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
 16   17   18   19   20 
Development of Village Administrative Data Management System Through PAPEDA (Village Population Administration Development Application) in Pitu Village, North Halmahera Regency Ikram, R A D; Kahar, A M; ., Gusrizal
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.v2025i1.504

Abstract

This study discusses the utilization of technology in managing village administrative data, improving public service systems, and providing base data for local government decisionmaking. Using qualitative methods for data collection and the SDLC Waterfall Model for system development, this research analyzes the benefits of PAPEDA (Aplikasi Pembangunan Administrasi Kependudukan Desa), an output of the Desa CANTIK program, on village administrative data management and public services. Based on the evaluation results using Black Box Testing and User Satisfaction Surveys, this study shows that technology utilization in villages positively impacts the community. The use of PAPEDA not only makes it easier for village officials to manage village administrative data but also accelerates the public service process in the village. Residents can access various administrative services online, anytime, and anywhere. Additionally, village monographs and stunting monitoring enable local governments to use them as a basis for development. However, uneven internet connectivity hinders technology utilization, emphasizing the need for local governments to improve internet infrastructure.
Promoting Peaceful and Inclusive Information Security Compliance: A Systematic Review of Assurance Behavior in IT Employees within the Context of SDG-16 in Malaysia Zarilla, Aziela Isma
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.v2025i1.508

Abstract

This systematic review examines the alignment between IT employees' desire,intention, and compliance with information security protocols, a critical issue in Malaysia wherehuman error is a leading cause of data breaches. Situated within the context of SustainableDevelopment Goal 16 (SDG-16), the study analyzes 30 peer-reviewed articles to identify keybehavioral factors. Findings indicate that while training improves knowledge, its impact on longterm behavior is limited. A significant compliance gap is driven by psychological factors likework overload and optimism bias, as well as organizational elements such as culture andmanagement support. The review concludes that effective information security assurancerequires a holistic strategy integrating tailored, ethical training with strong organizational supportto mitigate psychological strain and foster a robust security culture. This approach is essentialnot only for strengthening cybersecurity but also for supporting Malaysia's commitment to digitalresilience and the principles of SDG-16.
Analysis of the Effectiveness of Iterative Prompts in the Integration of Classification and Summarization of User Reports Based on NLP Widodo, Sulisetyo Puji; Akbar, Ilmi Aulia; Al Qorni, Waiz; Ramadhan , Rifqi; Haryono , Febi Dwi
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.v2025i1.510

Abstract

User reports submitted through feedback features or ticketing systems provide valuable insights for improving mobile applications. However, the high volume of reports creates challenges for review and decision-making. Effective classification and summarization are therefore essential to manage this information efficiently, allowing developers to quickly identify recurring issues and support data-driven development strategies. This study automates large-scale user feedback processing using Natural Language Processing (NLP) and evaluates multiple language models. The Bigbird-Small model achieved the highest agreement with the majority (81.51%) due to its ability to process long-text contexts. XLM-R-Base performed competitively (78.08%), while BERT-Base and Roberta-Base showed stable performance (75.68% and 74.32%). Distilbert-Base, though more computationally efficient, had slightly lower accuracy (74.32%). For summarization, Simple Prompt and Iterative Prompt approaches were compared. The Iterative Prompt with four iterations performed best, achieving similarity 0.911, compression 0.846, keyword overlap 0.624, and redundancy 0.070. These results demonstrate that combining automated classification with iterative summarization can significantly improve both efficiency and accuracy in managing user reports, supporting better decision-making and enhanced mobile app development.
AI-Driven Transformation in the Textile Industry: A Bibliometric Analysis and Scoping Review Pitarsi Dharma, Fajar; Laksono Singgih, Moses; Dwi Prastyo, Dedy
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.v2025i1.516

Abstract

Artificial Intelligence (AI) is rapidly reshaping the global textile industry, driving efficiency, precision, and sustainability across its value chain. Yet despite growing enthusiasm, the integration of AI remains fragmented, with limited statistical understanding of where, how, and why these technologies take root. This study addresses that gap by combining bibliometric network analysis and systematic scoping review to map and statistically interpret two decades (2003–2023) of research on AI applications in textiles. Using association strength normalization, VOS modularity clustering, and thematic centrality density mapping, we identified eight manufacturing clusters ranging from fabric defect detection and supply chain optimization to textile waste management and sustainability that structure the field. The novelty of this work lies in repositioning bibliometric analysis as a statistical instrument, not merely a descriptive tool. Keyword co-occurrence networks and citation trajectories are translated into evidence-based research agendas, connecting cluster signals to methodological pathways such as regression modeling, support vector machines, neural networks, and hybrid ML-statistical frameworks. This statistical logic is used to surface gaps. Particularly in empirical validation, predictive modeling, and cross-cluster integration and to chart future directions for data-driven textile innovation. By grounding future agendas in measurable statistical patterns rather than narrative interpretation alone, this study offers a rigorous analytical framework that links research structure to methodological opportunity. The resulting roadmap invites scholars and practitioners to bridge AI, textile engineering, and applied statistics, shifting the field from fragmented experimentation toward coherent, evidence-based innovation.
Estimating the Unemployment Rate at Sub-District Level in West Java Province in 2024 Using Hierarchical Bayesian Approach with Cluster Information Aditya, Randy Daffa; Zukhrufah, Awika; Auliya, Eksis; Widyastuti, Dyah; Lubis, Adrian; Nugraha, Anggie; Muchlisoh, Siti
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.v2025i1.518

Abstract

Unemployment is a substantial obstacle to growth in Indonesia, affecting both socialand economic stability. The Unemployment Rate is a crucial metric that quantifies the proportionof the labor force actively pursuing work opportunities. The unemployment rate serves as acritical indicator of labor market imbalances, essential for labor policy formulation andassessment. Nonetheless, unemployment data has limitations, particularly at the micro-level,owing to sample constraints. Small Area Estimation (SAE) can address these constraints. Thisstudy estimates the unemployment rate at the sub-district level in West Java province for 2024utilizing the Hierarchical Bayes Beta methodology and clustering techniques. The modelingresults indicate that most sub-districts exhibit a low to medium unemployment rate, however 21locations demonstrate a very high unemployment rate, ranging from 23.00 percent to 48.06percent.
Forecasting Indonesian Monthly Rice Prices at Milling Level Using Google Trends and Official Statistics Data Swardanasuta, I Bagus Putu; Sofa, Wahyuni Andriana; Muchlisoh, Siti; Wijayanto, Arie Wahyu
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.v2025i1.521

Abstract

Hunger is a very complex social issue to address. Alleviating hunger is closely related to achieving food security, which is a goal in realizing the second Sustainable Development Goals (SDGs), zero hunger. The most frequently consumed food commodity by the Indonesian population is rice, which has fluctuating prices in the market. Therefore, price forecasting is necessary so that the government can take preventive measures against rice price increases at certain times. Research on rice price forecasting using big data from Google Trends is still very rare in Indonesia, even though Google Trends has great potential to reflect the public's search popularity for certain keywords. Therefore, this study aims to forecast the monthly medium rice price in Indonesia at the milling level using exogenous variables of dried milled grain prices and the popularity index of related keywords on Google Trends. The forecasting is conducted using Seasonal Autoregressive Integrated Moving Average (SARIMA), SARIMA with Exogenous Variables (SARIMAX), and Extreme Gradient Boosting (XGBoost) models. The SARIMAX model has the best performance in forecasting rice prices, with a Root Mean Squared Error (RMSE) of 941.6933, Mean Absolute Error (MAE) of 817.9021, and Mean Absolute Percentage Error (MAPE) of 0.0620.
Impact of the Family Hope Program (PKH) on Household Expenditure in East Java, 2024 Nurdiana, Elvika Nanda; Monika, Anugerah Karta
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.v2025i1.522

Abstract

Poverty remains a development challenge in Indonesia, particularly in East Java, which contributes substantially to the national poverty rate. Household expenditure, which reflects a household’s ability to meet basic needs and maintain living standards, is widely used as a proxy for welfare and poverty. Assessing how social assistance programs influence expenditure is therefore crucial to understand their impact in improving welfare. The Family Hope Program (Program Keluarga Harapan/PKH), a conditional cash transfer initiative, aims to improve household welfare and reduce poverty. This study describes the characteristics of PKH recipients and evaluates the program’s impact on household expenditure as an indicator of welfare in East Java. This analysis uses data from the March 2024 Susenas survey on households that meet the PKH criteria, with separate analyses by household poverty levels. The Propensity Score Matching method was used to address selection bias resulting from non-random recipient selection. The results show that PKH recipients generally face limitations in housing, basic access, and socio-economic conditions. Overall, PKH has not increased total expenditures, but there has been an increase in food expenditures among extremely-poor households. Policy adjustments are needed to better align with the needs and characteristics of each group.
Spatial Determinants of CO2 Emissions on Java Island: STIRPAT Framework and SAR Model Habibullah, M. Hafidz; Cotva, Bunga Musva; Putra, Hafidh Rean; Sari, Agustin Kurnia; 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.v2025i1.525

Abstract

Java Island, Indonesia’s economic and population hub, faces intense environmental pressure from CO2 concentration, exhibiting strong spatial dependence across its 118 regencies and cities. This study examines the determinants of CO2 concentration and their spillover effects using an extended STIRPAT framework and a Spatial Autoregressive (SAR) model, applied to 2024 secondary data from BPS-Statistics Indonesia and Google Earth Engine (GEE). The SAR model outperforms OLS, with lower AIC (364.8979 vs. 489.0563) and BIC (387.0634 vs. 508.4551), confirming spatial effects. In SAR models, interpretation relies on decomposing estimated coefficients into direct effects (impacts within a region) and indirect or spillover effects (impacts transmitted to neighboring regions), allowing a more nuanced understanding of spatial influence. Population density and manufacturing sector GRDP increase emissions, while NDVI and HDI reduce them. Population density and manufacturing sector GRDP increase concentration, while NDVI and HDI reduce them. Notably, indirect (spillover) effects consistently surpass direct effects, driven by commuter flows in urban hubs like Jabodetabek and industrial pollution spillovers. These findings inform regional climate strategies, emphasizing cross-regency reforestation and emission controls to support Indonesia’s Enhanced Nationally Determined Contribution (ENDC) goals.
Correlation Analysis of Land Surface Temperature (LST) and Vegetation Density Using Landsat 8 and 5 Imagery in Purwakarta Regency Ainulmila, Aida; Tiana, S; N Mumtaz, K; S F Azhari, D; Ibrahim, F; S Anggraini, T
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.v2025i1.528

Abstract

Urbanization and industrial development in urban areas have led to a decrease in vegetation and an increase in land surface temperature. This phenomenon impacts microclimate change and environmental quality, as seen in Purwakarta Regency. The conversion of vegetated land into industrial and residential areas reduces the vegetation index. This vegetation index can be measured using the Normalized Difference Vegetation Index (NDVI) method. Meanwhile, monitoring the increase in surface temperature can be calculated using the Land Surface Temperature (LST) method, which can indicate physical changes on the Earth's surface. The purpose of this study is to analyze the relationship between vegetation density and the increase in surface temperature using remote sensing and Geographic Information System (GIS) methods. The analysis results show that vegetated land area decreased significantly from 67,564.8 ha (2004) to 44,970 ha (2024), while built-up land increased threefold. In the same period, the average surface temperature increased from 37.31°C to 40.41°C. The correlation analysis shows a strong positive correlation between the decrease in NDVI and the increase in LST, with a correlation coefficient of 0.707 in 2024.
Optimized Feature Engineering for Transaction Fraud Detection Using Sequential and HMM-Based Features Wai Thar, Kaung; Thinn Wai, Thinn
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.v2025i1.529

Abstract

Fraud detection in financial transactions remains a major challenge because fraudulent activities are extremely rare—often described as finding a “needle in a haystack”— and must be detected in real time. This study presents a hybrid feature engineering framework that integrates lightweight sequential indicators with Hidden Markov Model (HMM)-based behavioural features to improve accuracy and interpretability. Using the PaySim dataset containing 2.77 million transactions (0.2965% fraud), we extracted 22 sequential and 14 HMMbased features, from which 28 highly discriminative variables were retained. To address class imbalance, a batch-wise SMOTETomek approach was applied, expanding 1.94 million clean samples to 3.86 million balanced samples. Experimental results show that HMM-based features alone yield moderate performance (ROC AUC = 0.778, F2 = 0.051), but the combined ensemble of tuned XGBoost and LightGBM achieves superior accuracy (ROC AUC = 0.9983, F2 = 0.8431, MCC = 0.827). SHAP analysis identifies HMM-derived entropy and state likelihoods, together with transaction amount dynamics, as key predictors. The results demonstrate that optimized feature engineering plays a crucial role in achieving accurate, scalable, and interpretable fraud detection.

Page 18 of 26 | Total Record : 251

 16   17   18   19   20 

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