Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category)

Ripto Mukti Wibowo, Bahjat Fakieh, M. S. Ramzan, A. Alzahrani, M. Siddiqui, B. Alzahrani
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Abstract

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 percent.In this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities.
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开放数据的可视化和分析模型(案例:选举选民和儿童残疾类别)
由于疫情大流行,包括印度尼西亚在内的几个地区的领导人选举不得不推迟,原因是新冠肺炎大流行。印度尼西亚的几个大城市因其庞大的人口和GDP而受到关注。本研究对印度尼西亚一个城市的开放数据数据集进行了分析和测试。除了对区域首长选举进行研究外,我们还提供残疾儿童类别选民的信息。本研究中使用的步骤是在区域负责人选举中使用泗水市的区域地图数据。下载区域首长选举及残疾儿童类别的数据或数据集。根据数据集,来自残疾儿童类别的选民超过5%。在本研究中,我们使用Python来处理我们的数据集和大数据技术。数据清理或清理,探索性数据分析,经验累积分布函数(ECDF)也需要在python。ECDF图结果稳定增长(增量0.1)。方差值最大的是第5选区= 6.090,最小的是第4选区= 0.90。开放数据的结果是图形数据可视化和候选人分数,以帮助作为2024年地区负责人选举和残疾儿童类别的替代方案。
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