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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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Clustering of Cities/Regencies in East Java Province Based on the Number of Health Workers Using K-Means Clustering Analysis Nashir, Farras Ijlal; Safitri, N R; Salsabilla, D O C
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.v2025i1.710

Abstract

This study aims to classify cities/regencies in East Java Province based on the availability of health workers using the K-Means clustering analysis method. Secondary data was obtained from BPS East Java for the year 2024, covering 12 variables of health worker types. The analysis process included data standardization, determination of the optimal number of clusters using the Silhouette method, and the application of the K-Means algorithm. The analysis results show that the optimal number of clusters is two. Cluster 1 exclusively consists of the City of Surabaya, characterized by a high concentration of modern and technical health workers but lower in community-based health workers. Cluster 2 includes the other 37 cities/regencies, showing a greater dependence on basic health workers such as midwives and nutritionists, with limited access to specialist medical personnel. This study recommends strengthening community health workers in Surabaya and increasing the availability of professional medical personnel in other regions to reduce health service disparities in East Java.
Determination of Inflation Sistercity in Riau Province by Using K-Means Clustering Method Kesuma, M Nata; Yufa, Pedro Rahmat; Hariyanti, 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.v2025i1.711

Abstract

At the present time, the government is placing a significant emphasis on the regulation of inflationary pressures. The government's approach is multifaceted, ranging from the Minister of Home Affairs' direct leadership of coordination meetings on Monday mornings to providing fiscal incentives for regions that can control inflation and removing local government officials who cannot. However, note that BPS-Statistics Indonesia (BPS) does not calculate inflation in all Indonesian regencies and cities. The calculation of inflation only includes four out of the 12 regencies/cities in Riau Province. Therefore, we must establish an inflation sister city to allow regencies/cities not included in BPS's calculations to independently calculate the inflation rate. This study is pioneering in its analysis of Sister City Inflation in Riau Province. The k-means cluster analysis indicates that the city of Pekanbaru and the city of Tembilahan form distinct clusters, with no regencies or cities within their respective clusters that are associated with either of the two cities. Subsequently, the Dumai cluster forms a cluster with Bengkalis, Siak, and Pelalawan. Conversely, Kampar Regency formed a cluster with Kuantan Singingi, Indragiri Hilir, Indragiri Hulu, Rokan Hulu, Rokan Hilir, and the Meranti Islands. Consequently, regions that are not included in the inflation calculation may utilize the data from the cost of living survey in inflation regencies/cities within the same cluster to perform their calculations. Furthermore, if the local government requires the inflation rate as a reference for determining the regional minimum wage, it may employ it from the sister cities that have been established.
Small Area Estimation of Extreme Poverty Using Zero-Inflated Binomial GLMM: A District-Level Case Study in North Sumatra 2024 Lumban Gaol, Marta Desna Fitria Br.; Iryani, Beta Septi; Lestariningsih, Eni
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.v2025i1.714

Abstract

Eradicating extreme poverty is a key objective of Sustainable Development Goal (SDG) 1, with a global benchmark of reducing the proportion of people living below the US$1.90 PPP poverty line. However, in 2024, Indonesia—particularly North Sumatra Province—continues to face persistent challenges in achieving this target. Direct estimation based on the Foster-Greer-Thorbecke (FGT) formula using SUSENAS microdata suffers from large sampling errors (RSE > 25 percent) and zero estimates in multiple districts due to small or absent samples, indicating serious issues of zero inflation and overdispersion. To overcome these limitations, this study applies a model-based Small Area Estimation (SAE) approach using the Zero-Inflated Binomial Generalized Linear Mixed Model (ZIB-GLMM). This method incorporates auxiliary variables from the 2024 PODES dataset and effectively addresses the dual complexities of excess zeros and inter-district variability. Simulation results show that ZIB-GLMM outperforms conventional SAE models in terms of predictive accuracy and model stability. The proposed method offers realistic and policy-relevant district-level estimates of extreme poverty, providing robust evidence to inform targeted interventions and strengthen Indonesia’s national agenda to eradicate extreme poverty.
Business Description Categorization to the Five-Digit Indonesian Standard Classification of Business Field (KBLI) Using Machine Learning and Transfer Learning Amnur, Muh. Alfian; Muhammad Gazali, La Ode; Mumtaz Siregar, Amir; Ariya Jalaksana, Faruq; Nisa Rahayu Ananda Suwendra, Made; Fadila Utami, Nurul; Median Ramadhan, Alif; Krisela Fabrianne, Elisse; Wirata Raja Panjaitan, Eurorea; Aini Izzati, Fitri; Bintang Yuliani Manalu, Jernita; Gilang Hidayat, Muhammad; Hulliyyatus Suadaa, Lya; Yuniarto, Budi; Pramana, Setia
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.v2025i1.719

Abstract

The Indonesian Standard Classification of Business Fields (KBLI) is essential for economic statistics, yet manual classification of business descriptions to five-digit KBLI codes is time-consuming and prone to inconsistencies. This study aims to develop and compare machine learning (Support Vector Machine and Random Forest) and transfer learning  (IndoBERT) models for automating KBLI classification, supported by the preparation of synthetic and real-world datasets for model training. The synthetic data were generated using large language models, validated through human majority voting and complemented with realworld data from the National Labor Force Survey (Sakernas) and the Micro and Small Industry Survey (IMK). The findings indicate that Fine-tuned IndoBERT achieved superior performance, achieving an F1-score of 92.99% and an accuracy of 93.40% on synthetic data, alongside top-1, top-5, and top-10 accuracies of 32.93%, 54.71%, and 63.24% on real-world data. The deployment of fine-tuned IndoBERT as a RESTful API demonstrates its scalability and efficiency, presenting a reliable solution for large-scale KBLI classification in official statistics. 
Estimation of Energy Transition Index based on Official Statistics and Satellite Imagery Data : (Case Study: Regencies/Cities in Indonesia) Syahputri, Sabilla Hamda; 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.v2025i1.724

Abstract

Energy has a crucial role in sustaining human life, its implementation should be optimized based on the principles of sustainable development through a shift from non-renewable to renewable sources. To monitor this shift, the World Economic Forum (WEF) developed the Energy Transition Index (ETI), which measures national-level transitions using conventional statistical data. However, the ETI is limited to the country level, while more detailed assessments are needed at smaller administrative scales such as regencies and cities to capture regional specificities. This study addresses the gap by constructing an energy transition index at the regency/city level in Indonesia for 2024. The analysis integrates official statistics with satellite imagery data to overcome limitations in subnational data availability. Methodologically, Exploratory Factor Analysis and uncertainty analysis were applied. Among five scenario of uncertaincy analysis tested, scenario 1 featuring min-max normalization, unequal weighting across indicators and factors, and linear aggregation produced the most reliable results. The findings reveal that the index is composed of four main factors. Overall, Indonesia’s energy transition index values show a relatively even distribution, yet disparities remain evident across islands and between regencies/cities. Higher scores are concentrated in the western regions, while lower scores dominate the eastern parts of the country.
Dynamic Linkages and Monetary Policy Transmission in the Cryptocurrency Market: A Vector Autoregressive Study of Bitcoin, Ethereum, and The Fed's Interest Rate Azhari, Muhammad Zaki; Ghiffari, M A A; Ghiffari, A
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.v2025i1.727

Abstract

The cryptocurrency market, characterized by high volatility, has evolved into a significant financial asset class, attracting both retail and institutional investors. Understanding its interconnectedness with macroeconomic factors is crucial for risk management and financial stability. This study empirically analyzes the dynamic relationships between two primary crypto assets, Bitcoin (BTC) and Ethereum (ETH), and the monetary policy shifts of the U.S. Federal Reserve (The Fed). Using a Vector Autoregression (VAR) model on daily time-series data from January 1, 2022, to June 16, 2025, this research investigates the short-term dynamics, Granger causality, and shock transmissions within this system. The findings reveal a significant one-way causal relationship from The Fed's interest rate changes to both Bitcoin and Ethereum returns, challenging the weak-form Efficient Market Hypothesis. Furthermore, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses provide robust evidence of Bitcoin's market leadership, with shocks in Bitcoin explaining nearly 70% of the variance in Ethereum's movements. These results highlight a clear hierarchical structure: The Fed influences broad market sentiment, while Bitcoin leads internal market dynamics, offering critical insights for investors and policymakers navigating the digital asset ecosystem.
The Digital Footprint of Public Attention: Forecasting Indonesian Gold Prices using Google Trends Index and Optimized Support Vector Regression Restu Ilahi, Muhammad; Wahyu Wijayanto, Arie
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.v2025i1.730

Abstract

To provide actionable forecasting insights for gold prices in Indonesia’s public sentiment-driven market, this study developed a machine learning framework using the Google Trends Index (GTI) as a sentiment proxy. We employed an Optuna-optimized Support Vector Regression (SVR) model to comparatively evaluate three feature sets (GTI, historical Lag, and a Mix) across seven forecasting horizons (t+1 to t+30). A key advantage of our approach was the identification of horizon-dependent predictor dynamics: results revealed that while historical data excelled for short-term forecasts (MAPE 0.50% at t+5), the contribution of GTI became vital for long-term accuracy, where the hybrid model achieved its peak performance (MAPE 1.92% at t+30). Notably, the GTI-only model showed solid standalone potential (MAPE < 20%). We conclude that a hybrid approach is most effective, validating GTI as a relevant predictor for Indonesia. Furthermore, the proposed SVR-Optuna framework offers a generalizable methodology for forecasting other sentiment-driven assets, providing a clear, actionable guide for model selection based on forecasting horizons.
Unlocking Renewable Energy Potential: The Nexus Between Financial Inclusion and Renewable Energy in Indonesia Primasrani, Byun Jiye; Parina, Okta
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.v2025i1.733

Abstract

Indonesia has pledged to achieve net-zero emissions in 2060. The energy transition can be achieved through financial inclusion. Based on the Environmental Kuznets Curve (EKC) theory, financial inclusion can be a catalyst in reducing environmental impacts if a country has reached the EKC turning point. This study investigates the impact of financial inclusion on the consumption of renewable energy in Indonesia. The data used in this study will be the percentage of renewable energy consumption and the financial inclusion index from the International Monetary Fund 2004 to 2021. Additionally, economic growth and the number of internet users are included as control variables. This study utilizes the Error Correction Model and finds that financial inclusion and internet usage have a negative significant effect on the percentage of renewable energy consumption in the long run. Based on these findings, it can be concluded that according to EKC theory, Indonesia is still in an early stage of development, where increasing financial inclusion and technology still have a negative impact on the environment. Policymakers are encouraged to develop targeted financial inclusion strategies to enhance environmental sustainability. Green finance and green investment are critical solutions to support Indonesia's energy transition.
Extreme Value Theory: Modelling Catastrophic Losses In Sports Injury Juwono, Adriano
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.v2025i1.736

Abstract

Using Extreme Value Theory with a peaks-over-threshold method, we modelled the top 2% of sports-injury losses from 200,000 simulated claims. A generalized Pareto fit via MLE yielded a positive shape (? = 0.783), indicating a fat tail where rare injuries dominate severity. Q–Q and P–P diagnostics show good agreement between model and data. The implied 100-year loss is round 3.31 billion (currency units), and TVaR confirms that conditional on approaching the tail, predicted losses increase quickly. These findings support need for capital buffer to mitigate costly injuries, severe-scenario stress testing, and pricing loadings that specifically consider for costly but rare injuries.
The Digital Frontline: A Thematic Analysis of User Grievances and Satisfaction Drivers for Indonesian Public Service Apps Bangkit Wijaya, Ferdian; Budiaji, Weksi; Priyantama Ramadhan Bagaskara, Rafly; Ainun Tazkia, Zilda; Dwi Anugrah Pertiwi, Dinda
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.v2025i1.738

Abstract

This research assesses Indonesia's digital public service ecosystem by analyzing 50 mobile applications from a wide range of state agencies. Using a computational content analysis of metadata and user reviews from the Google Play Store, this study presents a dual-faceted evaluation. First, a thematic analysis of negative reviews (1-2 stars) reveals that user grievances are overwhelmingly dominated by foundational issues, such as login/access problems, slow performance, and technical glitches, rather than a lack of advanced features. Second, a corresponding analysis of positive reviews (5 stars) identifies that user satisfaction is primarily driven by high-quality features, ease of use, and overall application reliability. Quantitative findings show significant performance disparities across institutional categories, with Ministrydeveloped apps receiving the lowest average user satisfaction. An Importance-Performance Quadrant Analysis further uncovers a critical paradox: many high-download, mandatory apps suffer from low user ratings, indicating a clear disconnect between enforced adoption and usercentric quality. The research concludes that enhancing digital public services requires a strategic shift from feature proliferation to foundational reliability. Ensuring robust core functionalities is paramount to building citizen trust and achieving a successful digital transformation.

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