RASYID, MUHAMMAD RADIAN (2023) ANALISIS SENTIMEN KEPUASAN MASYARAKAT TERHADAP KINERJA EKSPEDISI DI INDONESIA. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The main objective of this study is to compare Multinomial Naive Bayes and Adaptive Boosting algorithm performance that used individually to new proposed method Bagging Classifier that in theory can improve the performance of two algorithms. Dataset was taken from Twitter using username as the search query. The dataset was tested using three algorithms and then compared the best overall performance of the three, using automatic labelling and word cloud to detect words that frequently occurs in dataset. The best result was obtained by using Bagging Classifier algorithm with an accuracy rate of 96.69% dan an f1-score of 96.64%. Keywords: Sentiment Analysis, Bagging Classifier, Natural Language Processing. Tujuan utama dari penelitian ini adalah membandingkan algoritma Multinomial Naive Bayes dan Adaptive Boosting yang digunakan masing-masing dengan Bagging Classifier yang mampu meningkatkan kinerja dari kedua algoritma tersebut. Dataset diambil dari Twitter dengan menggunakan username sebagai search query. Dataset diuji dengan menggunakan tiga algoritma tersebut lalu membandingkan performa terbaik dari ketiganya, digunakan label automatis dan word cloud untuk mendeteksi kata yang sering muncul di dalam dataset. Hasil terbaik diperoleh dengan menggunakan algoritma Bagging Classifier dengan tingkat akurasi sebesar 96.69% dan f1-score sebesar 96.64%. Kata kunci : Analisis Sentimen, Bagging Classifier, Natural Language Processing
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