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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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Impact of Land Use Changes Due to Tourism on Ecosystem Services Using InVEST: Case Study: Badung Regency, Bali Alfandi, Atanasius; Transver Wijaya, Yuliagnis
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.v2025i1.607

Abstract

Ecosystem services play a vital role in supporting human life and environmental sustainability. However, tourism activities in Badung Regency, Bali, have led to significant changes in land cover and use, impacting the function of ecosystem services. This study integrates remote sensing, machine learning, and InVEST technology to understand the impact of Land Use/Land Cover (LULC) changes on ecosystem services in Badung Regency. The results show a decrease in non agricultural vegetation area from 17659.65 hectares in 2014 to 11405.84 hectares in 2024. Meanwhile, built-up land experienced a drastic increase from 15074.47 hectares in 2014 to 22134.06 hectares in 2024. In addition, the InVEST model shows a decrease in carbon stock by 1379,841.68 tons in the period 2014 to 2024. Meanwhile, water yield, nitrogen export, and sediment export increased, reflecting a relationship between tourism development and the decline in ecosystem services. Correlation analysis shows a consistent negative correlation between water yield and carbon stock, as well as a positive correlation between nitrogen export and sediment export. The results of this study are expected to serve as a reference for further studies on the dynamics of ecosystem services and support sustainable environmental management efforts in areas with rapidly growing tourism activity.
Enhanced EV Battery Degradation Modeling in Tropical Environments via CVAE-GRU for Sustainable Transportation LOTCHOUANG FUSTE, Hervé; Marius, Kibong; Steyve, Nyatte; Emmanuel, Sapnken; Edwige, Mewoli; Gaston, Tamba
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.v2025i1.610

Abstract

Electric Vehicle (EV) battery degradation in tropical environments remains poorly understood, with traditional linear models like OLS facing significant challenges such as multicollinearity, leading to unreliable insights into influential factors. This study aims to experimentally characterize lithium-ion battery degradation and comprehensively evaluate the influence of local climatic (temperature, humidity, dust) and driving conditions (road quality, mileage) in a Cameroonian tropical context, addressing the limitations of conventional statistical approaches. Our unique contribution involves providing empirical real-world data from a subSaharan environment and applying a novel hybrid CVAE-GRU methodology to capture complex non-linear and temporal dependencies. An embedded system continuously collected battery parameters (SoH, internal resistance) alongside environmental and driving data. The CVAE learns robust latent representations from these correlated inputs, while the GRU models their temporal dynamics for degradation prediction. Results confirm progressive SoH degradation, significantly accelerated by high temperatures, humidity, dust, and poor road quality. The CVAE-GRU approach effectively mitigates multicollinearity, offering superior accuracy and deeper insights into these influences. This work highlights the critical impact of tropical conditions on EV battery aging, providing crucial findings for developing adapted Battery Management Systems and fostering sustainable mobility in similar regions.
Applied Bayesian Analysis of Intergenerational Fingerprint Pattern Similarity NK, Aswini; SHUKLA, RUDRANK; M C , Janaki
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.v2025i1.611

Abstract

This research reports on the inheritance of fingerprint types across three generations of families. Uses of Bayesian measures of statistical analysis indicates a moderate transference of loops and whorls between generations (grandfather, father, son), with negligible transference for arches and only joint moderate evidence across all three generations. A total of 150 samples from 50 family trios were analyzed, classified fingerprints as Arch, Ulnar/Radial Loop, Composite, and Whorl. Cross-tabulation showed the highest transference in Ulnar/Radial Loops, followed by Whorls, with minimal transference for Arches and Composites. The Bayesian correlation analysis of father & grandfather and son & father showed strong similarities between generations (father & grandfather - Pearson r = 0.283, BF?? = 44.74; Kendall’s ?B = 0.255, BF?? = 4650.48) and substantial evidence for the association between sons and fathers. The analysis showed negligible transference between sons and grandfathers. Bayesian regression and model comparisons supported the null model, with very low R² values (0.003–0.012), indicating minimal predictive influence of parental patterns on the son’s fingerprint phenotype. Overall, the findings indicate moderate hereditary continuity of fingerprint patterns between successive generations, but weak evidence for transmission across all three generations. This suggests that fingerprint inheritance is complex, influenced by both genetic and developmental-environmental factors affecting dermatoglyphic patterns.
Machine Learning Framework for Early Detection of Mental Health Conditions from Textual Data Riskhan, Basheer; Hadi, Abdullah Al; Saky, S M Asiful Islam; Arefin, Md Saiful; Hussain, Khalid
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.v2025i1.613

Abstract

Mental health disorders significantly affect global populations, placing heavy burdens on healthcare systems worldwide. Traditional diagnostic methods, mainly clinical assessments and self-reports, lack real-time monitoring, are prone to biases, and often result in delayed interventions. Recent advancements in machine learning (ML) offer promising opportunities to enhance mental health detection through behavioural and physiological data analysis. This study evaluates four widely used machine learning algorithms—Support Vector Machines (SVM), Logistic Regression, Naïve Bayes, and Random Forests—in identifying early indicators of mental health conditions from textual data. A dataset of 27,978 textual records from the “Analysis and Modelling on Mental Health Corpus” was analysed. Data preprocessing involved normalization, stop word removal, lemmatization, and TF–IDF vectorization to prepare robust features for model training. Model performance was assessed using accuracy, precision, recall, and F1-score metrics. Results showed that SVM and Logistic Regression outperformed other models, achieving accuracy rates of 92% and 91%. These findings demonstrate the potential of ML-based frameworks to support earlier and more accurate mental health interventions. Integrating such techniques into clinical practice can improve diagnostic accuracy, reduce healthcare workload, and enhance patient outcomes.
From Noisy Data to Insight: SOM Filtering Implementation For Improving the Machine Learning Model Firmansyah, 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.v2025i1.614

Abstract

The filtering of representative training data from Big Data are critical steps in developing machine learning models, particularly for official statistics. This study demonstrates the application of Self-Organizing Map (SOM) filtering for enhancing training data quality in remote sensing-based classification of paddy phenological stages using satellite data. By clustering the data, SOM identifies and filters representative samples, which further removing noise and irrelevancy. Following the filtering, comparison is conducted between several purity threshold scheme and non-filtering dataset during model development. Findings reveal that increasing the purity threshold consistently improves classification performance and accuracy respectively, as filtering becomes stricter. The results demonstrate SOM filtering as an effective strategy for improving the representativeness and reliability of training datasets in remote sensing applications, while emphasizing the trade-offs when optimizing machine learning model robustness and generalizability.
Predicting Bronchopulmonary Dysplasia in Infants: A Comparative Evaluation of Probit and Machine Learning Models Umar Madaki, Shazali; Bello Muhammad , Abba; Ahmad Hamisu , Hamisu
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.v2025i1.617

Abstract

This study compares the predictive performance of traditional Probit regression and several machine learning models in predicting Bronchopulmonary Dysplasia (BPD) among preterm infants. The models were evaluated using standard performance metrics, including accuracy, precision, specificity, sensitivity, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Among all models, the Random Forest demonstrated superior predictive performance with the highest accuracy (86.36%), precision (85.71%), specificity (87.50%), sensitivity (85.71%), F1-score (0.8571), and AUC (0.92), indicating a strong discriminative ability. Birth weight and postnatal weight at four weeks emerged as the most significant predictors of BPD. The findings suggest that machine learning approaches, particularly the Random Forest algorithm, provide a more robust predictive framework than the conventional Probit regression model for early detection of BPD risk in preterm infants.
A Hybrid Method for Standardising Civil Registration and Vital Statistics (CRVS) Location Data Sandyawan, Ignatius; Rimawati, Yeni; Rismansyah, Ari
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.v2025i1.618

Abstract

 Civil Registration and Vital Statistics (CRVS) systems in archipelagic contexts likeIndonesia face persistent challenges in location data standardisation due to free-text entries thatvary in spelling, formatting, and granularity. This study introduces a multi-stage hybridframework that systematically converts these unstructured entries into official administrativecodes using deterministic matching, fuzzy probabilistic matching, and geocoding. This studyprocessed 841,126 birth and death records using Python (Pandas, RapidFuzz, Geopy).Cumulatively, all stages achieved a combined match rate of 85.44% for births and 67.12% fordeaths. The layered pipeline ensured speed, precision, and coverage for real-world CRVS data.The findings demonstrate enhanced geographic precision in vital statistics, enabling morereliable public health and demographic applications. Future improvements may includetransformer-based embeddings, active learning for ambiguous records, and uncertainty-awaregeocoding techniques. This framework establishes a scalable, robust pathway for elevating thegranularity and reliability of geolocated vital event data.
Identifying Stratifications of Cancer Patient Visits: Approach of Clustering Using PCA of Mixed Data Yunitaningtyas, Kristiana; Herianti, Herianti
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.v2025i1.622

Abstract

Cancer is a significant contributor to the burden of non-communicable diseases and one of the diseases with the highest costs in Indonesia’s health insurance system. Understanding key factors influencing cancer patient visits and risk groups under national health insurance supports evidence-based and sustainable cancer care financing. The aim is to identify key factors influencing inpatient visits among cancer survivors and map risk patterns to improve cancer health service policies, using a 1% sample of claim data from the national health insurance (JKN) program. The PCA of mixed data analysis revealed that cost-severity level and contributionward classes shared influence of the visits. After PCA, K-Means was applied and 4 clusters were obtained. K-Means can give better understanding of the patient visits, especially the need for distinct strategies to be implemented for the groups so that the burden of cancer disease financing under the national health insurance program can be reduced.
Did the Digital Push Last? E-Commerce and Rural Agricultural Earnings in Indonesia During and After COVID-19, Evidence from Sakernas Ruslan, Kadir; Sukma, Weni Lidya
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.v2025i1.623

Abstract

This paper examines the impact of e-commerce adoption on earnings and incomedistribution among rural agricultural employers in Indonesia, both during and after the COVID19 pandemic. Using microdata from the National Labour Force Survey/Sakernas (2018–2024)and applying probit, OLS, Propensity Score Matching, and quantile regression models, weidentify the determinants of adoption and its impact on earnings. Adoption was strongly drivenby education, training, and enterprise characteristics, while older age and reliance on unpaidhousehold labor constrained uptake. Results show that e-commerce adopters earned substantiallyhigher than non-adopters (more than 30 percent) both during and after the pandemic, confirmingsustained income gains beyond the crisis. Quantile regressions reveal that the lowest-incomeemployers benefited most, with earnings gains exceeding 50 percent at the bottom quantileduring the pandemic. Although relative advantages shifted toward higher earners after thepandemic, large and significant effects remained for the lowest-income groups. These findingsindicate that e-commerce not only enhances market access but also contributes to improvingincome distribution. Policy interventions to strengthen digital literacy, rural infrastructure, andfinancial access are essential to preserve its inclusive role and ensure that vulnerable agriculturalemployers continue to benefit disproportionately.
Equipment Borrowing and Room Booking Information System at the Politeknik Statistika STIS Hadi Nugroho, Setya; 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.v2025i1.624

Abstract

The management of goods and space lending services at the Politeknik Statistika STIS is currently still done manually, resulting in various operational constraints such as limited access to information, inefficient processes, and potential errors in recording. This impacts the quality of service and the effectiveness of campus asset utilization. This study aims to design and build a website-based goods and space lending information system to address these issues. The system developers aimed to provide users with access to information on goods and space availability, simplify the loan application process, and improve the accuracy of inventory data. The system was developed using the SDLC method with a prototyping approach, while The researchers carried out the evaluation process using Black Box Testing and a PSSUQ survey survey to measure ease of use and user satisfaction. The developers successfully built the system and confirmed through Black Box Testing that all features operate correctly, and the PSSUQ evaluation shows an average score of 1.69, indicating that this system is well received and provides a high level of satisfaction for users.

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