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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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Household Food Insecurity in DKI Jakarta Province at The Beginning of The Covid-19 Pandemic Lutfi Hamdani Sutikno; Budiasih Budiasih
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.v2021i1.226

Abstract

Food insecurity is a global issue that’s concern not only in poor and developing countries, but also in developed countries. Its conditions have worsened since the beginning of the Covid-19 pandemic where social restrictions and economic contraction caused many people to lose their jobs, incomes, and increased poverty. DKI Jakarta was one of the most economically affected provinces at the beginning of the Covid-19 pandemic where economic growth in the first quarter of 2020 recorded grow 5.06 percent year on year (the lowest in the last ten years) and slowed down by 0.56 percent overall quarter to quarter, and an increase of poverty 1.11 percent, the highest in Indonesia. This study examines the effect of household characteristics in DKI Jakarta on their food insecurity status at the beginning of the Covid-19 pandemic. The data used is the March 2020 Susenas which was analyzed descriptively and inferentially using firth logistic regression. The results showed that there were 4.47 percent of households in DKI Jakarta had food insecurity status at the beginning of the Covid-19 pandemic. In general, households with food insecurity status are poor, don’t have social security, the head of the household doesn’t work and less than high school education.
A Spatial Analysis of Crime in East Java Province in 2019 Choirul Ummah; 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.v2021i1.227

Abstract

Crime is one of the consequences of fluctuations in the economic condition of a country. Crime incidents harm many parties. The number of criminal acts increased in 2019, especially in Sumatra and Java Island. Most provinces experienced an increasing number of criminal acts, one of them was East Java. East Java contributed more than a quarter of the number of crimes throughout Java Island. The number of criminal acts is count data with overdispersion because its variance is higher than its average. This study aims to analyze the number of criminal acts by applying Geographically Weighted Negative Binomial Regression (GWNBR). The result shows that GWNBR formed two regional groups based on significant variables. The four independent variables namely the unemployment rate, the number of poor people, the Gini ratio, and the police population ratio have a significant effect on all districts/cities. However, the mean year of schooling shows a significant effect only in certain districts/cities. The GWNBR is the best model in modelling the number of criminal acts in East Java.
Application of Logistic Regression Modeling for Complex Survey Data on Education Continuity of Poor Households Children Rudi Salam; Ardi Adji
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.v2021i1.228

Abstract

Many population-based surveys such as the National Socio-Economic Survey (Susenas) are built with complex sampling assumptions, namely probabilistic, stratified, and multistage sampling, with unequal weights for each observation. This complex design must be taken into account in order to have reliable results when doing modeling. The model that is often used when using survey data is logistic regression. The purpose of this study is to determine a logistic regression model with a complex sample design, and to show how it is estimated using a package survey from the R software. As an illustration, the 2019 Susenas data of East Java Province will be used as an application to correct the influence of the sample design in estimating risk factors related to the chances of children 7-18 years old in poor households continuing their education. The results show that the variables of gender and mother's education significantly affect the continuity of the education of children 7-18 years old in poor households.
An insight into Youth Unemployment in Indonesia Ayuningtyas Yanindah
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.v2021i1.229

Abstract

Youth unemployment in Indonesia has continued to remain at a high level relative to other age categories for several years. The case of Indonesia’s youth unemployment is grave with the presence of a low workforce participation rate, informal employment, and higher unemployment rates in young people compared with adults. Due to the lack of research on a country-wise view of youth unemployment, this study focuses on providing a much better understanding of the youth unemployment problem in emerging countries, especially Indonesia. The main aim of the paper is to bridge the research gap on youth unemployment with reference to microeconomic determinants, such as educational background and participation in training. This study utilized the August 2019 data of SAKERNAS (Survei Angkatan Kerja Nasional) and analyzed the data using the logistic regression method. Logistic regression is a special econometric model where the dependent variable is considered categorical and dichotomous (binary); in this case, it was unemployed (1) or working (0). The study found that training participation has a negative correlation with youth unemployment, while educational attainment generates mixed results.
Estimation of Inflation Threshold of Indonesia and Its Effect on Economic Growth Periode 1981-2019 Monika Stevany Manurung; Aisyah Fitri Yuniasih
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.v2021i1.230

Abstract

Sustainable economic growth with a low and stable inflation rate is one of the goals of macroeconomic policy in improving people's welfare. High inflation can be detrimental to economic growth in the medium and long term, while a certain level of inflation is needed to move the economy. Therefore, the question arises about the level of inflation that does not have a negative impact on economic growth. This study aims to estimate the inflation threshold level and identify its effect on Indonesia's economic growth 1981-2019. The research begins by determining the best model among the models that regress inflation on economic growth with quadratic regression, Hansen's (2000) threshold regression, and Mubarik's (2005) threshold regression (2005). The best model is the Mubarik threshold regression model (2005) with an inflation threshold of 6.85 percent. Mubarik's (2005) threshold regression analysis was reused in the model involving the FDI variable, the inflation threshold was 7.11 percent, and FDI had a positive effect. Inflation below the inflation threshold encourages economic growth, while inflation above the inflation threshold is detrimental to economic growth. The result of the estimated threshold level is higher than the inflation target by BI, so that inflation targeting can be increased.
Construction of Smart City Development Index in Indonesia Nabil Miftah Irfandha
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.v2021i1.233

Abstract

Development in urban areas requires city management to solve problems that occur because of high population growth. The complexity of the issues in urban areas varies widely, including a decrease in the quality of public services, reduced availability of residential land, congestion on the highway, excessive energy consumption, waste accumulation, increased crime rates, and other social problems. City assessment tools can be used as support for decision-making in urban development as they provide assessment methodologies for cities to show progress towards defined targets. In the 21st century, there has been a shift from sustainability assessment to developing smart cities. The construction of the Smart City Development Index (SCDI) is considered capable of providing a basis for formulating effective and efficient solutions in reducing existing city problems. The purpose of this study is to find out the general description and get the factors that form SCDI; get the results of SCDI measurements; examine the uncertainty analysis and sensitivity analysis of SCDI, and see the relationship between SCDI and HDI (Human Development Index). Based on the results of factor analysis, there are six factors formed where the highest SCDI with a population of fewer than 200,000 people in Madiun City (East Java Province), the highest SCDI with a population between 200,000 to 1,000,000 people in Yogyakarta City (DI Yogyakarta Province) and the highest SCDI with a total population of over than 1,000,000 people in Tangerang City (Banten Province). The results of uncertainty analysis and sensitivity analysis show that the formed SCDI is robust and reliable. In general, SCDI has a positive relationship to Human Development Index (HDI). The construction of this index aims to facilitate local and central governments in reviewing policies regarding the distribution of funds so that the smart city's development is by existing conditions.
Sentiment Analysis on PeduliLindungi Application Using TextBlob and VADER Library Fathonah Illia; Migunani Puspita Eugenia; Sita Aliya Rutba
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.v2021i1.236

Abstract

The Covid-19 virus has become a global pandemic, including Indonesia. Various efforts have been made by the Government to reduce the negative impact by this pandemic, one of which is through the PeduliLindungi application. The research was conducted to obtain public sentiment towards the application by using twitter data. The data collection period is from August 31 to September 7, 2021, this period was chosen due to the emergence of news regarding vaccine data leaks associated with data leaks in the PeduliLindung application. Sentiment analysis is carried out using the TextBlob and VADER libraries. The results of this sentiment analysis are sufficient to display public opinion and it is hoped that decision makers can improve applications based on these opinions. Then, it was found that the VADER library can be said to be better in conducting sentiment analysis in research because the lexicon approach used is based on social media.
Detection of Public Sentiment Analysis Model on the Implementation of PPKM in Indonesia Renata Putri Henessa; Muhammad Al-Fath Fisabilillah; Windy Rahmatul Azizah
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.v2021i1.237

Abstract

Covid-19 pandemic which has been being serious problem in Indonesia indirectly force Indonesian government to issue policies in order to decrease the number of Covid-19 spread. One of the policies is the Implementation of Restrictions on Community Activities (PPKM) in Java-Bali region from January 11-25, 2021. Due to its continued implementation, this policy raises pros and cons in the community. This research’s goal is to determine the best classification model and determine the effect of adding feature engineering in analyzing public sentiment on PPKM with scrapping data from Twitter so that with the best model, it is possible to classify public responses to PPKM automatically. The twitter scrapping dataset is preprocessed first, which includes case folding, tokenizing, filtering, stemming, and term weighting to clean the data. After preprocessing and through the analysis steps, it concludes that using feature engineering can increase the accuracy of the best selected four models. The logistic regression method with feature engineering with accuracy rate of 87.50% become the best method. In conclusion, the best suggested model to analyze public sentiment using Twitter scrappimg towards PPKM is by using the logistic regression.
Resilience of Workers Affected by COVID-19 Outbreak in Maintaining Their Jobs, in Which Sector Survives Most Longer? Fenanda Dwitha Kurniasari; Yulia Atma Putri
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.v2021i1.238

Abstract

Employment is one of the areas affected during the covid-19 outbreak. The government of Indonesia has taken numerous measures to restrain the growth rate of covid-19, such as the implementation of social restriction, which leads to a multidimensional problem – the employment problem. Indonesia’s unemployment in 2020 has increased compared to 2019. According to Statistics Indonesia, the open unemployment rate in August 2020 is about 1.84 percent higher than August 2019, and from the total working-age population in August 2020, 14.28 percent of them were affected by covid-19. This study aims to investigate the resilience of workers affected by the covid-19 outbreak in maintaining their jobs by comparing the survival rates in the sectors most affected by covid. The methodology used in this research is survival analysis in time resilience of workers affected by the covid-19 outbreak in maintaining their jobs. The conclusion obtained from this study is that the sector significantly influences worker’s time resilience (p-value < 0.05). Among the six sectors most affected by covid-19, workers in the construction sector has the highest time resilience compared to 5 other sectors – most survive workers in maintaining their jobs during covid-19 outbreak, followed by the accommodation and food services, other services activities, manufacturing, wholesale and retail trade sectors. The most affected sector for the time resilience of workers during the COVID-19 outbreak is transportation and storage.
AMDA: Anchor Mobility Data Analytic for Determining Home-Work Location from Mobile Positioning Data Amanda Pratama Putra; Wa Ode Zuhayeni Madjida; Ignatius Aditya Setyadi; Amin Rois Sinung Nugroho; Alfatihah Reno MNSP Munaf
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.v2021i1.239

Abstract

In conducting a mobility analysis using Mobile Positioning Data, the most critical step is to define each customer's usual environment. The initial concept of mobility used is the movement that occurs from and to every usual environment, so errors in determining the usual environment will cause incorrect mobility statistics. Therefore, Anchor Mobility Data Analytic (AMDA) is proposed for Home-Work Location Determination from Mobile Positioning Data. This algorithm uses clockwise reversal to make it easier to classify someone in their usual environment. Unfortunately, only about 80% of the raw data can be used to establish usual environments. The remaining 20% do not have sufficient data history. This study found that the accuracy of AMDA in determining monthly home location was 98.8% at the provincial level and 88.7% at the regency level. As for the determination of monthly work locations, 98.9% at the provincial level and 70.4% at the regency level.

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