Proceedings of The International Conference on Data Science and Official Statistics最新文献_第5页

Spatial Analysis of Crime in East Java Province in 2019 2019年东爪哇省犯罪空间分析

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.227

Choirul Ummah, R. Rahani

引用次数: 0

Measuring The Economic Contribution of Tourism: An Improvement in Indonesia 衡量旅游业的经济贡献:印尼的进步

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.187

Akhmad Mun’im

{"title":"Measuring The Economic Contribution of Tourism: An Improvement in Indonesia","authors":"Akhmad Mun’im","doi":"10.34123/icdsos.v2021i1.187","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.187","url":null,"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.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"15 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":"123710241","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

Development of Question Answering System for Public Relation Division in Student Admission 招生公关部问答系统的开发

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.81

Lutfi Rahmatuti Maghfiroh, Wahyu Syahputra, Ibnu Santoso

{"title":"Development of Question Answering System for Public Relation Division in Student Admission","authors":"Lutfi Rahmatuti Maghfiroh, Wahyu Syahputra, Ibnu Santoso","doi":"10.34123/icdsos.v2021i1.81","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.81","url":null,"abstract":"Politeknik Statistika STIS (Polstat STIS) holds the new students' admission (PMB) every year which aims to gather, test, and, select all of its applicants who want to continue their study at STIS. STIS establish a committee during this event named Public Relation (PR) Division. PR Division to be intermediaries between STIS and the applicants. One of many PR Division tasks is to reply to all the questions from applicants about administration, procedure, or other things about PMB and STIS. PR Division is facing some problems that can hinder its performance to do the tasks. How do we address the problem is the reason that this research begins in the first place. The goal of this research is to build and establish a web-based system that is capable to solve all the problems the current system has. The system is divided into two main functions, the first one is FAQ management by PR Division members. The other function is a chatbot that automatically answers the question by using the TF-IDF algorithm. The conclusion on all testing and evaluation is the system that being build is already fulfilled all its requirements also the system is feasible to be used.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"480 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":"122726486","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

Learning Bayesian Network for Rainfall Prediction Modeling in Urban Area using Remote Sensing Satellite Data (Case Study: Jakarta, Indonesia) 利用遥感卫星数据学习贝叶斯网络建立城市降水预测模型(以印度尼西亚雅加达为例)

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.37

Salwa Rizqina Putri, A. Wijayanto

{"title":"Learning Bayesian Network for Rainfall Prediction Modeling in Urban Area using Remote Sensing Satellite Data (Case Study: Jakarta, Indonesia)","authors":"Salwa Rizqina Putri, A. Wijayanto","doi":"10.34123/icdsos.v2021i1.37","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.37","url":null,"abstract":"Rainfall modeling is one of the most critical factors in agricultural monitoring and statistics, transportation schedules, and urban flood prevention. Weather anomaly during the dry season in urban coastal areas of tropical countries such as Jakarta, Indonesia has become a challenging issue that causes unexpected changes in rain patterns. In this paper, we propose the Bayesian Network (BN) approach to model the probabilistic nature of rain patterns in urban areas and causal relationships among its predictor variables. Rain occurrences are predicted using temperature, relative humidity, mean-sea level (MSL) pressure, cloud cover, and precipitation variables. Data are obtained from the remote sensing sources of the National Oceanic and Atmospheric Administration (NOAA) satellite in Jakarta 2020-2021. We compare both of the score-based, i.e., Hill Climbing (HC), and hybrid structure learning algorithms of Bayesian Network including the techniques of Max-Min Hill Climbing (MMHC), General 2-Phase Restricted Maximization (RSMAX2), and Hybrid-Hybrid Parents & Children (H2PC). Further, we also compare the performance of score-based model (Hill Climbing) under five different popular scorings: Bayesian Information Criterion (BIC), K2, Log-Likelihood, Bayesian Dirichlet Equivalent (BDE), and Akaike Information Criterion (AIC) methods. The main contributions of this study are as follows: (1) insights that the hybrid structure learning algorithms of Bayesian Network models are either superior in performance or at least comparable to its score-based counterparts (2) our proposed best performed Bayesian Network model that is able to predict the rain occurrences in Jakarta with a promising overall accuracy of more than 81 percent.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"49 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":"116740888","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: 12

Topic Modelling in Knowledge Management Documents BPS Statistics Indonesia 知识管理文档中的主题建模BPS统计印度尼西亚

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.52

Muhammad Yunus Hendrawan, Nucke Widowati Kusumo Projo

{"title":"Topic Modelling in Knowledge Management Documents BPS Statistics Indonesia","authors":"Muhammad Yunus Hendrawan, Nucke Widowati Kusumo Projo","doi":"10.34123/icdsos.v2021i1.52","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.52","url":null,"abstract":"Knowledge management is an important activity in improving the performance an organization. BPS Statistics Indonesia has recently implemented such a system to improve the quality and efficiency of business processes. The purposes of this research are: 1) implementing topic modelling on BPS Knowledge Management System to identify groups of document topics; 2) providing recommendations on which the best topic modelling; 3) building a web service function of topic modelling for BPS that includes data preprocessing function and topic group recommendation function. This study applies the Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) topic modelling methods to determine the best grouping techniques for knowledge management systems in BPS Statistics Indonesia. The results show that the LDA model using Mallet is the best model with 25 topic groups and a coherence score of 0.4803. The performance result suggest that the best modelling method is the LDA. The LDA model is then successfully implemented in RESTful web service to provide services in the preprocessing function and topic recommendations on documents entered into the Knowledge Management System BPS.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"83 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":"130337778","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

Individual and Province-level Determinants of Unemployed NEET as Young People’s Productivity Indicator in Indonesia During 2020: A Multilevel Analysis Approach 作为2020年印度尼西亚青年生产力指标的失业啃老族的个人和省级决定因素:多层次分析方法

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.102

Ni Putu Gita Naraswati, Yogo Aryo Jatmiko

{"title":"Individual and Province-level Determinants of Unemployed NEET as Young People’s Productivity Indicator in Indonesia During 2020: A Multilevel Analysis Approach","authors":"Ni Putu Gita Naraswati, Yogo Aryo Jatmiko","doi":"10.34123/icdsos.v2021i1.102","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.102","url":null,"abstract":"Nowadays, employment has become one of the focus of attention for developing countries, including Indonesia. This is one of the urgencies that must be addressed considering that the Indonesian population is entering the demographic divident period. Success in achieving the demographic divident is very dependent on the employment conditions of young people in realizing a low level of dependence. However, obstacles in terms of education and employment are still experienced by youth which can be seen from the percentage of NEET from Year-on-Year (YoY), especially in 2020 it is exacerbated by Covid-19 pandemic. Based on these problems, it is necessary to research NEET in Indonesia in 2020. This study uses 2020 National Labor Force Survey (Sakernas) data which is analyzed by using multilevel binary logistic regression analysis. The unemployed status of young NEETs is influenced by gender, age, marital status, highest education completed, disability status, classification of the area of residence, and recent migrant status. There is a multilevel effect in the NEET assessment of young people as evidenced by the influence of Gross Domestic Product (GDP) and Human Development Index (HDI). The research results are expected to be used as a reference in making policies to optimizing the mismatch program on the pre-employment card to bridge the young age of job seekers with available job opportunities and based on the province-level variable, the province government are expected to maximize the province-level variables that affect the tendency of NEETs to remain active in the labor market. that are targeted towards the NEET problem in Indonesia.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"136 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":"115894653","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: 5

The Best K-Exponential Moving Average with Missing Values: Gold Prices in Indonesia, Saudi Arabia, and Turkey during COVID-19 缺失值的最佳k指数移动平均线:印度尼西亚、沙特阿拉伯和土耳其在2019冠状病毒病期间的黄金价格

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.42

Fadhlul Mubarak, Atilla Aslanargun, I. Siklar

引用次数: 0

Short-Term Forecasting of Air Travellers Outflows from Bali Using Web Search Data 利用网络搜索数据对巴厘岛航空旅客外流的短期预测

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.122

Parma Dwi Widy Oktama

{"title":"Short-Term Forecasting of Air Travellers Outflows from Bali Using Web Search Data","authors":"Parma Dwi Widy Oktama","doi":"10.34123/icdsos.v2021i1.122","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.122","url":null,"abstract":"Air travelers have become one of the strategic indicators in the transportation sector. The official data-released by Statistics Indonesia (BPS) for thirty days-lag, makes the condition of this indicator can’t be known in real-time. By the utilization of web search data that has been briskly evolving in recent years, this study aims to explore the possibility of using web search data in performing short-term forecasting to know the general outlook of the indicator earlier. Based on this study, web search data and official statistics figures show a strong correlation and having similar movement patterns over time. The application of web search data as a predictor in time series modeling, especially on time series regression and autoregressive model (SARIMA and SARIMAX), turn out a predicted value that well-approach the actual value of the response variable. In addition, it is proven that the use of web search data can increase model accuracy. The analysis results using SARIMAX model shows that the number of air traveller’s outflows from Bali in September and October 2021 will generally be higher than the number in August 2021. The increasing number of air travelers is thought due to a decrease in Covid-19 cases which has triggered the public's confidence in travelling about to rise again.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"96 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":"132196314","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

Estimation of Total Fertility Rate (TFR) Using Small Area Estimation (SAE) in Nusa Tenggara Timur (NTT) Province 利用小面积估算法估算努沙登加拉帖木儿省总生育率(TFR

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.107

Mellinda Mellinda, C. Sumarni

{"title":"Estimation of Total Fertility Rate (TFR) Using Small Area Estimation (SAE) in Nusa Tenggara Timur (NTT) Province","authors":"Mellinda Mellinda, C. Sumarni","doi":"10.34123/icdsos.v2021i1.107","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.107","url":null,"abstract":"The large population in Indonesia has an impact on providing basic services for population which is not optimal so the condition and distribution of the population in a country must be addressed through fertility control methods. Total Fertility Rate (TFR) is one of fertility measures used in Indonesia. The estimation of TFR at the district level is very important, especially for the Nusa Tenggara Timur (NTT) Province as the province with the highest TFR in Indonesia. The availability of TFR data up to the district level is difficult to obtain every year due to data limitations. This study uses the National Socio-Economic Survey to address these problems. TFR estimation through survey data (direct estimation) generally results in a large Relative Standard Error (RSE) value, so it is necessary to estimate using an indirect estimate in the form of Small Area Estimation (SAE). By using SAERestricted Maximum Likelihood (REML) procedure, TFR with an RSE that is lower than the direct estimate is obtained. There are 5 district that have a medium-high TFR, namely: Sumba Barat Daya, Sumba Tengah, Sabu Raijua, Sumba Barat, and Manggarai Barat. The government is recommended to focus more on that 5 districts to suppress the high TFR in NTT.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"26 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":"134103718","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

Determining the Stopping Point on GPS Data Using Density Based Spatial Clustering of Application with Noise and Gaussian Mixture Model Cluster 基于噪声和高斯混合模型聚类的密度空间聚类方法确定GPS数据的停止点

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.123

Y. Faeni

引用次数: 0