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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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Unlocking the Potential of Input-Output Tables for Spatial Analysis Using the Miyazawa Model: A Case Study of East Java Province Murjani, Ahmadi; Wiratama, Budhi Fatanza
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.v2025i1.530

Abstract

East Java Province holds a strategic role in the national economy, serving as the second-largest contributor to GDP after Jakarta and as a key trade hub to Eastern Indonesia. Yet regional disparities remain substantial, particularly reflected in the economic underdevelopment and weak logistics connectivity of Madura Island, which lies adjacent to the Gerbangkertosusila growth corridor. Addressing this gap requires a deeper understanding of sectoral and spatial linkages that shape Madura’s growth trajectory. This study applies the Miyazawa Input-Output Model for East Java Province, integrating 17 economic sectors and 38 regencies/municipalities to enable simultaneous sectoral and regional analysis. The simulations assess the effects of increasing household income in Madura, spillover from surrounding regions, and the combined role of strengthening the Transportation and Warehousing sector alongside Agriculture and Manufacturing. The findings show that the logistics sector in Madura, when considered independently, has limited impact; however, its significance rises when complemented by productive local sectors. Moreover, spillover from surrounding regions into Madura proves weaker than spillover directed outside Madura, underscoring the island’s fragile spatial connectivity. These results highlight the urgency of affirmative policies that strengthen productive sectors, enhance interregional linkages, and ensure Madura’s integration into East Java’s broader economic development.
Data Collection for Nearest Public Facility Using Ball Tree Algorithm and Google Maps API Ramadhan, Handika
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.v2025i1.541

Abstract

Accessibility to public facilities is a crucial factor in regional development, includingat the village level as the smallest administrative unit. The Central Bureau of Statistics (BPS)currently collects data on public facilities and their distances to village offices throughinterviews, making the results dependent on respondents’ perceptions. This research aims tomeasure the nearest distance from village offices to public schools by utilizing the BallTreealgorithm and the Google Maps API. The dataset consists of 128 village offices and a list ofpublic schools classified into four categories. BallTree was used to filter the nearest schoolcandidates within a given radius, after which the route distance of the ten nearest candidates wascalculated using the Google Maps Distance Matrix API to identify the school with the nearestroute distance based on the road network. The findings show that straight-line distance oftenaligns with route distance, although not at all, highlighting the importance of Google Maps routecalculation. This research concludes that combining BallTree and the Google Maps APIimproves computational efficiency while providing objective and reliable information.
Investigating the Profile of Digital Readiness and Sustainability Development: An Explainable Clustering Pamuji, Agus; Susanty, Aries; Warsito, Budi
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.v2025i1.545

Abstract

The level of digital readiness within Islamic Higher Education Institutions (IHEIs) has emerged as a critical concern, drawing increasing scholarly and institutional attention over the past five years. This study aims to examine the empirical relationship between two key dimensions: digital readiness, as reflected by the National Readiness Index (NRI), and progress toward the Sustainable Development Goals (SDGs). Data were collected from more than 20 IHEIs between 2023 and 2024 to support a sequential analytical approach. Pearson’s correlation coefficient was employed to identify associations between NRI-based digital readiness and SDG performance within the IHEI context. Subsequently, cluster analysis was conducted using the Duda–Hart Index, while the Pseudo T² statistic was applied to validate the robustness of the clustering outcomes. A cartographic visualization was also generated to illustrate variations across readiness and sustainability clusters. The results indicate a considerable disparity between digital readiness and sustainability among IHEIs. Only a limited number of institutions demonstrate consistent performance in both areas, suggesting that effective leadership and strategic investment in digital infrastructure are essential prerequisites for achieving sustainable institutional transformation.
Comparative Study of Autoencoder and LSTM-AE for Extreme Temperature Anomaly Detection in Semarang Kusuma Wijaya, Galih; Anggraeni, Aliyya; Chulaili Sahri Nova, Tsalisa; Alifian yusuf, Muhammad; Kharisudin, Iqbal
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.v2025i1.549

Abstract

Climate change has increased the frequency and intensity of extreme weather events, including heatwaves and cold spells, posing critical risks to public health and urban infrastructure. This study proposes and compares two deep learning frameworks based on Autoencoders, namely the Long Short-Term Memory Autoencoder (LSTM-AE) and the standard Autoencoder (AE), for detecting extreme temperature anomalies using historical daily data from 2005 to 2025 in Semarang City. Unlike conventional anomaly detection methods, the LSTM-AE introduces temporal learning through recurrent memory cells, enabling it to capture sequential temperature dependencies that static AE models cannot. Both models are trained to reconstruct “normal” temperature patterns, with anomalies identified when reconstruction errors exceed the 95th percentile threshold. The results demonstrate that the LSTM-AE more consistently identifies significant heatwave and cold spell events, with seasonal alarm rates that closely align with local climatic transitions. Several detected peaks coincide with historically documented events such as the 2015–2019 El Niño and 2019–2020 transition periods reported by BMKG, confirming climatological relevance. In contrast, the standard AE detects a higher number of anomalies (726 vs 366 from the LSTM AE) but tends to generate false alarms outside transitional periods. Model performance is evaluated using reconstruction error distributions, Jaccard similarity indices, and monthly alarm rates. This study highlights the potential of LSTM-based architectures for improving anomaly detection in climate data and contributes to developing data-driven strategies for urban climate resilience in tropical regions.
Detecting Marine Debris Using Sentinel-2 Satellite Images: (Case Study: Kuta Beach, Bali) Faradinah Nasir, Fadiah; Kurniawan, Robert
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.v2025i1.552

Abstract

Plastic waste pollution in the oceans remains a global problem. Kuta Beach is one of Bali's tourist destinations that has been affected by plastic waste pollution. This is not in line with the 14th SDGs, which is to prevent and reduce marine debris pollution. However, the marine debris monitoring process carried out by the Ministry of Environment and Forestry requires officers to conduct direct monitoring in the field, which incurs higher costs. Therefore, satellite imagery can be an alternative option for more effective and efficient marine debris detection. This study aims to detect marine debris on Kuta Beach using machine learning algorithms, namely Random Forest (RF), XGBoost, and LightGBM. This study uses the Marine Debris Archive (MARIDA) dataset, which has marine debris labels, and Sentinel-2 images of Kuta Beach from 2019–2023. The LightGBM algorithm provided the best performance in detecting marine debris with an F1-score of 95.16%. The area detected as marine debris on Kuta Beach in 2019–2023 was 500 m2, 0 m2, 100 m2, 300 m2, and 400 m2, respectively. Based on these results, marine debris is generally detected around the coastline, particularly in the southern area of Kuta Beach, which is located near a shopping center.
The Impact of the Job Creation Law and Other Variables on Indonesia's FDI from 2018 to 2024 Sofiana, Apriani; Sohibien, Gama Putra Danu
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.v2025i1.555

Abstract

Although national Foreign Direct Investment (FDI) realization in Indonesia increased following the enactment of the Job Creation Law in 2021, regional FDI realization actually showed a decline in 17 of Indonesia's 34 provinces. Reviews from international organizations such as the World Bank and the World Trade Organization (WTO) suggest the need for analysis to examine the influence of investment-supporting variables on FDI in Indonesia, including the Job Creation Law policy. Therefore, the objective of this study is to analyze the variables influencing regional FDI realization in 34 provinces for the 2018-2024 period. The method used is panel data regression with the selected Random Effect Model (REM). The results show that the Household Consumption Expenditure (HCE) as a proxy for market size, non-oil and gas exports as a proxy for openness of market access, the mining sector's GRDP as a proxy for natural resource potential, and the Job Creation Law have a positive effect on regional FDI realization. These results align with eclectic dunning theory. Disparities in FDI realization were also found, regions outside Java Island that experienced high FDI realization were partly due to internal factors such as abundant natural resources, the presence of industrial areas, and product diversification.
Analysis of Factors Affecting Deforestation in Riau From 2001 To 2023 Using The ARDL Approach Sukajaya, I Wayan Divandra Maharesandya; Utami, Efri Diah
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.v2025i1.556

Abstract

Forests are one of the most important elements for human life. One of Indonesia'sproblems for decades has been high rates of deforestation. Riau is the province with the highesttotal deforestation in Indonesia in the last 23 years. The government has implemented variousmeasures to achieve both short-term and long-term targets related to reducing deforestation.Therefore, this study aims to analyze the variables suspected of influencing deforestation in theshort and long term using the Autoregressive Distributed Lag. The results of the study indicatethat the variables influencing deforestation in Riau Province in the short term are the GDP of theagriculture, forestry, and fisheries sectors and forest and land fires. In the long term, thesignificant influencing variables are the GDP of the agriculture, forestry, and fisheries sectors,the implementation of Law No. 18 of 2013, and the extent of forest and land fires. Based onthese findings, in the short term, the government is expected to transform the agricultural sectoreconomy toward a more sustainable direction and halt the clearing of forest areas for oil palmplantations, especially those conducted through forest burning. In the long term, the governmentshould further strengthen the implementation of the law.
Deciphering Student Academic Success: Bayesian Analytical Insights Kannan, V Suriya; Lakshmi, S; ., Reshmavathi
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.v2025i1.559

Abstract

This study delves into the factors influencing student’s academic achievement utilizing Bayesian mixed effect models. It presents five distinct models, each integrating various fixed variables such as gender, playing hours, stress level, and travelling hours, alongside random variables such as school level and type of school. These models are evaluated using the LeaveOne-Out Information Criterion (LOOIC) to gauge their adequacy in fitting the data and predicting outcomes. The findings unveil that the inclusion of additional factors, such as school characteristics and students' activities, modifies the relationship between gender and academic success, with gender exerting a diminishing influence as more variables are incorporated. Additionally, stress level and travelling hours emerge as noteworthy predictors of average marks. Among the models assessed, the one incorporating gender, playing hours, and stress level as fixed effects, alongside school level and type as random effects, demonstrates superior fit and predictive capability. This underscores the significance of considering both individual traits and contextual elements in comprehending academic performance.
The Individual and Contextual Factors of Precarious Employee Status of Youth Workers in Indonesia 2024: Application Multilevel Binary Logistic Regression Mandy, Arya Samuel; ., Sugiarto
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.v2025i1.561

Abstract

Human resources are a strategic component for countries in achieving developmentgoals and promoting progress. Among age groups, youth play an important role as drivers of acountry's development. However, the challenge of obtaining decent work is a serious problemthat causes many youth people in Indonesia to be forced into precarious employment. In the lastfour years, the Precarious Employment Rate (PER) of youth people in Indonesia in 2024 hasincreased dramatically compared to the previous year, even becoming the highest among all agegroups. This study aims to determine the general picture and analyze the individual andcontextual factors that influence the status of precarious employees among youth workers inIndonesia. The analysis method used is multilevel binary logistic regression. The results of thestudy show that 85.97 percent of youth workers in Indonesia have precarious employee status.The analysis shows that individual factors such as gender, marital status, education level,participation in training, regional classification, employment sector, labor union membership,and contextual factors such as the provincial minimum wage have a significant effect on theprecarious employee status of youth workers in Indonesia in 2024.
Real-Time Vibration Fault Detection in Rotating Machines Using Transformers to Minimize Production Losses in Industry 5.0: VIBT TOUKAP NONO, FERNAND JOSEPH; TOKOUE NGATCHA , DIANORE; OFFOLE, Florence; Nyatte, Steyve; MOUZONG PEMI, Marcelin
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.v2025i1.566

Abstract

Quickly identifying anomalies in rotating machinery is crucial for safety and profitability in contemporary industry (Industry 5.0). Unidentified failures can cause costly malfunctions and production interruptions. This research proposes an innovative strategy based on Transformer for the analysis of multidimensional vibration events (VIBT), with a view to early and accurate detection of anomalies in rotating machinery. The goal is to minimize production interruptions in Industry 5.0. The study highlights the limitations of conventional vibration analysis approaches and traditional deep learning techniques, emphasizing the need for innovative solutions. VIBT incorporates transformers and a filter bank convolution (FBC) module for effective denoising, as well as an adaptive wavelet transformation (WTA) mechanism for dynamic feature fusion at various scales, thereby addressing the challenges posed by non-stationary and noisy signals. Extensive testing on the Mafaulda dataset reveals that VIBT achieves 98.1% precision and 98.8% accuracy, significantly outperforming existing standard models. The results suggest that VIBT not only improves fault detection capabilities but also optimizes maintenance strategies in industrial applications, paving the way for future research on semi-supervised learning based on transformers and the integration of intermodal data.

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