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Gerliandeva, Alfin
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Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media

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Optimasi Klasifikasi Sentimen pada Komentar Online menggunakan Multinomial Naïve Bayes dan Ekstraksi Fitur TF-IDF serta N-grams Gerliandeva, Alfin; Chrisnanto, Yulison; Ashaury, Herdi
Jurnal Pekommas Vol 9 No 2 (2024): Desember 2024
Publisher : Sekolah Tinggi Multi Media “MMTC” Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jpkm.v9i2.5585

Abstract

The Naïve Bayes (NB) algorithm is a classifier method that calculates simple probabilities and it is suitable for text classification in the context of sentiment analysis. The classic variant of NB is Multinomial Naïve Bayes (MNB). The weakness of the MNB algorithm is the assumption of feature independence. This research uses a dataset of comments and reviews from various online platforms. This study uses the proposed method to handle the weakness of the MNB algorithm, namely the use of TF-IDF feature extraction and N-grams (1-gram to 5-gram), and the use of Chi-Square feature selection, as well as handling dataset imbalance using SMOTE (oversampling and undersampling method). The results of this study show that the use of pentagram (5-gram) with data that has been oversampled by SMOTE produces the highest accuracy value of 94% and an Area Under Curve (AUC) value of 100%
Co-Authors Ashaury, Herdi Yulison Herry Chrisnanto