KOMPARASI ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM ANALISIS SENTIMEN NETIZEN TERHADAP TURNAMEN MOBILE LEGENDS "MPL XIV" PADA PLATFORM X DI INDONESIA

HUBERT, RAFFI AHMAD (2025) KOMPARASI ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM ANALISIS SENTIMEN NETIZEN TERHADAP TURNAMEN MOBILE LEGENDS "MPL XIV" PADA PLATFORM X DI INDONESIA. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The e-sports industry in Indonesia has been growing rapidly, as evidenced by the public’s enthusiasm for the Mobile Legends Professional League (MPL) Season XIV tournament. Social media, particularly platform X (formerly Twitter), serves as a primary channel for netizens to express opinions, criticisms, and support regarding the tournament. The data in this study underwent several pre-processing steps, including case folding, normalization, stopword removal, stemming, and translation before sentiment labeling (positive, neutral, negative). Naive Bayes and Support Vector Machine (SVM) algorithms were applied, using an 80% training and 20% testing data split, with TF-IDF feature extraction. The results show that SVM outperformed Naive Bayes with an accuracy of 78% and high stability, surpassing Naive Bayes’ 72% accuracy. Public sentiment was predominantly positive, followed by neutral, with negative being the least, reflecting a generally supportive response to the tournament. Keyword analysis revealed differences in sentiment expression: criticisms related to team performance appeared in negative sentiment, enthusiastic support in positive sentiment, and factual information in neutral sentiment. In conclusion, SVM proved to be more effective and accurate for sentiment analysis of social media text data. Kata Kunci: MPL XIV, Naive Bayes, sentiment analysis, Support Vector Machine, X platform Perkembangan industri e-sports di Indonesia semakin pesat, salah satunya terlihat dari antusiasme publik terhadap turnamen Mobile Legends Professional League (MPL) Season XIV. Media sosial, khususnya platform X (sebelumnya Twitter), menjadi sarana utama bagi netizen dalam menyampaikan opini, kritik, dan dukungan terhadap turnamen tersebut. Data pada penelitian ini melalui proses preprocessing, termasuk case folding, normalisasi, stopword removal, dan stemming, dan translating sebelum pelabelan sentimen (positif, netral, negatif). Algoritma Naive Bayes dan Support Vector Machine (SVM) diterapkan, dengan pembagian data 80% pelatihan dan 20% pengujian, serta ekstraksi fitur TF-IDF. Hasil menunjukkan SVM unggul dengan akurasi 78% dan stabilitas tinggi, melampaui Naive Bayes yang mencapai 72%. Sentimen publik didominasi positif, diikuti netral, dan negatif paling sedikit, mencerminkan respons umum yang mendukung turnamen. Analisis kata kunci mengungkapkan perbedaan ekspresi sentimen: kritik terkait performa tim pada sentimen negatif, dukungan antusias pada positif, dan informasi faktual pada netral. Kesimpulannya, SVM terbukti lebih efektif dan akurat untuk analisis sentimen data teks media sosial. Kata Kunci: analisis sentimen, MPL XIV, media sosial X, Naive Bayes, Support Vector Machine

Item Type:	Thesis (S1)
Call Number CD:	FIK/SI. 25 058
NIM/NIDN Creators:	41821010008
Uncontrolled Keywords:	analisis sentimen, MPL XIV, media sosial X, Naive Bayes, Support Vector Machine
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.7 Multimedia Systems/Sistem-sistem Multimedia > 006.75 Social Multimedia/Multimedia Social > 006.754 Online Social Network/Situs Jejaring Sosial, Sosial Media 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Sistem Informasi
Depositing User:	khalimah
Date Deposited:	15 Aug 2025 01:26
Last Modified:	15 Aug 2025 01:26
URI:	http://repository.mercubuana.ac.id/id/eprint/96799

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