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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 151 Documents
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SMOTE and Nearmiss Methods for Disease Classification with Unbalanced Data : Case Study: IFLS 5 Anas Rulloh Budi Alamsyah; Salsabila Rahma Anisa; Nadira Sri Belinda; Adi Setiawan
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.240

Abstract

Unbalanced data are often encountered in practice. They complicate the search for a model suitable for classification. This is because the number of individuals who have a history of a disease is less than the number of individuals who do not. We analyse the IFLS 5 data on medical history of a set of patients. We split the dataset in the proportion 80:20 to training and test subsets. Of course, both datasets are unbalanced, with only a small minority of patients who had a stroke. We apply the SMOTE and Nearmiss methods and evaluate the rate of correct classification. After being treated using the two methods, the training data was transformed into balanced data. The classification process is carried out to test the comparison of the effectiveness of the two methods in solving the problem of unbalanced data. Based on the results obtained, it can be concluded that the Nearmiss method is better than SMOTE in balancing the data. It was obtained by comparing several measures such as accuracy, F-score, Kappa, sensitivity, and specificity on the SMOTE and Nearmiss methods.
Determinants of Unmet Need Family Planning Among Married Woman of Reproductive Age in North Sumatra (Susenas March 2019) Aprillia Anis Saputri; Rini Rahani
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.241

Abstract

Unmet need is one of the obstacles of the family planning programs that can reduce contraceptive prevalence. The percentage of total unmet need in North Sumatra Province is 12.1 and comparable to the total national unmet need in 2019. This study aims to determine the factors that influence family planning needs and the tendency of married women of reproductive age in North Sumatra Province in 2019 with multinomial logistic regression. The data used is sourced from the Susenas KOR 2019. Results show that married women of reproductive age having a greater tendency to experience the unmet need for limiting are characterize as 35-49 years old, living in urban areas, and with junior high/equivalent levels. Meanwhile, the characteristics of married women of reproductive age (WUS) who have a greater tendency to experience the unmet need for spacing such as aged 15-24 years, Age at First Marriage more than 18 years, and with a higher education level. Therefore, a more optimal commitment and support from family planning field workers in family planning counselling are needed and increase equitable access and quality of family planning services.
Satu Data Indonesia in Sectoral Statistics: Concept of Satu Data Metadata Framework (SDMF) Hakiki Sandhika Raja; Chaidir Arsyan Adlan
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.243

Abstract

Satu Data Indonesia is a policy contrive to encourage the problem of inadequate data governance in Indonesia. This policy makes 4 main principles, namely metadata, data standards, reference codes, and interoperability as metrics of success in its implementation. In this study, we analyze the satu data Indonesia implementation in Kutai Timur Regency. We found that the integration of the satu data principle is challenging to apply technically because sectoral data in Indonesia has 2 characteristics based on the preparation of the list of data needs, namely the centralized data list, and the decentralized data list. Decentralized data list is a list of data that is partially prepared in each agency without any coordination with other stakeholders for completing the satu data principles. To accommodating this condition, we design the Satu Data Metadata Framework (SDMF) a data standard framework that is in accordance with the conditions of data governance in Indonesia. SDMF utilizes contextual layer and discovery layer of metadata to provide temporal attribute called Satu Data Resource Identifier (SDRI) for integration purpose
Analysis of Rice Field Cluster in Indonesia as an Evaluation of Food Production Availability Using Fuzzy C-Means Heru Setiono; Totok M Dianto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.245

Abstract

Rice fields area in Indonesia is getting narrower every year with the rampant construction of housing and buildings. It results in lower availability of food production hence to meet the needs we have to import rice from other countries. By clustering rice fields, it can be used as an evaluation material to increase food production in Indonesia so that the need for rice imports can be minimized. The method used in the grouping of Rice Fields is the Fuzzy C-means method, implementation of the Knime Tool with data training and testing. The Fuzzy C-Means program produces three data groups/clusters, namely wide, moderate, and narrow rice fields. The results of the clustering show that the most potential areas for food production from rice fields are East Java, Central Java, and West Java.
Big Data for Small Area Estimation: Happiness Index with Twitter Data Sheerin Dahwan Aziz; Azka Ubaidillah
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.248

Abstract

Data availability for small area level is one of the keys to the success of regional development. However, direct estimation of small areas can produce high error due to inadequate sample sizes so the estimation is not reliable. One of alternative solution to this problem is to use the Small Area Estimation (SAE) method which can improve precision by "borrows strength" of the corresponding region information or auxiliary variable information that is strongly related to the response variable. This study uses two SAE models, namely SAE EBLUP Fay-Herriot model with auxiliary variables Podes data and SAE with Error Measurement with auxiliary variable Twitter data. Estimation results using the SAE method are better than direct estimates. This is shown by the RSE value which produced from SAE method, both the EBLUP model and Measurement Error, is smaller than the direct estimate. Therefore, big data can be used as an alternative variable in the SAE model because the data is available in real-time, covers up to the smallest area, and relatively low cost.
Comparing Voluntary and Involuntary Part Time Female Workers in Maluku Muhamad Bagus Adji Briliyanto; Titik Harsanti
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.249

Abstract

Maluku Province has the third highest average length of schooling (RLS) for women nationally, but the rate of female workers with below normal working hours (part-time workers) is quite high. This study aims to determine the general description of married women age 15-49 years as part-time worker in Maluku and the determinants, also their tendency based on the significant variables using data from the National Labor Force Survey (Sakernas) August 2019. The analytical method used is multinomial logistic regression. The results of the study indicate the variables that significantly affect the part-time worker status of married women of reproductive age are employment status, income, and business field. The status of involuntary part-time worker (underemployed) significantly affected by age, work sector, disability, and the presence of toddlers. The status of voluntary part-time workers significantly affected by regional classification and education. The tendency to become underemployed is highest among those who have incomes below the minimum wage, work in agricultural sector, and work in informal sector. Meanwhile, the tendency to become voluntary part-time workers is highest among those who have incomes below the minimum wage, and work in the agricultural sector. So, policy makers must ensure married women get a decent paid job.
Analysis of Input-Output Table: Identifying Leading Sectors in Indonesia (Case Study in 2010, 2016 and 2020) Yoga Dwi Nugroho
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 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.v2021i1.251

Abstract

According to Neoclassical theory every country has to maximize their own resources include labor, natural resources also physical resources for developing their economy. Sector-based economic development must be carried out using comprehensive economic indicators, not only looking at the economic structure but also being able to identify and analyze inter-industry relationships. One of the right indicators is through the analysis of the Input-Output Table. The I-O table used is this research are I-O Table 2010, 2016, and 2020 estimated. In this comprehensive analysis, the Forward and Backward Linkage Indexes were calculated so that the sectors that are included in the Leading sector can be identified. In addition, a good multiplier analysis is carried out include output, income, labor, and value-added multiplier to see the amount of output, income, labor, and value-added changes caused by the changes of final demand. The results of the research show that sector that is included as a lever sector is the manufacturing sector (sector 3) and the procurement of electricity and gas (sector 4). Sector 3 is the most potential sector as leading sector due to some reasons this sector has a large output, added value and input structure, and has a high multiplier for four types of multipliers and analysis of Forward linkages and Backward Linkage Indexes shows this sector has high value. Manufacturing industry is a strong leading sector, from this the recommendation is the government can increase output of the manufacturing industry by give subsidy or decrease the tax or government can decrease the price of another sector that be the intermediate sector for manufacturing industry by giving subsidy.
Nowcasting of Chili Pepper (Capsicum frutescens L.) Prices in East Java Province Using Multi-Layer Perceptron Method Mohamad Choirul Zamzami; Nucke Widowati Kusumo Projo
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 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.v2023i1.274

Abstract

The aims of study is to predict the price of chili pepper at the provincial level in East Java by looking for the best input variable from three types of input variables, price of chili pepper at the regency and city levels, natural factors, and word search index on Google Trends as an approach to the causes of chili pepper price fluctuations. The Multi-Layer Perceptron method, accompanied by a search for the best combination of model parameters is selected to get the model with the best nowcasting ability. The result shows that the best model for nowcasting is characterized by: the input variable is price of chili pepper at the regency and city levels with three hidden layers and 32, 45, and 51 neurons in each hidden layer, maximum iteration is 200 iterations, maximum iteration when the model not increase in performance for applying early stopping is 20 iterations, non-linear activation used is RELU (Rectified Linear Unit), and optimization function used is ADAM optimizer. The accuracy of nowcasting in this study is highly accurated with MAPE smaller than 10%.
SPATIAL ANALYSIS OF FIRE OCCURRENCE IN JAKARTA, INDONESIA Ika Rosantiningsih; Chotib Chotib
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 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.v2023i1.275

Abstract

The occurrence of fire incidents in urban villages of Jakarta Special Capital Region significantly impacted losses, necessitating prevention and handling efforts. Therefore, this study aims to analyze the spatial influence of social and physical variables (independent variables) such as sex ratio, vulnerable age population, number of buildings, and size of slum areas on fires (dependent variable) in Jakarta Special Capital Region. The analysis area includes five municipalities of Jakarta Special Capital Region. Secondary data were obtained from Central Agency of Statistics of Jakarta Special Capital Region, maps from the official site jakartasatu.jakarta.go.id, and publication data from Government of Jakarta Special Capital Region for 2020. Furthermore, the quantitative approach in descriptive and inferential analysis, determined using Microsoft Excel and GeoDa version 1.20.0.10, was used to evaluate the spatial relationships between adjacent sub-districts. Although the regression data processing results using GeoDa were significant, the spatial regression results with Lagrange Multiplier (LM) Lag and Lagrange Multiplier (LM) error > 0.05 were insignificant and significant when using the parameter 0.1. This means fire symptoms in Jakarta Special Capital Region do not have a spatial effect, contrary to the clustering observed between dependent and independent variables using Morans'I and Scatter Plots. The results of this study can aid the Jakarta provincial government in preventing and handling potential fires by restructuring slum areas to minimize the likelihood of such incidents.
RegTech Solutions: Generic Business Process Analysis and Modeling Benny Firmansyah; Arry Akhmad Arman; Widya Sri Wahyuni
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 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.v2023i1.276

Abstract

Regulatory Technology, often known as RegTech, is an innovative strategy developed in the finance industry to streamline the regulatory compliance process. RegTech takes advantage of various new technologies such as artificial intelligence (AI), machine learning (ML), big data analytics (BD), cloud computing (CC), robotic process automation (RPA), and various other new technologies. RegTech can be applied to other industries where compliance with and oversight by rules are necessary. To support this, one way to be done is to analyze and model business processes for RegTech solutions in a generic style so that they can be adopted by various other fields in executing these solutions. In this research, a generic business process analysis of RegTech solutions was carried out through a literature study related to the use of RegTech in the financial industry. Next, modeling the analysis results using Business Process Model and Notation (BPMN) with the support of the Bizagi application. The modeling results are then tested for validity and applied to a logical scenario related to non-financial regulatory compliance. The test results show that the business process modeling results are valid and can be used as a reference in implementing RegTech solutions outside the financial sector.

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