Analysis of School Community Sentiment towards Personal Data Protection Law Using Support Vector Machine (SVM) Method

Gusti Fachman Pramudi, Gerry Firmansyah, Budi Tjahjono, Agung Mulyo Widodo
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Abstract

This research aims to analyze the awareness status of the school community regarding the right to personal data protection, test and analyze the sentiments of the school community towards the implementation of the Personal Data Protection Law and as a means of outreach regarding personal data protection laws in the world of education, especially school community besides that to find out whether the Support Vector Machine method can be used as a method in conducting Sentiment Analysis research. The results of this study can be concluded that only 38% of the school community knows about this regulation while the other 62% still don't know much about this rule. Then for the use of the Support Vector Machine method which has been carried out five (5) trials using different variations of training data and test data produces an average accuracy rate of 85.97% with the highest results on training data and test data 50% - 50% that is equal to 88.00% and the lowest result is in the experiment of training data and test data of 90% - 10% which is equal to 84.44%. For the school community's sentiment towards the Personal Data Protection Act, it was 56% or as many as 496 of 887 words. which shows a neutral response and 8% or as many as 72 out of 887 sentiment words show a negative response.
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基于支持向量机(SVM)方法的学校社区对个人数据保护法的情绪分析
本研究旨在分析学校社区对个人数据保护权利的意识状况,测试和分析学校社区对《个人数据保护法》实施的情绪,以及作为教育界特别是学校社区对个人数据保护法律的外展手段,以了解支持向量机方法是否可以作为进行情感分析研究的方法。本研究的结果可以得出结论,只有38%的学校社区知道这一规定,而其他62%的人仍然不太了解这一规定。然后使用支持向量机方法对训练数据和测试数据进行了五(5)次试验,平均准确率为85.97%,其中训练数据和测试数据的最高结果为50% - 50%,等于88.00%,训练数据和测试数据的最低结果为90% - 10%,等于84.44%。对于“个人信息保护法”,学校团体的意见为56%,887个单词中有496个。在887个情感词中,有72个是否定的,占8%。
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