Systematic Literature Review: Analisa Sentimen Masyarakat terhadap Penerapan Peraturan ETLE

Syafrial Fachri Pane, Muhammad Syiarul Amrullah
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Abstract

This study examines the efforts to develop a model for analyzing public sentiment regarding applying ETLE (Electronic Traffic Law Enforcement) regulations. The method used is the systematic literature review. A systematic literature review (SLR) consists of three stages: planning, conducting, and reporting. The planning stage is the determination of the SLR procedure. This stage includes preparing topics, research questions, article search criteria & inclusion and exclusion criteria. The conducting stage, namely the implementation, includes searching for articles and filtering articles. The reporting stage is the final stage of SLR. This stage includes writing the SLR results according to the article format. The explanation follows: First, hybrid is the most widely used method in developing sentiment analysis models. Apart from hybrid, several methods are used to develop sentiment analysis models, including multi-task, deep, and machine learning. Each has its advantages and disadvantages in the development of sentiment analysis models. Second, this study shows the development of a model with superior performance, namely using XGBoost as a sentiment analysis model, and the stages it goes through are preprocessing data, handling imbalanced data, and optimizing the model. Therefore, the model for analyzing public sentiment regarding the application of ETLE regulations can be an option for hybrid methods, multi-task learning, deep learning, machine learning, and the XGBoost model to obtain superior performance with preprocessing data stages, handling imbalanced data and optimization models.
本研究考察了为分析公众对应用电子交通执法法规的情绪而开发模型的努力。采用的方法是系统的文献综述。系统性文献综述(SLR)包括三个阶段:计划、实施和报告。计划阶段是确定单反程序。这个阶段包括准备主题,研究问题,文章搜索标准和纳入和排除标准。执行阶段,即实施阶段,包括搜索文章和过滤文章。报告阶段是单反的最后阶段。这个阶段包括根据文章格式编写单反结果。解释如下:首先,hybrid是开发情感分析模型中使用最广泛的方法。除了混合,还有几种方法用于开发情感分析模型,包括多任务、深度和机器学习。在情感分析模型的开发中,每种方法都有其优缺点。其次,本研究展示了一个性能优越的模型的开发,即使用XGBoost作为情感分析模型,它经历了预处理数据、处理不平衡数据和优化模型的阶段。因此,ETLE法规应用舆情分析模型可以选择混合方法、多任务学习、深度学习、机器学习和XGBoost模型,在预处理数据阶段、处理不平衡数据和优化模型方面获得更优的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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