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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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Achievement of Creative Economy Dimensions in Regional Development Indonesia in 2021 Anisa Nur Jannah; Ekaria Ekaria
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.v2023i1.354

Abstract

So far, measurement for knowing development of creative economy in Indonesia has only seen from GDP and number of workers. Even though there are many other factors used to determine development of creative economy that are not included in these two measurements. Therefore, this study aims to develop a measurement that can be used as a tool for assessing and analyzing the state of creative economy in 34 provinces in Indonesia and for comparison. Data used is secondary that sourced from BPS and several agencies. Creative Economy Index (CEI) refers to Global Innovation Index which is composed from seven dimensions, institution, human capital and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, creative outputs. Analysis method used is factor analysis to validate dimensions of CEI based on their indicators. Results showed that Indonesia`s CEI is relatively low. Dimension with highest achievement is institution, while lowest achievement is market sophistication. When compared by region, CEI in Western Region is higher than Eastern Region. There are also similarities with HDI and ICT-DI.
Geospatial Big Data Approaches to Estimate Granular Level Poverty Distribution in East Java, Indonesia using Machine Learning and Deep Learning Regressions Rifqi Ramadhan; Arie Wahyu Wijayanto; Setia Pramana
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.v2023i1.359

Abstract

One of the economic development the focus of the Indonesian government's efforts is for reducing poverty. In Indonesia, collecting poverty data uses the conventional method, the name is National Socio-Economic Survey (SUSENAS) which takes a large cost, time, and effort. To overcome these limitations, there is a need for additional data to provide more detailed poverty data. Recent studies show that the use of geospatial big data could identify poverty at a granular level, with a lower cost and faster update because of their unique and unbiased capacity to identify physical and socioeconomic phenomena. The integrated multi-source satellite imagery data such as the normalized difference vegetation index (NDVI) for detecting rural areas based on vegetation, built-up index (BUI) for identifying urban areas through building distribution, normalized difference water index (NDWI) for land cover detection, day time land surface temperature (LST) for identifying urban regions based on surface temperature, and pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) to evaluate economic activities based on pollution. Additionally, point of interest (POI) density and minimum POI distance are used to measure area accessibility. Therefore, the contribution of this research is to implement the utilization of geospatial big data to estimate the numbers of poverties at a granular level to the 666 sub-districts in East Java Province using machine learning and deep learning regression models. The evaluation results to estimate sub-district level poverty shows that the best model development using Support Vector Regression (SVR) in machine learning was the best root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values of 0.365, 0.293, and 0.032 with R-squared of 0.59 and MLP in deep learning algorithm with 0.444, 0.345, and 0.039 values of RMSE, MAE, and MAPE with R2 0.52. In addition, the results of visual identification revealed that high estimates of lower poverty are typically found in urban areas with high accessibility, and these areas are not spatially deprived areas with limited accessibility.
Sentiment Classification of Community towards COVID-19 Issues on Twitter (Case Study: Indonesia, March-May 2020) Nur Ainun Daulay; Rifqi Ramadhan; Lya Hulliyyatus Suadaa
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.v2023i1.360

Abstract

This study examines sentiment analysis related to COVID-19 in Indonesia (March-May 2020) using InSet Lexicon as training data in supervised machine learning models. The dataset comprises 7,967 tweets, divided into 90% training data and 10% testing data. The results reveal that Support Vector Machine (SVM) and Random Forest (RF) are the most effective methods, achieving accuracy above 80%, with SVM reaching 87% and RF at 86%. InSet Lexicon itself attains an accuracy of 75%, a macro average of 69%, and a weighted average of 74%, making it an effective alternative for large-scale data labeling. Research recommendations support further development of InSet Lexicon for sentiment classification and expansion of the lexicon for foreign languages to enhance sentiment analysis accuracy in a global context. This study provides valuable insights into understanding public sentiment regarding crucial issues such as COVID-19 in Indonesia.
Design and Implementation of an Interactive Visualization Dashboard for Monitoring the Flood Vulnerability and Mapping Windy Rahmatul Azizah; Arie Wahyu Wijayanto
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.v2023i1.362

Abstract

This study aims to build a web-based interactive visualization dashboard from granular flood vulnerability index estimation maps using data from satellite imagery. The approach used to build this visualization dashboard is a two-dimensional (2D) approach created with the qgis2web python plugin facilitated with a JavaScript leaflet. Raw data from satellite imagery consisting of indicators of the causes of flooding are extracted in comma-separated value (CSV) format. Furthermore, the data is integrated based on its spatial attributes and stored in Geographic JavaScript Object Notation (GeoJSON) format to produce a visualization of the flood vulnerability index map. In web views, dashboards are built by utilizing hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). This interactive dashboard has several useful features in helping the process of monitoring the flood vulnerability of an area such as zoom, "show me where I am", measure distance, search, legend, and change year. Thus, the flood vulnerability estimation map dashboard is expected to assist the government in monitoring areas with extreme flood vulnerability and support the decision-making process related to mitigation of areas that have high flood vulnerability.
A Geovisualization Dashboard of Granular Food Security Index Map using GIS for Monitoring the Provincial Level Food Security Status Dwi Karunia Syaputri; Bony Parulian Josaphat; Arie Wahyu Wijayanto
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.v2023i1.364

Abstract

This study aims to build a web-based interactive geovisualization dashboard from a granular food security index map using satellite imagery and other geospatial big data. The map dashboard is built using a two-dimensional (2D) data visualization approach. Making a two-dimensional map using QuantumGIS (QGIS) tools, displayed in the form of WebGIS with the plugin used "Qgis2web" based on javascript leaflets. Once included in WebGIS, interactive visualizations are displayed on websites with interfaces based on hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). The dashboard map is equipped with interactive features such as legend, click grid, zoom, show me where I am, measure distance, and search. Therefore, the dashboard map can be used to monitor the food security index, search for food security index areas, as well as geographical identification of food security index areas which are useful for supporting the analysis of decision-making or policies by the government regarding food security strategies.
Implementation of User-Oriented Geovisualization Web Dashboard for Monitoring Access to Improved Water using Satellite Imageries Data Fauzan Faldy Anggita; Arie Wahyu Wijayanto
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.v2023i1.365

Abstract

This study aims to develop an engaging, web-based visualization dashboard for improved water access in Indonesia. The dashboard map was made using three technologies: the Qgis2web Python plugin for producing two-dimensional (2D) dashboard maps, JavaScript leaflets for map visualization, and Hypertext Markup Language (HTML), Cascade Stylesheet (CSS), and JavaScript for the user interface. The built-in map dashboard has several features, including grid click, legend, zoom, search, and measure distance, which are meant to help users determine the location of the nearest water treatment facilities, identify geographical features, and keep track of areas that have poor access to improved water. Evaluation using the system usability scale (SUS) concludes the dashboard is acceptable with an excellent rating. Our results reiterate and enhance support for government institution and relevant stakeholders in providing sustainable access to public water.
Modeling Coastal Area Change Analysis of Coastal Urban Areas at Semarang City, Indonesia: Modeling Coastal Area Change Analysis of Coastal Urban Areas at Semarang City, Indonesia: A Comparison of Machine Learning Classifiers on Optical Satellite Imageries Data Renata De La Rosa Manik; Arie Wahyu Wijayanto
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.v2023i1.367

Abstract

A coastal area is defined as the boundary between land and sea. Coastal urban areas are susceptible to various hazards that are becoming more severe, such as flooding, erosion, and subsidence due to a mix of man-made and natural factors, including urbanization and climate change. Regardless of the high importance of coastal area monitoring, conducting field surveys is expensive, time-consuming, and geographically limited to non-remote regions. Semarang City is one of the cities in Indonesia that is at risk of changes in its coastline and causes various natural problems. This research aims to estimate changes in the coastal land area in Semarang City. In observing the phenomenon of changes in area in coastal areas in Semarang City, remote sensing technology with Sentinel-2 satellite imagery was used. This research implements and compares the Random Forest (RF) and Support Vector Machine (SVM) machine learning methods in building classification models. From the results of land area in 2019, 2021, and 2023 with the best classification model, namely SVM, information was obtained on an increase in coastal area of 387.94 ha in 2021, then a change in area decrease of 417.32 ha in 2023.
The Effect of Company Performance on Stock Returns in the LQ45 Stock Cluster in 2020-2022 Auliya' Jami'atus Saufi; Ekaria
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.v2023i1.368

Abstract

In the Indonesian capital market, various securities are traded, but stock investors dominate. The LQ45 index illustrates the Indonesian capital market condition is better than the JCI (Jakarta Composite Index). Stocks on the LQ45 index have large market capitalization, high liquidity, and good company fundamentals, but have varying returns. It is necessary to group the stock returns of LQ45 index companies. The method used is time series clustering in the 2020-2022 period. Furthermore, the logistic regression analysis is used to determine the effect of company performance that is consistent in the LQ45 index on stock return status. The results showed that the selected algorithm for clustering was K-Means with 2 optimal numbers of clusters characterized as lagging stock and leading stock. Then, company stocks in the LQ45 index for the 2020-2022 period tend to be classified as leading stocks if they have a low Debt to Equity Ratio but have a high Net Profit Margin and Price Earnings Ratio.
Integrating Satellite Imageries and Multiple Geospatial Big Data for Granular Mapping of Spatial Distribution of Human Development Index in East Java, Indonesia Rifqi Ramadhan; Arie Wahyu Wijayanto
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.v2023i1.369

Abstract

The availability of data on the Human Development Index (HDI) is crucial as a gauge of regional performance, particularly in terms of assessing the development of human resources. In Indonesia, the collecting of HDI data usesthe conventional method, such as undirect estimation, National Socio-Economic Survey (SUSENAS), The Ministry of Religion, or inventory of sectoral data that used the large cost, time, and effort. Additional data are required to provide more detailed poverty data at a lower cost and with more recent information to overcome these limitations. According to recent studies, the quality of life for measuring HDI can be identified down to the granular level using geospatial big data. Therefore, the contribution of this research is to implement the use of geospatial big data, such as integrated multi-source satellite imagery data and Point of Interest (POI). Besides that, this study develops the relative spatial human development index in 11 km x 11 km resolution for the granular mapping of the quality of life to measure the HDI in East Java, Indonesia. The kinds of weighted sum models used in this study such as equal weight (EWS), Pearson (PCCWS), Spearman (SCCWS) correlation-based weight, and Principal Component Analysis (PCA)-based weight (PCAWS). The best RSHDI PCCWS for representing the human development index in East Java in 2022, which was determined using a weight-sum model based on Pearson correlation, has a correlation coefficient of 0.7858 (p-value = 5.078 x 10-9) and is highly correlated with official HDI data. The use of this RSHDI as a predictor variable in the estimation of HDI data shows the ideal model had an RMSE of 3.098% and an R2 of up to 61.75% using RSHDI PCCWS. According to the findings of the descriptive analysis of this map, areas with low RSHDI scores typically in some regencies areas in Madura Island and the east area of East Java with geographically depressed, while areas with high RSHDI scores typically have dense populations and have better accessibility such as urban area in Surabaya and Kota Malang. As a result, the official human development index data can be supported by the RSHDI's ability to map spatially deprive areas
Prediction of Central Java’s Number of Exports to Four ASEAN Countries Using the Markov Chain Analysis Ria Novita Awalia Ramadhani; Andreas Rony Wijaya; ALIFIA ZAHRA WINESTI; DESTY MAYANG PRATIWI
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.v2023i1.371

Abstract

Central Java is one of the provinces that has many of natural resources and extraordinary industrial potential, able to offer reliable prospects to various developed countries in ASEAN, namely Singapore, Brunei Darussalam, Malaysia, and Thailand, to become the focus of exploration attention. Therefore, a prediction is made of Central Java's exports to the four ASEAN countries in 2022 and 2023 by applying the Markov chain analysis method. The prediction results obtained that the total exports to Singapore, Brunei Darussalam, Malaysia and Thailand in a row in 2022 are 0.701, 0.001, 0.239, and 0.058. While the predictions for 2023 for the four countries are 0.540, 0.001, 0.409, and 0.050 respectively. Meanwhile, the steady state of the Markov chain is 0.3595 for Singapore, 0.0013 for Brunei Darussalam, 0.6001 for Malaysia, and 0.0389 for Thailand. The results of this prediction can assist parties involved in making economic decisions related to Central Java's exports to developed countries in ASEAN. Information regarding predictions of an increase or decrease in exports from one year to the next can be used as a reference for business people, governments and related organizations to plan more appropriate and efficient economic strategies and policies.

Page 13 of 26 | Total Record : 251

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