ANALISIS PERBANDINGAN METODE KLASIFIKASI LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE HALAMAN SAMPUL (STUDI KASUS: ANALISIS SENTIMEN TWITTER TERHADAP GAME FIFA 21)

Hanief Muhiburrahman, NIM.: 19106050029 (2023) ANALISIS PERBANDINGAN METODE KLASIFIKASI LOGISTIC REGRESSION DAN SUPPORT VECTOR MACHINE HALAMAN SAMPUL (STUDI KASUS: ANALISIS SENTIMEN TWITTER TERHADAP GAME FIFA 21). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

The game industry is growing rapidly in this era where technology is developing very rapidly. FIFA 21 is a game that has many fans around the world. In the game industry, feedback from gamers is needed to improve the quality of games so that the continuity of the game business can be maintained. Electronic Arts (EA) as the developer of the game FIFA 21 needs feedback from gamers to improve the quality of their games to be applied to the FIFA 21 game update or the next FIFA game. Sentiments on the Twitter application can be game feedback by users which can be used as a dataset by game developers To classify sentiment into several classes, a classification method is needed. There are several classification methods, such as Naïve Bayes, Logistic Regression, SVM, Random Forest, and others. We can choose a method where the method gives maximum and effective results. In one case, we are confused in choosing a classification method. A comparison between X and Y classification methods can be made to solve this problem. In this study, a comparison was made between the Logistic Regression and Support Vector Machine (SVM) classification methods. Comparison of the two methods aims to identify which model has optimal performance based on the resulting accuracy. The choice of Logistic Regression and Support Vector Machine (SVM) methods in this study is based on the reason that Logistic Regression is a classification method that is proven to be effective, efficient and quite good at handling correlated features. Meanwhile, SVM is a method that has the ability to deal with high-dimensional features.

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