Proceedings of The International Conference on Data Science and Official Statistics最新文献_第2页

Trajectory of life expectancy and its relation with socio-economic indicators among developing countries in Southeast Asian 东南亚发展中国家的预期寿命轨迹及其与社会经济指标的关系

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.307

M. Wijaya, Yanne Irene, Iqbal Rachadi

{"title":"Trajectory of life expectancy and its relation with socio-economic indicators among developing countries in Southeast Asian","authors":"M. Wijaya, Yanne Irene, Iqbal Rachadi","doi":"10.34123/icdsos.v2023i1.307","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.307","url":null,"abstract":"Life expectancy is a one of key global health indicators and plays an important role in health policy measures. The status of a country indirectly influences the life expectancy of a nation. Developing countries have slower economic progress compared to developed countries, which in turn affects the well-being of the population. Therefore, this study aims to analyze the trend of life expectancy among developing countries in Southeast Asian and assess the influence of socio-economic indicators in life expectancy. Linear mixed effects model is used to model the association between socioeconomic factors and life expectancy. The results indicate that GDP growth rate, GDP per capita, and unemployment rate have significant impact on life expectancy and the impacts depend on gender. Life expectancy among females is generally higher than males. Prediction of life expectancy in males in year 2025 is found the lowest in Myanmar with average of 64.2 years (95%CI: 60.8-77.1) and the highest in Thailand with average of 76.2 years (95%CI: 60.7-76.9). Meanwhile, prediction of life expectancy in females is found the lowest in Timor Leste with average of 71.1 years (95%CI: 67.8-83.9) and the highest in Thailand with average of 84.3 years (95%CI: 68.7-84.9).","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"93 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of Central Java’s Number of Exports to Four ASEAN Countries Using the Markov Chain Analysis 利用马尔可夫链分析预测中爪哇省对东盟四国的出口数量

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.371

Ria Novita Awalia Ramadhani, Andreas Rony Wijaya, Alifia Zahra Winesti, Desty Mayang Pratiwi

{"title":"Prediction of Central Java’s Number of Exports to Four ASEAN Countries Using the Markov Chain Analysis","authors":"Ria Novita Awalia Ramadhani, Andreas Rony Wijaya, Alifia Zahra Winesti, Desty Mayang Pratiwi","doi":"10.34123/icdsos.v2023i1.371","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.371","url":null,"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.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"134 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlocking potential of data: A localized data-driven approach for stunting reduction in South Kalimantan Province 释放数据潜力：南加里曼丹省减少发育迟缓的本地化数据驱动方法

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.394

F. Rizkiah

引用次数: 0

Opportunities and Challenges of Remote Sensing, Geospatial Data, and Machine Learning in Obtaining Accessibility and Location Information for Sustainable Development in Indonesia 遥感、地理空间数据和机器学习在获取可访问性和位置信息以促进印度尼西亚可持续发展方面的机遇与挑战

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.309

Terry Devara

{"title":"Opportunities and Challenges of Remote Sensing, Geospatial Data, and Machine Learning in Obtaining Accessibility and Location Information for Sustainable Development in Indonesia","authors":"Terry Devara","doi":"10.34123/icdsos.v2023i1.309","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.309","url":null,"abstract":"With the advancement of technologies so does the data collection method which creates a large, rapid, and diverse stream of data. Statistic Indonesia (BPS) has also encouraged to utilize this by starting to collect geospatial information on respondents and public facilities. To keep up with this a change needs to be made in processing methods to accommodate massive, high-dimensional, and multiform data collected in different forms such as machine learning. This progression also opens up a new opportunity for tackling various statistical data problems such as accessibility and location data. Remote sensing is one of the big data sources that undergoes a lot of changes shown in the high spatial and temporal resolution satellite imagery availability, together with the BPS geotagging data shows great promise in classifying land use and geospatial analysis. Even so, there are still some challenges in remote sensing as well as other geospatial data utilization. The goals of this review paper are to study the opportunities and challenges in utilizing remote sensing, geospatial data, and machine learning for accessibility and location information. In this paper, we explore the possibilities and limitations in its implementation into SDGs indicators that involve accessibility and location such as indicators 9.1.1, 11.1.1, 11.2.1, 11.3.1, and 11.7.1 including other variables needed for the calculation like access to public facilities. Moreover, our experiment using geotagging data shows potential in improving proportion estimation when compared to using a simple ratio. Our DEGURBA following the UN definition using machine learning LULC for dasymetric mapping also provides more insight compared to the existing data. We can conclude that there are great opportunities in applying remote sensing and other geospatial data to monitor the accessibility and location to further sustainable development in Indonesia.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing Blockchain in BPS Microdata Dissemination 在 BPS 微数据传播中利用区块链

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.325

F. S. Genah, D. Venditama

{"title":"Harnessing Blockchain in BPS Microdata Dissemination","authors":"F. S. Genah, D. Venditama","doi":"10.34123/icdsos.v2023i1.325","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.325","url":null,"abstract":"Towards achieving BPS-Statistics Indonesia missions, the dissemination process of statistical products must be carried out well. One of BPS-Statistics Indonesia statistical products is the microdata. In this case, the activity of disseminating microdata should be conducted through the implementation of feasible best practices. Related to that, in a fast-paced of the ever-changing world that heavily relies on the evolution of technology, the process of bringing out the best efforts in disseminating microdata must as well follow the rhythm of the moving technology to meet the current needs of the digital society, because otherwise it will be obsolete as time goes by. One of the important issues is the limitation of the existing system in tracking microdata to ensure its authenticity and integrity, in case where the users have purchased the microdata from BPS-Statistics Indonesia. In addressing this traceability issue, a solution through the implementation of the cutting-edge Blockchain technology is considered. A design is proposed to incorporate Blockchain into the existing mechanism of BPS-Statistics Indonesia microdata dissemination. Therefore, a system architecture and a schema for smart contract utilization are proposed to reinforce the microdata tracking.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Kernel Smoothing and Local Polynomial Smoothing Method in Overcoming Age Heaping 核平滑法与局部多项式平滑法在克服年龄堆叠方面的比较

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.312

N. A. Putri, E. T. Astuti, L. M. A. Fadila, S. S. Hafizhah

{"title":"Comparison of Kernel Smoothing and Local Polynomial Smoothing Method in Overcoming Age Heaping","authors":"N. A. Putri, E. T. Astuti, L. M. A. Fadila, S. S. Hafizhah","doi":"10.34123/icdsos.v2023i1.312","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.312","url":null,"abstract":"Age data plays an important role in every aspect yet there are found age misreporting. It involves digit preference that causes build up in a certain age. Digit preference in demography is called age heaping that often happens at age with 0 and 5 as the last digit. Age heaping induces poor data quality and data bias that could influence government policy making. Two indicators used to detect age heaping are Whipple Index (WI) and Myers Blended Index (MBI). Methods to cope with age heaping are nonparametric regression approaches which are Kernel Smoothing and Local Polynomial Smoothing. The objective of this research is to measure and elevate the quality of population age data and population mortality data in Sensus Penduduk (SP) 2020 as well as comparing methods between Kernel Smoothing and Local Polynomial Smoothing. The data being used in this paper is SP2020 which the research variables are age population, age of death, and total population. The result shows that the data quality of total population death is inaccurate compared to total population thus needs a smoothing process to improve age data to population data accuration. The method that has better accuracy is the Local Polynomial Smoothing method.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"199 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139145809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Detection and Counting of Urban Housing and Settlement in Depok City, Indonesia: An Object-Based Deep Learning Model on Optical Satellite Imageries and Points of Interests 印度尼西亚德波克市城市住房和住区的自动检测与计数：基于光学卫星成像和兴趣点的对象深度学习模型

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.349

A. Pindarwati, Arie Wahyu Wijayanto

{"title":"Automatic Detection and Counting of Urban Housing and Settlement in Depok City, Indonesia: An Object-Based Deep Learning Model on Optical Satellite Imageries and Points of Interests","authors":"A. Pindarwati, Arie Wahyu Wijayanto","doi":"10.34123/icdsos.v2023i1.349","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.349","url":null,"abstract":"Detecting urban housing and settlements has a substantial position in decision-making problems such as monitoring housing and development, not to mention the widely required urban mapping application. One of the most important goals in the United Nations Sustainable Development Goals (SDGs) is to improve urban living conditions globally by 2030. We propose an automatic detection of urban housing and settlements on remote sensing satellite imagery data using object detection-based deep learning using semantic segmentation and the potential availability of remote sensing datasets at high spatial resolutions, Open Street Map (OSM) geolocation point of interest dataset, and Sentinel-2 optical satellite imagery data. The detection model using Mask Region-based Convolutional Neural Networks (Mask R-CNN) is implemented in Depok City, Indonesia. These regions were chosen because it is the second most populous suburb in Indonesia and the tenth most populous globally and, making it challenging to extract building features from satellite imagery. This model categorizes dense, moderate, and sparse conditions and has a promising result of an average precision of 100% and an F1-score of 67% with evaluation performance metrics only considering points associated with buildings, not building boundaries or the intersection over union (IoU). The model performance has been compared to ground check results of field surveys, and it performs best in sparse conditions. Our findings offer the potential implementation of the model for fast and accurate monitoring of housing, settlement, and regional planning in urban areas.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"118 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach 聚焦印度尼西亚雅加达的空气污染：人工智能辅助半监督学习法

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.348

Harun Al Azies

{"title":"Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach","authors":"Harun Al Azies","doi":"10.34123/icdsos.v2023i1.348","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.348","url":null,"abstract":"The air quality in the Jakarta area is examined in this study using artificial intelligence (AI) to assist a semi-supervised learning technique. The clustering approach is used in this article to separate air pollution into three main categories moderate, low, and high levels. This clustering helps identify shared characteristics among measures like PM10, SO2, NO2, and others, even when air quality labels are not always accessible. Using the Random Forest method, the air quality will be categorized in this experiment with an accuracy rate of 93%. Additionally, the results of variable significance analysis are examined on this article to identify the variables with the biggest effects on air quality, notably PM10, SO2, and NO2. This study demonstrates the enormous potential of applying machine learning techniques, particularly semi-supervised learning approaches, to assist sustainable environmental regulations while also monitoring and enhancing Jakarta's air quality. We describe the experimental procedures, the findings, and the implications of our research for comprehending and addressing urban air pollution in this article","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploration of Resnet Variants in High Spatial Resolution Domain Adaptation 探索高空间分辨率领域适应中的 Resnet 变体

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.280

Sulisetyo Puji Widodo, Nur Rachmawati

{"title":"Exploration of Resnet Variants in High Spatial Resolution Domain Adaptation","authors":"Sulisetyo Puji Widodo, Nur Rachmawati","doi":"10.34123/icdsos.v2023i1.280","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.280","url":null,"abstract":"Abstract. When mapping land cover from airborne to spaceborne data, a problem arises, where the difference in sensors between the two shows a large spatial resolution inconsistency and spectral differences. Consequently, the same object may exhibit completely different features. This problem causes models trained from annotated airborne to be ineffective when applied to spaceborne. Cross-Sensor Land-COVER (LoveCS) shows good results in overcoming this problem. LoveCS leverages small-scale aerial image annotations to promote land cover mapping on large-scale spacecraft. LoveCS uses ResNet50 as its encoder. In recent years, many studies have tried to develop other variants of ResNet, such as ResNeXt, ResNeSt, Res2Net, and Res2NeXt. This variation of ResNet turned out to give better results in a variety of tasks compared to ResNet. Therefore, in this study we modified the LoveCS encoder by replacing ResNet50 with ResNet variants such as ResNeXt, ResNeSt, Res2Net, and Res2NeXt in an effort to improve LoveCS accuracy. We also offer LoveCS schemes with better accuracy based on the best encoders. Our evaluation shows that Res2Net50 as an encoder is able to improve LoveCS performance where the average F1 increases by 1.38%, OA by 1.96%, and Kappa by 2.75% from the baseline method.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"103 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vine Copula Model: Application to Chemical Elements in Water Samples 藤蔓 Copula 模型：水样中化学元素的应用

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.346

Salsabila Zahra Aminullah, Mila Novita, Ida Fithriani

{"title":"Vine Copula Model: Application to Chemical Elements in Water Samples","authors":"Salsabila Zahra Aminullah, Mila Novita, Ida Fithriani","doi":"10.34123/icdsos.v2023i1.346","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.346","url":null,"abstract":"Copula can link the bivariate distribution function with marginal distribution functions without requiring specific information about the interdependence among random variables. There are several types of copulas, such as elliptical copulas, Archimedean copulas, and extreme value copulas. However, in multivariate modeling, each type of copula has limitations in modeling complex dependence structures in terms of symmetry and tail dependence properties. The class of vine copulas overcomes these limitations by constructing multivariate models using bivariate copulas in a tree-like structure. The bivariate copulas used in this study include the Clayton, Gumbel, Frank, Gaussian, and Student's t copula families. This study discusses the construction of vine copula models, parameter estimation, and their applications. The construction of vine copulas is done through the decomposition of conditional probability density functions and substituting bivariate copula density functions into the decomposition results. The data used in the study is the logarithm of the concentration of chemical elements in water samples in Colorado. The parameter estimation method used is pseudo-maximum likelihood with sequential estimation. Model selection is then performed using the Akaike information criterion (AIC) to determine the most suitable model. The results indicate that Caesium and Titanium have a dependency relationship with Scandium. Moreover, Scandium and Titanium exhibit the strongest dependence compared to other variable pairs.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"48 1‐2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0