its development. This combination provides unprecedented sub national detail and robust insights into effective policy levers. The findings reveal significant disparities among the southern coastal, northern coastal, and non-coastal areas. The southern coastal regions exhibit higher BEI values compared to their northern coastal and non-coastal counterparts, which fall below the average. Results from the Two-Step System GMM regression analysis indicate that internet usage, infrastructure, and the COVID-19 period exert significant effects on the BEI. Specifically, infrastructure development, proxied by Nighttime Light (NTL), demonstrates a negative impact on the BEI, suggesting that environmentally unsustainable infrastructure may undermine the sustainability of the blue economy. Meanwhile, access to digital technology through internet usage plays a crucial role in fostering inclusive blue economy growth. Based on these findings, the proposed policy recommendations include optimizing environmentally friendly infrastructure development, leveraging digital technology to expand market access, and strengthening the resilience of the blue economy through Adaptive-Responsive-Innovative (ARI) crisis policies. Consequently, the development of the blue economy in Central Java is expected to enhance the sustainable welfare of coastal communities while fully optimizing the potential of coastal areas.

Page 24 of 26 | Total Record : 251

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