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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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Estimating Customer Lifetime Value in the E-Commerce Industry Using Multivariate Analysis Bagaskoro Cahyo Laksono; Ika Yuni Wulansari
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.v2021i1.161

Abstract

Companies can develop their business using big data to support decision-making. Big data in the e-commerce industry that includes size and speed of high transactions can be used to analyze customer behaviour and predict customer value. Nowadays, companies are starting to develop customer-oriented rather than product-oriented business interests. One way that can be used to determine customer value is by calculating Customer Lifetime Value (CLV). By knowing CLV at the individual level, it will be useful to help decision-makers to develop customer segmentation and resource allocation. It is important to do segmentation or customer grouping that describes customer loyalty groups. Therefore, this research aims to calculate CLV and customer segmentation using the RFM analysis method. The dimensions of forming CLV include the values of Recency, Frequency, and Monetary. In this study, concept of multivariate statistical analysis will be applied, namely K-Means Clustering and factor analysis. Segmentation is done to determine the level of customers. The higher the CLV value, more valuable customer is to maintain. In the end, the customer segmentation method built by author can be used to optimize company's strategy to get maximum profit. This method can be applied to various cases and other companies.
Analysis of The Impact of The Covid-19 Pandemic on The Performance of Indonesian Non Oil and Gas Exports I Gusti Bagus Ngurah Diksa; Dewa Ayu Srijayanti
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.v2021i1.164

Abstract

In 2020, Indonesia's exports decreased by 2.61 percent due to declining global and domestic demand during the COVID-19 pandemic. The decline in exports was not too deep due to the increase in non-oil exports by 16.73 percent, while non-oil exports fell by 10.10 percent. This shows the potential for non-oil exports to support the Indonesian economy during the pandemic. Seeing the impact of COVID-19 on export performance then used the ARIMA method. Based on the research, it was found that at the beginning of the COVID-19 pandemic, Indonesia experienced a slump in export performance, especially non-oil and gas. This is due to various policies regarding restrictions on mobility.
The Impact of Domestic Investment, Foreign Investments, HDI, Export, and Import on the Economic Growth in Indonesia Lutfia Septiningrum; Paramita Dewanti; Fauzah Hikmawati
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.v2021i1.165

Abstract

The aims of this study were to examine the causal relationship between domestic investment, foreign, Export, Import, HDI and their impact on Indonesia's economic growth measure with GDP. The data used was panel data from 18 provinces in 2016-2020 which was taken based on stratified random sampling. The model used to complete the purpose of this research was panel data regression. The results of the analysis show economic growth based on the value of GDP in each province tends to decline. Modelling of economic growth in Indonesia was used Panel Data Regression. In this research, Hausman Test is used to obtain the best model of panel data regression because the model contain of Random Effect Model. Based on Simultaneous test results obtained at least one significant variable to the model and based on partial test the GDP was significantly influenced by the variables of FDI, DDI, HDI and Import sectoral value. Variable Export has an effect on GDP but is not significant where R2 shows the results of 98.9%
Data Collection Improvement: Daily Self-Enumeration Accommodation Survey Ignatius Aditya Setyadi
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.v2021i1.168

Abstract

Until now, BPS - Statistics Indonesia has conducted monthly accommodation surveys both for the star and non-star accommodation categories to provide information on commercial accommodation activities at the national and regional levels. Both star and non-star accommodation categories are done by complete enumeration in each region. Statistics include guest night and room capacity to obtain the occupancy rate of a hotel room. The data contains daily accommodation information that is collected every month, so then it will be entered completely in each region following the observation month. Due to the timeliness requirements for monthly press releases, BPS has implemented online data entry since 2017. It may seem obvious, regions that have more interest will have an impact on a bigger number of accommodations, which also affects the number of enumerators and may lead to such problems especially in response burden. Unfortunately, the same problem is also not easily avoided by regions with less accommodation, mostly due to the distance issues to the accommodation area and its spread in the region. Therefore, a new data collection strategy is required to provide respondents with convenience in order to increase response rates, as well as to reduce the workload of enumerators which also leads to the lower cost. The outbreak of COVID-19 has posed unprecedented problems for National Statistical Offices (NSOs) around the world, including BPS – Statistics Indonesia. This crisis has led us to think in new ways and make decisions that will change our statistical operations in order to meet ongoing data needs even throughout the epidemic. The purpose of this paper is to discuss the evolution of accommodation surveys, which are designed to not only solve problems but also achieve objectives. Currently, there are nearly 180 active users of this self-enumeration accommodation survey for about 142 distinct accommodations across Indonesia. Moreover, this addition has proven to have succeeded in increasing the response rate average from 57.17% in 2020 to 68,35% in 2021.
Multilevel Analysis: Household and Regional Factors Influence Agricultural Household Poverty in Indonesia, 2019 Ariful Romadhon; Diyana Indah Sari; Bayu Rhamadani Wicaksono
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.v2021i1.173

Abstract

The agriculture sector is not only a source of food but also a support for the economic activities of most people in Indonesia, especially in rural areas. Unfortunately, most of their life are still below the Poverty Line/Garis Kemiskinan (GK). The uniqueness of this study is that this study uses household and regional variables to see their effect on agricultural household poverty. Thus, the policies will be taken are not only from the micro-economic of the household but also from the macro-economic perspective. Using multilevel binary logistic regression analysis, this study aims to examine the household and regional factors that affect the household poverty in agriculture sector in 2019 as the potential sector to alleviate poverty. Household and regional factors that affect agricultural household poverty are education, household size, resident area, ownership of pension social security, ownership of social assistance, credit assistance for businesses, and Gross Regional Domestic Product (GRDP) agricultural per capita. The variation of agriculture household poverty due to differences in characteristics between 514 districts in Indonesia is 35.19 percent.
Measurement of Sustainable Agriculture at Household Level: Results of Indonesian Agriculture Integrated Survey (AGRIS) Pilot Kadir Kadir; Isnaeni Nur Khasanah; Eka Rudiana
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.v2021i1.177

Abstract

This study aims to measure and analyzes the level of agricultural sustainability at the household level using the results of the Integrated Agricultural Survey (AGRIS) pilot conducted by Statistics Indonesia in 2020. Applying descriptive analysis on the computation results of eleven sub-indicators of the SDGs 2.4.1 indicator at the household level, we analyzed the proportion of agricultural households categorized as sustainable and unsustainable for each corresponding sub-indicator of sustainability. We also estimated the average land area managed by agricultural households for each category in each sub-indicators. We found that most agricultural households in West Java, East Java and West Nusa Tenggara are categorized as unsustainable in agricultural practices regarding land productivity. The proportion of households practising unsustainable agriculture are also quite large regarding fertilizer use and decent employment. We also found that less land productivity and poor management of fertilizer use are the phenomena of a relatively large scale farm.
Variables Affecting Eligible Women in Poor Households to Smoke in Indonesia 2017 Maghfira Ramadhani; Risni Julaeni Yuhan
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.v2021i1.180

Abstract

Smoking is one of public health threats. Cigarette consumption does not only impact on a person's declining health but also social behavior. Smoking behavior in women, especially eligible women (15-49 years old) threatens women’s reproductive health and the condition of the fetus in the womb during pregnant, which may get worse in poor households. Aside from that, cigarette consumption in Indonesia occupies the second position in food consumption with a portion of 12.17 percent. Therefore, the purpose of this study is to examine the variables that affect eligible women in poor households to smoke in Indonesia. The sources of the research data are the 2017 Indonesia Demographic and Health Survey (2017 IDHS) with the Household and Eligible women questionnaires. The method of analysis used descriptive analysis and inferential analysis with binary logistic regression method in rare event with the firthlogit model. The results of the study show that eligible women in poor households in Indonesia would have a tendency to smoke when they live in urban areas, are more mature in age, their highest educational level is lower than junior high school, work, never access mass media, have partner who do not work and have a big number of household members.
Antisocial Behavior Monitoring Services of Indonesian Public Twitter Using Machine Learning Fitri Andri Astuti
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.v2021i1.181

Abstract

Antisocial behavior is a personality disorder that has characteristics such as repetitive actions that violate social norms, deceit and lying, impulsiveness, irritability and aggression, reckless disregard for the safety of oneself and others, consistently irresponsible, and lack of remorse. The cause can be from various factors, including genetics, psychological conditions, interactions in the environment, and wrong parenting. The impact of antisocial behavior on social life can cause people to tend to be aggressive and take it into action by not having feelings of guilt for their actions. Thus, a monitoring of antisocial behavior disorders is needed so that it can be a warning for the public to be more concerned about the difficulties experienced by each other. The potential gained from the availability of tweet data access from the Twitter API opens up opportunities for monitoring antisocial behavior. By utilizing traditional machine learning and deep learning methods, it can be an opportunity to automate labeling on Twitter data that contains elements of antisocial behavior. Based on the description of the problems and opportunities found, this study proposes a multi-class classification monitoring service to identify public antisocial behavior on Twitter Indonesia using machine learning.
Return to Education Estimation on Self-Employment Entrepreneurs and Their Comparison with Workers in Indonesia Dwi Wahyudi; Muhammad Hanri
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.v2021i1.183

Abstract

Entrepreneurship in various pieces of literature is mentioned as one aspect that adds value to a country's economy. Using Sakernas August 2019 data and the Mincer income model, this study estimates the educational investment in self-employed entrepreneurs. The results show a positive effect between years of schooling and income earned. Compared to workers, the level of assessment of entrepreneur education looks lower. In addition, this study also looks at how income among entrepreneurs. The Gini coefficient shows 0.47 for self-employed entrepreneurs and 0.41 for workers. There is a sizeable amount of income inequality for self-employed entrepreneurs.
Measuring The Economic Contribution of Tourism: An Improvement in Indonesia Akhmad Mun'im
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.v2021i1.187

Abstract

The implementation of the international standard manual is an effort made by every national statistical office (NSO) in developing its official statistics so that they have comparability at the global level. The methods recommended in the international standard manual have also been refined and adapted to other standard manuals so that the resulting official statistics are consistent with each other. Statistics Indonesia (BPS) as the Indonesian NSO adopts various international standard manuals, including the International Recommendations for Tourism Statistics (IRTS) and Tourism Satellite Accounts: Recommended Methodological Framework (TSA:RMF) 2008 manuals recommended by UNWTO in calculating the tourism contribution in the Indonesian economy. Both recommend the utilization of the supply and use table (SUT) framework that explains tourism supply-demand in measuring tourism contributions. This approach is an improvement from the previous approach which used shock analysis under input-output (I-O) framework in calculating tourism contributions. Through the supply-demand of tourism sector approach, the amount of tourism direct gross domestic product (TDGDP) is obtained which shows the contribution of tourism to the national economy. During 2016-2019, the tourism sector contributed around 4.6 – 4.9 percent to the Indonesian economy.

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