Proceedings of The International Conference on Data Science and Official Statistics最新文献_第8页

Potencies and Threats of The Demographic Bonus on The Quality of Human Resources and Economy in Indonesia 2019 2019年印尼人口红利对人力资源质量和经济的潜力和威胁

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.154

Febby Risandini, Rini Silvi

{"title":"Potencies and Threats of The Demographic Bonus on The Quality of Human Resources and Economy in Indonesia 2019","authors":"Febby Risandini, Rini Silvi","doi":"10.34123/icdsos.v2021i1.154","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.154","url":null,"abstract":"The success of Indonesia’s development is marked by increasing economic growth, which is in line with the demographic transition, where the number of people who are borne is less than the population which bears it. Indonesia will enter the peak of the demographic bonus in 2030, where every 100 productive aged people bear 46 to 47 non-productive-aged people. The demographic bonus can positively impact on the economy and the quality of human resources if its potential is adequately utilized but becomes a threat if not maximized. Therefore, path analysis is used in this study to analyze the potencies and threats of the demographic bonus and its effect on economic growth, either directly or indirectly through the quality of human resources. The results of this study are the potential index consisting of labor absorption, household savings, and women in the labor market does not significantly influence on the quality of human resources and economic growth. Meanwhile, the threats index, which consists of internet access, migration, and child marriage, has a significant positive direct effect on economic growth and a significant negative indirect effect on economic growth. These results indicate that the threat index has a greater influence than the potential and it is hoped that the government will focus on reducing the threat of the demographic bonus, but it must be accompanied by an increase in the quality of human resources.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133227392","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: 3

Revisiting Local Walking Based on Social Network Trust (LWSNT): Friends Recommendation Algorithm in Facebook Social Networks 基于社交网络信任(LWSNT)的重访局部行走:Facebook社交网络中的好友推荐算法

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.124

Wahidya Nurkarim, A. Wijayanto

{"title":"Revisiting Local Walking Based on Social Network Trust (LWSNT): Friends Recommendation Algorithm in Facebook Social Networks","authors":"Wahidya Nurkarim, A. Wijayanto","doi":"10.34123/icdsos.v2021i1.124","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.124","url":null,"abstract":"In the last decades, the internet penetration rate and online social network users have grown very fast. Online social network, such as Facebook, is a platform where one can find friends without having to meet face to face. A social network is represented by a large graph because it involves many participants. Hence, it is hard to find potential friends who have the same thoughts and interests. The Local Walking Based on Social Network Trust (LWSNT) algorithm is one of the popular algorithms for social friend recommendation. This study re-examines whether the correlation between attributes gives un-match ranks in different cases (cases with and without correlation). We assess the performance of LWSNT in Facebook networks under the supervised manner by comparing its F-score against similar methods. By using Kendall’s tau correlation, the results show that the correlation of attributes has no significant effect on the order of friend recommendations. In addition, the LWSNT performance is quite inferior against the Common Neighbors algorithm and Jaccard index.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128689914","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

Enhancing Official Statistics Data Dissemination using Google Firebase Platform on Mobile Application: A User-Centered Design Approach 使用谷歌Firebase移动应用平台增强官方统计数据传播:以用户为中心的设计方法

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.30

Wisma Eka Nurcahyanti

{"title":"Enhancing Official Statistics Data Dissemination using Google Firebase Platform on Mobile Application: A User-Centered Design Approach","authors":"Wisma Eka Nurcahyanti","doi":"10.34123/icdsos.v2021i1.30","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.30","url":null,"abstract":"The dissemination of official statistics as publicly available information has been mandated in the United Nations Fundamental Principles of Official Statistics (UNFPOS) to be highly accessible to all users. Recently, with an increasing volume of data and public demand, National Statistical Offices (NSO) including Statistics Indonesia (BPS) are being challenged to provide accurate, excellent-quality, and user-friendly information. In this paper, we introduce our attempts to enhance the official statistics data dissemination by developing an Android-based mobile application using a User-Centered Design (UCD) approach to meet the requirement of specifically targeted users. Google Firebase platform is utilized to improve the administrator-level usability in updating the disseminated information. The proposed mobile application is launched at BPS-Statistics Madiun Municipality, East Java Province called Batu Cadas (an acronym for BAca TUjuh CAtatan DAta Statistik). Further evaluations using Black-Box functionality testing, System Usability Scale, and specific needs comparison conclude that the proposed mobile application is sufficient to cover the gap between user needs and the currently existing applications.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122328512","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

Study of Exchange Rate Volatility and Its Effect on Indonesian Economic Indicators With Potential Exchange Rate Crisis 潜在汇率危机下印尼汇率波动及其对经济指标的影响研究

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.108

Adin Nugroho, N. Nasrudin

{"title":"Study of Exchange Rate Volatility and Its Effect on Indonesian Economic Indicators With Potential Exchange Rate Crisis","authors":"Adin Nugroho, N. Nasrudin","doi":"10.34123/icdsos.v2021i1.108","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.108","url":null,"abstract":"Exchange rate volatility occurred when exchange rate movement was wildly fluctuating which could depict uncertainty. Since Indonesia used an open economy, exchange rate fluctuation became important to be maintained due to crisis potential. This research was conducted to analyze the effect or impact of exchange rate volatility on the Indonesian economy in general and few related case using time series analysis. ARIMA (Autoregressive Integrated Moving Average) and EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) were used for measuring the volatility in the period between 1997-2021. Then, regressions were applied to analyze the impact of exchange rate volatility on few macroeconomic indicators. The result shows that exchange rate volatility yielded a significant negative effect on GDP Growth rate, export, and import. Logistic regression was used to analyze the factors that were affecting the crisis potential. The result showed only a negative GDP growth rate and high volatility that gave more risk which could lead to crisis. Therefore, it is important to keep exchange rate volatility stable.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124882016","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

Variables Affecting Eligible Women in Poor Households to Smoke in Indonesia 2017 2017年印度尼西亚贫困家庭中影响合格妇女吸烟的变量

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.180

Maghfirah Ramadhani, Risni Julaeni Yuhan

{"title":"Variables Affecting Eligible Women in Poor Households to Smoke in Indonesia 2017","authors":"Maghfirah Ramadhani, Risni Julaeni Yuhan","doi":"10.34123/icdsos.v2021i1.180","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.180","url":null,"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.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117020578","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

Classification of Paddy Growth Phase with Machine Learning Algorithms to Handle Imbalanced Multi-Class Big Data 用机器学习算法处理不平衡多类大数据的水稻生长阶段分类

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.45

Hady Suryono, H. Kuswanto, Nur Iriawan

{"title":"Classification of Paddy Growth Phase with Machine Learning Algorithms to Handle Imbalanced Multi-Class Big Data","authors":"Hady Suryono, H. Kuswanto, Nur Iriawan","doi":"10.34123/icdsos.v2021i1.45","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.45","url":null,"abstract":"The global Sustainable Development Goals (SDGs) adopted by countries in the world have significant implications for national development planning in Indonesia in the period 2015 to 2030. The Agricultural sector is one of the most important sectors in the world and has a very important contribution to achieving the goals. Availability of accurate paddy production data must be available to measure the level of food security. This can be done by monitoring the growth phase of paddy and predicting the classification of its growth phase accurately and precisely. The paddy growth phase has 6 classes with the number of class members usually not the same (imbalanced data). This study describes the results of the classification of paddy growth phases with imbalanced data in Bojonegoro Regency, East Java in 2019 using machine learning algorithms on the Google Earth Engine (GEE) platform. Classification is done by Classification and Regression Tree, Support Vector Machine, and Random Forest. Oversampling technique is used to deal the problem of imbalanced data. The Area Sampling Frame survey in 2019 conducted by BPS was used as a label for classification model training. The results showed that the overall accuracy (OA) using the Random Forest algorithm by modifying the dataset using oversampling was 82.30% and the kappa statistic was 0.76, outperforming the SVM and CART algorithms.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"405 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123364155","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: 1

Analysis of The Impact of The Covid-19 Pandemic on The Performance of Indonesian Non Oil and Gas Exports 新冠肺炎疫情对印尼非石油和天然气出口业绩的影响分析

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.164

I. G. B. N. Diksa, Dewa Ayu Srijayanti

引用次数: 0

Multilevel Analysis: Household and Regional Factors Influence Agricultural Household Poverty in Indonesia, 2019 多层次分析:影响印尼农户贫困的家庭和区域因素，2019

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.173

A. Romadhon, Diyana Indah Sari, B. Wicaksono

{"title":"Multilevel Analysis: Household and Regional Factors Influence Agricultural Household Poverty in Indonesia, 2019","authors":"A. Romadhon, Diyana Indah Sari, B. Wicaksono","doi":"10.34123/icdsos.v2021i1.173","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.173","url":null,"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.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131510838","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

A Simple Approach using Statistical-based Machine Learning to Predict the Weapon System Operational Readiness 使用基于统计的机器学习预测武器系统战备状态的简单方法

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.58

A. D. W. Sumari, Dimas Shella Charlinawati, Yuri Ariyanto

{"title":"A Simple Approach using Statistical-based Machine Learning to Predict the Weapon System Operational Readiness","authors":"A. D. W. Sumari, Dimas Shella Charlinawati, Yuri Ariyanto","doi":"10.34123/icdsos.v2021i1.58","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.58","url":null,"abstract":"Weapon system operational readiness is a critical requirement to ensure the combat readiness in order to guarantee the state defense sustainability time by time. Weapon systems are only operated by the military and their readiness are programmed every year based on some factors such as the amount of the allocated budget, the weapon system strength, and its circulation. Usually, the weapon system readiness is programmed based on the planner’s experiences that are inherited from time to time. In this research, we proposed a simple approach by using statistical-based machine learning method called linear regression for helping the planner to predict the weapon system operational readiness faced to its affecting factors such as scheduled and unscheduled maintenance. We used a dataset from a randomized primary data for 5 years from year 2016 to year 2020 to predict year 2021. To ensure the performance of the model, two measurements are used namely, Mean Absolute Percentage Error (MAPE) to measure its accuracy and goodness, and R-squared (R2) to measure the ability of the independent variables, the weapon system circulation, influences the dependent variable, the weapon system readiness. From the measurement results, the models, in general, are able to achieve MAPE as much as 1.99% that has interpretation as very accurate prediction with the accuracy of 98.02%. On the other hand, the system is able to achieve R2 as much as 84.15% that means the combination of the independent variables altogether have given a strong influence to the dependent variable. The higher the value of R2 the better the model is. Our research conclude that linear regression is the proper machine learning model for predicting the weapon system operational readiness.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131021445","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: 3

Working Age Population and CO2 Emissions in Indonesia: 印尼劳动年龄人口与二氧化碳排放:

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.136

Mirta Dwi Wulandari

{"title":"Working Age Population and CO2 Emissions in Indonesia:","authors":"Mirta Dwi Wulandari","doi":"10.34123/icdsos.v2021i1.136","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.136","url":null,"abstract":"An increase in the working age population causes an increase in consumption which in turn will have an impact on increasing CO? emissions. The household is an element that must be responsible for increasing emissions of greenhouse gases because of their fossil fuels consumption. This study aims to observe the relationship of the working age population and the CO? emissions in households. This study use data from National Socio-Economic Survey (Susenas) 2019 with households consuming gasoline / diesel / kerosene for transportation, and LPG / kerosene for cooking as a unit of analysis. Apart from working age population as the main independent variable, socioeconomic characteristics (household size, income, residential area, poverty, age, sex, education, employment status, and access to modern fuels) are also used as control variables. Multiple regression analysis was used in this study. The results show that the working age population variable is positively correlated to total CO? emissions, transportation-related emissions, and cooking fuels emissions. Respectively, households dominated by members of working age (15-64 years) emitted 8.7%, 12.7%, 3.2% higher than households dominated by non working age (0-14 years and/or 65+ years). Providing sustainable transport system can be the best solution to reduce CO? emissions.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131297839","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: 2