Proceedings of The International Conference on Data Science and Official Statistics最新文献

Development of FASIH Application for the Badan Pusat Statistisk using Flutter Framework 使用 Flutter 框架为 Badan Pusat Statistisk 开发 FASIH 应用程序

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.404

Riofebri Prasetia Prasetia, Lutfi Rahmatuti Maghfiroh Maghfiroh

{"title":"Development of FASIH Application for the Badan Pusat Statistisk using Flutter Framework","authors":"Riofebri Prasetia Prasetia, Lutfi Rahmatuti Maghfiroh Maghfiroh","doi":"10.34123/icdsos.v2023i1.404","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.404","url":null,"abstract":"One of the data collection methods used by the Badan Pusat Statistik (BPS) is Computer Assisted Personal Interviewing (CAPI). Currently, CAPI, known as FASIH, is continuously updated by BPS using the Kotlin programming language, which can run on the Android platform. It is possible that FASIH will be needed in a multiplatform form. However, there is an alternative for multiplatform application development, namely Flutter, which can be used in the development of FASIH. Nevertheless, BPS has not conducted any study on the development of the FASIH application using Flutter, hence the strengths and weaknesses of implementing this technology in FASIH application development remain unknown. Therefore, the author aims to conduct a study on the development of the FASIH application utilizing Flutter. The application development is carried out using the Rapid Application Development (RAD) Prototyping method. The resulting application is tested using black box testing and performance testing using a third-party application, Apptim. The black box testing results indicate that the application meets the functional requirements of stakeholders. In terms of performance, the Kotlin version of FASIH outperforms the Flutter version. However, Flutter has an advantage in accelerating development time. Additionally, concerning user interface development, the Flutter version of the FASIH application can run on multiple platforms. Nevertheless, further integration is required to ensure the proper functioning of the Flutter version of the FASIH application.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143063","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

Comparative Analysis of Retriever and Reader for Open Domain Questions Answering on BPS Knowledge in Indonesian 检索器和阅读器在印尼语 BPS 知识开放域问题解答中的比较分析

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.384

Sulisetyo Puji Widodo

{"title":"Comparative Analysis of Retriever and Reader for Open Domain Questions Answering on BPS Knowledge in Indonesian","authors":"Sulisetyo Puji Widodo","doi":"10.34123/icdsos.v2023i1.384","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.384","url":null,"abstract":"Enumerators from Badan Pusat Statistik (BPS) still often encounter problems in finding solutions to cases encountered during censuses or surveys. Even though knowledge lists have been created and collected in various systems such as QA and knowledge management systems, enumerators still need to find appropriate answers from long and complex knowledge search results. On the other hand, Open-domain Question Answering (OpenQA) is capable of identifying answers to natural questions based on large-scale documents. OpenQA has main components, namely Retriever and Reader. For Retriever tasks, Dense Retrieval (DR) is proven to outperform traditional sparse retrieval such as TF-IDF or BM25. However, other research actually shows that BM25 is superior to DR in terms of accuracy. In this study, we compared DR and BM25 separately and DR+BM25 as a retriever. Additionally, we combine and evaluate several enhanced language models as Readers. In this way, a model with the best combination of Retriever and Reader can be obtained to be implemented in search systems such as QA and knowledge management systems.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143478","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

Development of Paddy Yield Gap Between Java and Outside Java: Does It Have a Contribution to Paddy Yield Improvement from 2018 to 2021? 爪哇岛内外水稻产量差距的发展：它对 2018 至 2021 年水稻产量提高有贡献吗？

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.330

Kadir Ruslan, Octavia Rizky Prasetyo

{"title":"Development of Paddy Yield Gap Between Java and Outside Java: Does It Have a Contribution to Paddy Yield Improvement from 2018 to 2021?","authors":"Kadir Ruslan, Octavia Rizky Prasetyo","doi":"10.34123/icdsos.v2023i1.330","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.330","url":null,"abstract":"Increasing the paddy yield is crucial for Indonesia to maintain its national rice sufficiency amid the consistent depletion of wetland paddy areas. In this regard, the yield disparities between regions are challenging, particularly between Java and outside Java. Our study aims to examine the development of the paddy yield gap between the two regions from 2018 to 2021 and its contribution to paddy yield improvement during the period. Using the results of the National Crop-cutting Survey, we found that while the paddy yield in Java outperformed the paddy yield outside Java, the yield difference between the two regions narrowed from around 26 per cent in 2018 to 22 per cent in 2021 due to the increase of the yield outside Java. The results of the Blinder-Oaxaca decomposition suggested that the narrowing gap has a significant contribution to the national paddy yield increase from 2018 to 2021. Our finding confirms that narrowing the yield gap between the two regions by increasing the yield outside Java is crucial to improving paddy yield in Indonesia. Our study also pointed out that improvement in irrigation systems, fertilizer use, and fertilizer assistance are important factors in maintaining the paddy yield and narrowing the gap.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144612","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

Can Paddy Growing Phase Produce an Accurate Forecast of Paddy Harvested Area in Indonesia? Analysis of the Area Sampling Frame Results 水稻生长阶段能否准确预测印度尼西亚的水稻收获面积？面积抽样框架结果分析

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.316

Kadir Ruslan, Octavia Rizky Prasetyo

{"title":"Can Paddy Growing Phase Produce an Accurate Forecast of Paddy Harvested Area in Indonesia? Analysis of the Area Sampling Frame Results","authors":"Kadir Ruslan, Octavia Rizky Prasetyo","doi":"10.34123/icdsos.v2023i1.316","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.316","url":null,"abstract":"Our study aims to evaluate the accuracy of the forecasts produced based on the paddy growing phase obtained from the results of the Area Sampling Frame (ASF) Survey and, as a comparison, proposes an alternative forecast method taking into account the seasonal pattern and hierarchical structure of the national paddy harvested area estimation obtained from the ASF to improve the accuracy. In doing so, we calculated the MAPE by comparing the realization of paddy harvested area during the period January to September 2022 with their forecasts produced from the area of generative, late vegetative, and early vegetative phases. We also implemented a Hierarchical forecasting method on monthly data of the harvested area from January 2018 to August 2022 for all provinces. Specifically, we applied the bottom-up method for the reconciliation and the rolling window method to produce a three-consecutive month forecast for the period January to September 2022. We found that the accuracy prediction based on the paddy growing phase is moderately accurate. The combination of the bottom-up reconciliation method and the SARIMA model produces a much better accuracy for the national figure of paddy harvested area as shown by a lower MAPE. Our findings suggest that the Hierarchical forecasting method could be an alternative for the prediction of harvested area based on the ASF results other than the prediction obtained from the standing crops.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139145024","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

Role of E-Commerce on Entrepreneurial Welfare in Indonesia 电子商务对印度尼西亚企业家福利的作用

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.397

F. A. Putri

引用次数: 0

Does Farm Size Matter for Food Security Among Agricultural Households? Analysis of Indonesia’s Agricultural Integrated Survey Results 农场规模对农户的粮食安全有影响吗？印度尼西亚农业综合调查结果分析

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.318

Kadir Ruslan, Octavia Rizky Prasetyo

引用次数: 0

Deep Learning Approaches for Predicting Intraday Price Movements: An Evaluation of RNN Variants on High-Frequency Stock Data 预测日内价格变动的深度学习方法：高频股票数据的 RNN 变体评估

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.278

Mochamad Ridwan, Kusman Sadik, F. Afendi

{"title":"Deep Learning Approaches for Predicting Intraday Price Movements: An Evaluation of RNN Variants on High-Frequency Stock Data","authors":"Mochamad Ridwan, Kusman Sadik, F. Afendi","doi":"10.34123/icdsos.v2023i1.278","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.278","url":null,"abstract":"This study discusses the comparison of four recurrent neural networks (RNN) models: Simple RNN, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional RNN (BiRNN), in forecasting minute-level stock price time series data. The performance of these four models is evaluated using the Mean Absolute Percentage Error (MAPE) on a stock dataset from Bank Central Asia (BBCA.JK). The experimental results reveal that the GRU model exhibits the best performance with an average MAPE of 0.0255%, followed by the LSTM model with an average MAPE of 0.0377%. The BiRNN model also demonstrates good performance with an average MAPE of 0.0668%, while the Simple RNN has the highest average MAPE at 0.5118%. This suggests that more complex recurrent architectures like GRU and LSTM have better capabilities in capturing patterns in high-frequency time series data. This study can be expanded by exploring other models such as CNN, conducting tests on diverse datasets, and experimenting with a wider range of hyperparameter variations. Additional variables such as economic indicators, global market data, and social data can also offer a more comprehensive understanding of factors influencing stock prices.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"127 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146457","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

High-resolution-gridded rainfall dataset derived from surface observation by adjustment of satellite rainfall product 通过调整卫星降雨量产品从地表观测得出的高分辨率网格降雨量数据集

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.314

Achmad Rifani, Muhammad Rezza Ferdiansyah

{"title":"High-resolution-gridded rainfall dataset derived from surface observation by adjustment of satellite rainfall product","authors":"Achmad Rifani, Muhammad Rezza Ferdiansyah","doi":"10.34123/icdsos.v2023i1.314","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.314","url":null,"abstract":"A high-resolution-gridded rainfall dataset is essential for many purposes. Such as analysis of extreme weather conditions, natural-disaster mitigation, or to be used as an input to the hydrological model. Satellite-based rainfall products (e.g., Global Satellite Mapping of Precipitation-GSMaP) can solve the spatial and temporal issues despite their rainfall intensity often being under or overestimated. This research aims to provide a high-resolution rainfall dataset by adjusting the 0.1 deg GSMaP rainfall data to the surface rainfall data from several observation points in the greater Jakarta area (Jabodetabek) during January 2020 when several flooding occurred in Jakarta. The adjustment process includes calculating the bias between the satellite estimation in the nearest observation point and interpolating the error back to the 0.01 deg grid by using radial basis function (RBF) to obtain the correction factor in every grid point, GSMaP data then adjusted by the correction factor. We implemented the method in January 2020 when several floods occurred in Jakarta. The result reveals a more realistic rainfall spatial distribution than regularly interpolating the observation data. The validation of adjusted rainfall estimation at the verification points also shows a reduction in domain-wide RMSE by 30 – 80%.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"4 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147888","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

hyper-Poisson Model for Overdispersed and Underdispersed Count Data 超分散和欠分散计数数据的超泊松模型

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.344

V. D. Situmorang, S. Nurrohmah, Ida Fithriani

引用次数: 0

Using Data Science to Assess the Impact of Disaster Event on Climate Change Belief: Case of Australian Bushfire Catastrophe 利用数据科学评估灾害事件对气候变化信念的影响：澳大利亚丛林大火灾难案例

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.413

Diaz Prasetyo, Trisna Mulyati

{"title":"Using Data Science to Assess the Impact of Disaster Event on Climate Change Belief: Case of Australian Bushfire Catastrophe","authors":"Diaz Prasetyo, Trisna Mulyati","doi":"10.34123/icdsos.v2023i1.413","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.413","url":null,"abstract":"Australia, vulnerable to bushfire incidents due to its unique climatic conditions, witnessed a transformative event in the 2019-2020 bushfire season. This research examines the impact of these bushfires on public perception of climate change. Leveraging robust statistical techniques, including McNemar's hypothesis testing and logistic regression, the study deciphers survey data collated pre and post these fires. The study's hypothesis that post-fire respondents are more likely to acknowledge climate change's role is confirmed. Factors such as education, political affiliation, and support for fossil fuel reduction are identified as influential predictors of climate change belief. The analysis also highlights the complex interplay of demographic characteristics and media exposure in shaping attitudes. Notably, direct firebush exposure showed a nuanced relationship with belief. The research underscores a significant shift in Australian attitudes toward climate change following the bushfires. These findings contribute to our understanding of public opinion dynamics and the role of experiential factors in climate change belief.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"90 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139145752","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