Calvin, Calvin (2024) Analisis sentimen Youtube terhadap komentar toxic pada music video "HOZIER take me to church" dengan metode naive bayes. Bachelor thesis, Universitas Pelita Harapan.
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Abstract
Social media platforms like YouTube have significantly evolved in this
digital era. People from all over the world use YouTube to watch videos and share
their opinions, comments, and feelings about the videos they watch. This research
focuses on sentiment analysis to understand the patterns of negative comments
that may appear in the comments section of the videos. The data to be analyzed is
obtained from the YouTube API and will use the Naive Bayes algorithm, along
with performance evaluation of accuracy, precision, recall, and F1-score. The
classification stages in this research include text preprocessing, data training, data
testing, and evaluation. The accuracy performance results obtained from the Naive
Bayes algorithm using the confusion matrix were 74%. The negative precision
result is 96%, neutral precision is 97%, and positive precision is 64%. The
negative recall result is 57%, neutral recall is 49%, and positive recall is 99%. The
negative F1-score result is 71%, neutral F1-score is 65%, and positive F1-score is
78%. These numbers show that the model is very good at identifying positive
samples (99% recall), although it has challenges in detecting negative and neutral
samples. High precision for the negative and neutral classes shows good
prediction accuracy in these classes. Overall, the higher F1-score performance in
the positive class confirms the efficiency of the model in handling predictions for
the positive class. / Platform media sosial seperti Youtube sangat berkembang di era digital ini.
Banyak orang dari seluruh dunia menggunakan youtube untuk menonton video
dan juga berbagi pendapat, komentar, dan perasaan mereka tentang video yang di
tonton. Penelitian ini berfokus pada analisis sentimen untuk memahami pola
komentar negatif yang mungkin muncul di kolom komentar video tersebut. Data
yang akan di analisis di dapatkan dari Youtube API dan akan menggunakan
algoritma Naive Bayes dan juga akan melakukan evaluasi performa akurasi,
presisi, recall, dan f1-score. Tahap-tahap klasifikasi dalam penelitian ini
mencakup text-preprocessing, data training, data testing hingga evaluasi. Hasil
performa akurasi yang di dapatkan dari algoritma naive bayes dengan
menggunakan confusion matrix adalah 74%. Hasil presisi negatif adalah 96%,
presisi netral adalah 97% dan presisi positif adalah 64%. Hasil recall negatif
adalah 57%, recall netral adalah 49% dan recall positif adalah 99%. Hasil f1-
score negatif adalah 71%, f1-score netral adalah 65% dan f1-score positif adalah
78%. Angka-angka ini menunjukkan bahwa model sangat baik dalam
mengidentifikasi sampel positif (recall 99%), meskipun memiliki tantangan dalam
mendeteksi sampel negatif dan netral. Presisi yang tinggi untuk kelas negatif dan
netral menunjukkan akurasi prediksi yang baik pada kelas tersebut. Secara
keseluruhan, performa F1-score yang lebih tinggi pada kelas positif menegaskan
efisiensi model dalam menangani prediksi untuk kelas positif.
Item Type: | Thesis (Bachelor) |
---|---|
Creators: | Creators NIM Email ORCID Calvin, Calvin NIM03082190028 tancalvin44@gmail.com UNSPECIFIED |
Contributors: | Contribution Contributors NIDN/NIDK Email Thesis advisor Romindo, Romindo NIDN0111119101 romindo@uph.edu |
Uncontrolled Keywords: | Analisis sentimen ; Naive Bayes |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | University Subject > Current > Faculty/School - UPH Medan > School of Information Science and Technology > Informatics Current > Faculty/School - UPH Medan > School of Information Science and Technology > Informatics |
Depositing User: | Calvin Calvin |
Date Deposited: | 10 Aug 2024 14:30 |
Last Modified: | 10 Aug 2024 14:30 |
URI: | http://repository.uph.edu/id/eprint/64745 |