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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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Cost-Sensitive Boosting Algorithm for Classifying Underdeveloped Regions in Indonesia Bayu Suseno; Bagus Sartono; Khairil Anwar Notodiputro
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.v2023i1.373

Abstract

Imbalanced classes are indicated by having more instances of some classes than others. The cost-sensitive boosting algorithm is a modification of the AdaBoost algorithm, which aims to solve the problem of imbalanced classes. In this study, we evaluate the cost-sensitive Boosting algorithm AdaC2 using Indonesia's underdeveloped region's data. This study confirms that the cost-sensitive boosting algorithm (AdaC2) performs better in classifying the instances in the minority classes than standard classifiers algorithms.
Analysis of Spotify's Audio Features Trends using Time Series Decomposition and Vector Autoregressive (VAR) Model Daffa Adra Ghifari Machmudin; Mila Novita; Gianinna Ardaneswari
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.v2023i1.375

Abstract

Streaming is the most popular music consumption method of the current times. As the biggest streaming platform based on subscriber number, Spotify stores miscellaneous information regarding the music in the platform, including audio features. Spotify’s audio features are descriptions of songs features in form of variables such as danceability, duration, and tempo. These features are accessible via Application Programming Interface (API). On the other hand, Spotify also publishes their own charts consisting of 200 most streamed songs on the platform (based on regions) which are updated daily. By combining Spotify’s song charts and the songs’ respective audio features, this research conducted analysis on musical trends using time series modelling. First, the combined data is decomposed to extract the trend features. Second, a Vector Autoregressive (VAR) model is built and followed by forecasting of the audio features. Lastly, the performance of forecasted values and the actual observations is evaluated. As a result, this research has proven that musical trends can be forecasted in the future for a short period by using VAR model with relatively low error.
Interest Rate Transmission on Indonesia’s Monetary Policy Analysis: Case of Banking Interest Rate Adin Nugroho; Prientananda Ghina Salsabila
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.v2023i1.378

Abstract

Indonesia's economic stability should be achieved by implementing monetary and fiscal policies, for instance, setting the interest rate by Bank Indonesia (BI) as policy rate of central bank, which should be followed by other banking institutions. Unfortunately, this interest rate regulation by BI had not been able to achieve the goal of restoring economic stability since it always had long time lag. This happened because the policy of increasing interest rates had not been followed up spontaneously by other banking institutions. In fact, time lag might cause disadvantages such as long-lasting high inflation, increased poverty, and severe economy vulnerability. This research was conducted to analyze the time lag of the transmission of Bank Indonesia's interest rate monetary policy and the response of banking institutions in Indonesia. The method used in this study was survival analysis. The results indicated that the time lag of monetary policy transmission using the interest rate in Indonesia needed to be improved to double adjustment speed to reach the optimal point. The response of banking institutions could be improved because there was still asymmetry response in all aspects including types of interest rates, allocations, and change direction. Meanwhile, from the aspect of ownership, both state-owned and private-owned banks had shown in line response of time lag performance.
Comparison Of Kernel Support Vector Machine In Stroke Risk Classification (Case Study:IFLS data) Lensa Rosdiana Safitri; Nur Chamidah; Toha Saifudin; Gaos Tipki Alpandi
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.v2023i1.381

Abstract

Stroke s a disability main source and main disability source to lost years of disability-adjusted life. Currently the information technology development, especially the field of machine learning has an important role in early warning of various diseases, such as strokes. One of the methods used for stroke classifying is Support Vector Machine (SVM). In this study, we aim to compare several kernel functions in SVM such as linear, radial basis function(RBF), polynomial, and sigmoid for classifying stroke risk. We determine the best kernel based on accuracy, sensitivity, and specificity values. The result of this study shows that linear kernel function gives the best performance in classifying with values of classification accuracy 99.0%, specificity 100.0%, ,and sensitivity 97.0%. Those scores are the highest scores among the other kernel , that means the linear kernel function is the best method for classifying strokes risk.
Implementation of Machine Learning and Its Interpretation for Mapping Social Welfare Policy in Indonesia Aldo Leofiro Irfiansyah; Ari Rismansyah; Novia Permatasari; Isnaeni Noviyanti; Atqo Mardiyanto; Ade Koswara
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.v2023i1.383

Abstract

This research leverages data from the 2022 Early Socio-Economic Registration (Regsosek) activity to develop a machine learning model capable of predicting family expenditure levels based on the Proxy Mean Test (PMT) with high accuracy. By integrating the SHAP (SHapley Additive exPlanations) method for model interpretation, we identify the contributions of socio-economic features to expenditure predictions and link them to relevant social assistance programs. We compare two regions, Kulonprogo Regency and Yogyakarta City, representing varying poverty levels, and identify unique characteristics influencing family welfare in each area. The results highlight that effective policy interventions must be tailored to the unique characteristics of each region and family, taking into account dimensions such as housing, education, income, and community expenditures. This research provides valuable insights for policymakers, demonstrating that successful poverty alleviation policies are data-driven and adaptable to the diverse socio-economic realities across regions.
Comparative Analysis of Retriever and Reader for Open Domain Questions Answering on BPS Knowledge in Indonesian Sulisetyo Puji Widodo
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.v2023i1.384

Abstract

Enumerators from Badan Pusat Statistik (BPS) still often encounter problems in finding solutions to cases encountered during censuses or surveys. Even though knowledge lists have been created and collected in various systems such as QA and knowledge management systems, enumerators still need to find appropriate answers from long and complex knowledge search results. On the other hand, Open-domain Question Answering (OpenQA) is capable of identifying answers to natural questions based on large-scale documents. OpenQA has main components, namely Retriever and Reader. For Retriever tasks, Dense Retrieval (DR) is proven to outperform traditional sparse retrieval such as TF-IDF or BM25. However, other research actually shows that BM25 is superior to DR in terms of accuracy. In this study, we compared DR and BM25 separately and DR+BM25 as a retriever. Additionally, we combine and evaluate several enhanced language models as Readers. In this way, a model with the best combination of Retriever and Reader can be obtained to be implemented in search systems such as QA and knowledge management systems.
Analysis of Factors Affecting the Open Unemployment Rate (UOR) 2022 : A Case of Banten in Indonesia Aqilla Haya; Risma Dwi Lestari; Tengku Mashitah Crisanty
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.v2023i1.386

Abstract

Unemployment is one of the many economic problems. One type of unemployment is open unemployment. The Open Unemployment Rate (UOR) in Banten Province has the largest rate in Indonesia. In this study, exploratory factor analysis was used with the aim of finding out the factors that contribute to UOR in Banten Province. The data source used is secondary data obtained from publications by Badan Pusat Statistik. Factor 1 (human development) includes the Human Development Index, Economic Growth, Population Growth Rate, Literacy Rate and Mean Years of Schooling (MYS) and Factor 2 (population) consists of only one variable, that is total population. The results of this research show that total population, HDI, and MYS have the largest contribution to UOR in Banten Province. It is hoped that the government can increase business opportunities, employment opportunities and mature human development planning to reduce UOR in Banten Province.
Time-Series Clustering of the Regencies Hotel Room Occupancy Rate in Indonesia after the COVID-19 Pandemic Ladisa Busaina; Setia Pramana; Satria Bagus Panuntun
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.v2023i1.387

Abstract

After COVID-19 pandemic, Indonesia entering the recovery era. The government provides incentives for tourism industry recovery. This policy was created because the impact of COVID-19 pandemic on tourism industry at each regencies/cities are different. This study investigates a different recovery pattern at regencies/cities across Indonesia. The data of this study consist of the room occupancy rate (ROR) from Badan Pusat Statistik (BPS) Indonesia and from web scraping monthly data from Agoda website between 1 January 2021 until 1 August 2023. The regencies/cities are clustered by ROR category using the dynamic time warping method. The result of study, there is a difference of tourism industry recovery at regencies/cities across Indonesia, which is the speed are fast, medium, or slow. This could be the result of differences of different policy in each regency/city to respond COVID-19 pandemic on their tourism industry.
Energy Poverty and Its Determinants at Subnational Level of Indonesia in 2021 Salbila Anandia Ramadanti; Wahyuni Andriana Sofa
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.v2023i1.389

Abstract

In the coming decades, the energy sector will soon be faced with three major transformations, one of which is energy poverty. The World Economic Forum defines energy poverty as people's limited access to modern energy services and products. Access to modern energy has not been fully met for all regions in Indonesia and disparities between regions still occur. For this reason, indicators are needed to measure the level of energy poverty at both the national and district/city levels. This study aims to analyze energy poverty in Indonesia and determine its determinants using the Multidimensional Energy Poverty Index (MEPI) approach. The data used is the March National Socio-Economic Survey and BPS Village Potential in 2021. This research uses Geographically Weighted Regression (GWR) to determine the determinants of Indonesia's multidimensional energy poverty at the district/city level in 2021. It was found that there were still inequalities in energy poverty conditions in most of Indonesia's districts/cities. Analysis using the GWR model resulted in 66 regional groups that were grouped based on the similarity of variables that had a significant effect. The level of influence of the independent variables vary across districts/cities as consequence of spatial heterogeneity in the data.
The Effect of Financial Development on Economic Growth in East Kalimantan in 2013-2021 Raihan Hibatullah; Aisyah Fitri Yuniasih S.S.T., S.E., M.Si.
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.v2023i1.392

Abstract

Indonesia has a strong commitment to realizing inclusive and sustainable economic growth. The 8th SDGs achievement program has become the government's main program implemented in all provinces in Indonesia. The economic growth of a region can be measured using growth of Gross Regional Domestic Product (GRDP). East Kalimantan is one of the largest GRDP contributing provinces in Indonesia with the mining and quarrying sector as the leading sector. However, economic growth in the province is still relatively low and has never reached national figures. This forces the government to consider and develop the potential of other sectors. The Fiscal Policy Agency stated that the financial sector with its development has driven Indonesia's economic growth in the last few decades. This study aims to analyze the general picture of economic growth and financial development as well as the influence of financial development factors on the economic growth of districts/cities in East Kalimantan Province in 2013-2021. The analytical method used in this research is panel data regression. The results obtained are number of bank offices per population, number of cooperatives per population, credit distribution per GRDP, and number of workers have a positive effect on the economic growth of districts/cities in East Kalimantan Province in 2013-2021.

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