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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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Two-Stage RFM and Macroeconomics Interaction Model for Accurate CLV Prediction in Direct Sales Istopo Hartanto, Unung; Putu Asto Buditjahjanto, I Gusti; Yustanti, Wiyli
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.v2025i1.642

Abstract

This study introduces a two-stage predictive model integrating Recency, Frequency, Monetary (RFM) metrics with macroeconomic indicators to estimate Customer Lifetime Value (CLV) in direct sales, addressing dynamic customer behavior in volatile markets. Data from the Halalmart Sales Integrated System (January 2023–July 2025, 29,893 transactions, ~431 unique customers monthly) were combined with Indonesian macroeconomic indicators (Consumer Confidence Index, Consumer Expectation Index) from Bank Indonesia and inflation data from the Central Bureau of Statistics (BPS). The first stage uses CatBoost classification, achieving 89.3% accuracy to identify active customers, followed by an ensemble regression (CatBoost, XGBoost, LightGBM, Ridge, RandomForest), yielding an R2 of 0.894 for CLV prediction. RFM features contribute 40.3% to classification and 16.2% to regression variance, while macroeconomic interactions dominate, contributing 59.7% and 83.8%, respectively. A key interaction, Monetary and Consumer Confidence Index, shows a 0.773 correlation with CLV. SHAP analysis enhances model interpretability. Despite a skewed dataset with approximately 65% zero CLV, the model supports targeted marketing strategies, offering valuable insights for strategic decision-making in direct sales environments
Unveiling Regional Disparities in Indonesia: Clustering Provinces by Development Indicators Yusman, Akbarrullah; 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.v2025i1.645

Abstract

Indonesia’s pursuit of sustainable development—integrating economic, social, and environmental dimensions—remains challenged by persistent regional disparities. In 2022, only four of seven national priority indicators were achieved, while 21 provinces failed to meet more than three targets. To capture these disparities more precisely, this study applies hierarchical and non-hierarchical clustering to classify 34 provinces based on seven development indicators. The comparative approach enhances robustness: hierarchical clustering reveals inter-provincial linkages, while non-hierarchical clustering improves internal consistency. Validation tests identify Ward’s method as optimal, yielding four distinct clusters. Cluster 1 includes four eastern provinces with multidimensional inequality—high stunting (31.43%), early marriage (10.37%), and low literacy (36.44%). Cluster 2 comprises 20 provinces with structural stagnation, marked by persistent stunting (24.80%) and reliance on primary sectors. Cluster 3 consists of seven industrial provinces with strong economic performance (manufacturing 33.59% of GDP) and improving social indicators. Cluster 4 includes three service-based provinces excelling in social outcomes—lowest stunting (13.07%) and highest literacy (78.46%)—but facing environmental challenges. These findings highlight the urgency of region-specific, evidence-based policy interventions to promote equitable and sustainable development.
The Impact of Training-Testing Proportion on Forecasting Accuracy: A Case of Agricultural Export in Indonesia Wijayanti Septiarini, Tri; Diyah Putri Martinasari, Made; Pariyanti, Eka
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.v2025i1.649

Abstract

Accurate forecasting of agricultural exports is crucial for supporting trade policy and ensuring economic stability in Indonesia. This study investigates the impact of training–testing proportions on the forecasting accuracy of six models: linear regression, decision tree, optimized decision tree, neural network, Auto Regressive Integrated Moving Average (ARIMA), and exponential smoothing. Using Indonesia’s agricultural export data, model performance was evaluated under two data-splitting schemes (80%:20% and 75%:25%) with error metrics including MAE, MSE, RMSE, and MAPE. The results consistently show that statistical time series models outperform regression-based and machine learning approaches. In particular, SES achieved the lowest forecasting errors across all evaluation criteria, with MAPE values as low as 0.93%, followed by ARIMA as the second-best performer. Machine learning models, on the other hand, produced relatively higher error values, suggesting their limited ability to capture temporal dependencies in the data. Importantly, the choice of training–testing proportion did not significantly alter the ranking of model performance, indicating that model selection plays a more critical role than data partitioning. Overall, this study highlights the robustness of exponential smoothing methods as reliable forecasting tools for Indonesia’s agricultural exports and provides evidence-based insights for policymakers in designing effective trade strategies.
Strategic Expansion of Digital Payments in Papua and West Papua: Individual Character Analysis Using Random Over and Under Sampling CART Amelia, Reni; Mun'im, Akhmad
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.v2025i1.651

Abstract

This study examines the characteristics and influencing factors of digital payment usage among individuals in Papua and West Papua. Understanding these characteristics enables stakeholders to design effective strategies for promotion, socialization, and education to support the expansion of digital payment adoption. The analysis uses data from the March 2023 National Socio-Economic Survey conducted by BPS, involving 52,081 respondents aged 17 years and older. A Classification and Regression Trees (CART) approach was applied with random oversampling and undersampling techniques to handle data imbalance. The results reveal that business fields, types of residential areas, and education levels are key determinants of digital payment usage. Three primary user profiles were identified: (1) individuals aged 17+ working outside the agricultural sector with at least a high school education; (2) individuals aged 17+ working outside agriculture, with junior high school education or below, residing in urban areas; and (3) individuals aged 17+ working in agriculture or unemployed, living in urban areas, and having completed high school or higher. These findings suggest that stakeholders should tailor promotional strategies and educational programs based on individual characteristics to effectively increase digital payment adoption in Papua and West Papua.
Spatial Analysis of Food Security Index and Its Factor to Support Program Priority Area in Central Java, Indonesia Syafinda Fyndiani, Saskia; Putri Titisari, Hanung; Fadhiil Al-Ghifaary, Muhammad; Handayani, Tiara; Fadhilah, Achmad
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.v2025i1.652

Abstract

Food security Index (FSI) is a global issue influenced by ecological and socio-economic factors. Food security is a condition in which humans can meet their food needs. Therefore, it is necessary to identify the conditions of food security and the factors that can influence it as a first step in overcoming food insecurity. The study area of this research is Central Java. This study uses spatial autocorrelation method. This method can determine patterns or correlations between study locations using Moran’s I and LISA. This method also provides information related to the relationship between poverty distribution characteristics between locations in Central Java. This study also analyzes the Food Security Index (FSI) in Central Java Province by integrating drought parameters (Normalized Difference Drought Index), poverty levels, food expenditure, and open unemployment rates. The results of the analysis show a correlation between ecological conditions and FSI achievements. These results confirm that the FSI level in the study area does not only depend on natural resources but is also influenced by socioeconomic factors. Thus, the results of this analysis may be beneficial as recommendations for policymakers through a spatial-based approach to provide strategies for improving food security, especially in Central Java.
Spatial Modelling of the Relationship Between the Characteristics of Vegetation Index, Life Expectancy and Fertility Rate in Banten Province Asyari, Ahmad Syuhada Islami; Sumirah, Diana; ., Syaefunnisa; Fadhilah, Achmad; Putra, Andika Permadi
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.v2025i1.659

Abstract

Rapid urbanization in Banten Province has reduced green open spaces, impacting environmental sustainability and demographic dynamics. This study analyzes the spatial relationship between vegetation index, life expectancy (LE), and total fertility rate (TFR) using Landsat 8 imagery (2020–2024) and demographic data from the Central Bureau of Statistics (BPS). The vegetation index, measured using the Normalized Difference Vegetation Index (NDVI), was examined alongside LE and TFR through Pearson correlation and Moran’s I spatial autocorrelation. The results indicate a moderate negative correlation between NDVI and LE (r = -0.561, p < 0.05) and a strong negative correlation between LE and TFR (r ? -0.94). Urban areas such as Tangerang City and South Tangerang City, despite having low vegetation cover, recorded higher LE due to adequate healthcare access. Conversely, rural areas with greater vegetation tended to have lower LE. Spatial analysis identified urban centers as hotspots with high LE, while rural regions appeared as coldspots. These findings confirm that healthcare access and socioeconomic factors can compensate for limited vegetation, while demographic transitions contribute to fertility decline, ultimately supporting sustainable development in Banten Province.
Clustering of Junior High School Education in West Java Based on Density and Dropout Ratios Using Quartile and KMeans Methods Nurkhofifah, Eva; Athina, Dwilaras; Ristiyanti Tarida, Arna; Amelia Pratiwi, Friska
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.v2025i1.662

Abstract

Education disparities across regions often reflect differences in school density, teacher availability, and student dropout rates. This study aims to classifies junior high school education in West Java into more homogeneous groups to better understand these disparities. Two clustering approaches were applied: quartile grouping and the K-Means algorithm. Quartile grouping provided a simple categorization of each indicator into four levels (very high, high, low, very low), while K-Means offers a more flexible and data-driven segmentation. K-Means algorithm produced three distinct clusters: (1) Balanced and Stable regions with proportional ratios and low dropout rates, (2) High-Density but Stable regions concentrated in urban and periurban areas with high student-teacher and student-school ratios but controlled dropout levels, and (3) Elevated Dropout Risk regions, mostly in rural and southern areas, with lower density but higher dropout rates. The comparison shows that quartile grouping is easy to interpret for individual indicators, while K-Means provides more comprehensive insights into multidimensional patterns. This research highlights the potential of clustering methods to guide policymakers in designing differentiated strategies, from infrastructure expansion in dense regions to social support programs in dropout-prone areas. 
Development of Best Beta CAPM with Adjustment of Sharia Elements: A Case Study on Sharia Stocks in Indonesia Aziz, Abdul; ., Supriyanto; ., Abdurakhman
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.v2025i1.663

Abstract

This paper introduces the Best Sharia-based Capital Asset Pricing Model (BSCAPM), a modification of the BCAPM model integrating Islamic finance principles. This study focuses on optimizing the beta parameter within the model by integrating Sharia-compliant factors such as zakat and purification, while excluding short-selling practices. Using data from the Jakarta Islamic Index (JII) from June 2020 to November 2024, the BSCAPM portfolio outperforms the BCAPM portfolio in terms of the Sharpe ratio. The findings indicate that the BSCAPM serves as a viable alternative framework for Islamic investment modelling, providing Muslim investors with a Sharia-compliant, optimal portfolio formation model. The research contributes to the underexplored domain of portfolio selection modelling in the Islamic sector, enriching references on asset pricing in Sharia portfolios, particularly in the Indonesian Sharia stock market.
GIS-Based Analytical Hierarchy Process Flood Hazard Mapping in Deli Serdang, Indonesia Using Satellite Images Hafizhahurrahman, Zaidan; Aulia Kamal, Shafnanda
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.v2025i1.680

Abstract

As of the regions with a high frequency and significant impact of flood disasters, Deli Serdang in North Sumatera, Indonesia highly requires spatial-based hazard mapping as a foundation for mitigation efforts. This study aims to map the flood hazard levels by integrating the Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS). Five parameters were analyzed to construct the model: elevation, slope, rainfall, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI), with data acquired through the Google Earth Engine platform. The AHP weighting results indicate that rainfall is the most dominant factor (40%) influencing the hazard level. The resulting hazard map identifies a clear spatial pattern with a north-to-south gradation, where 50.17% of the total area falls into the high-hazard category, 47.57% into the moderate category, and the remainder into the low-hazard category. A significant finding reveals that all sub-districts within the study area are classified as either moderate or high hazard, confirming the northern coastal zone as the most critical area. The results of this research can serve as a scientific basis for local government in formulating more adaptive and targeted disaster mitigation policies and spatial planning.
Application of K-Medoids for Regional Classification Based on Quality, Access, and Governance of Education in Indonesia Robiati, Silfi; Hakim, Abdul; Dharmawan, Goldy; Khotimah, Chusnul
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.v2025i1.682

Abstract

Education is a fundamental foundation for individuals, yet substantial disparities persist across Indonesia, including both 3T (Disadvantaged, Frontier, and Outermost) and non3T regions. Addressing the limited research on systematic regional mapping based on education indicators, this study analyzes 514 regencies/cities at the senior secondary level using 13 indicators covering three latent dimensions identified through Factor Analysis: education quality, quality of the learning process, and governance and educational participation. Data were processed through outlier detection, standardization, dimensionality reduction using Principal Component Analysis, factor score extraction, and K-Medoids clustering in RStudio. The optimal solution with three clusters was validated with a Davies–Bouldin Index of 1.44, confirming its effectiveness in capturing regional variation. Results reveal distinct spatial patterns in educational characteristics, where some 3T regions perform comparably to non-3T areas, while certain remote regions face challenges across all dimensions. These findings provide a basis for targeted, cluster-based policy interventions to improve education quality, expand access, and strengthen governance, supporting equitable educational development nationwide. The study demonstrates the utility of combining dimensionality reduction and clustering for evidencebased policy planning and highlights the importance of addressing regional disparities in education.

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