Implementasi Metode Naïve Bayes untuk Analisis Sentimen Warga Jakarta Terhadap

Afrizal, S; Irmanda, Helena Nurramdhani; Falih, Noor; Isnainiyah, Ika Nurlaili

doi:10.52958/iftk.v15i3.1454

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Analisis sentimen bertujuan untuk menganalisis opini, sentimen dan emosi yang terdapat pada dokumen atau data. Tugas dasar dari analisis sentimen adalah untuk mengelompokan sifat dari teks yang ada di dalam kalimat maupun pendapat [3].…”

Section: Pendahuluanunclassified

Analisis Sentimen HateSpeech pada Pengguna Layanan Twitter dengan Metode Naïve Bayes Classifier (NBC)

2023

View full text Add to dashboard Cite

Twitter users in January 2023 increased by 27.4% over the prior-year period worldwide. One of the uses of Twitter as a social networking service is used to create HateSpeech. HateSpeech fosters hatred and hostility of certain individuals or groups (ethnic, religious, racial and inter-group) in the form of insults, defamation, blasphemy, provocation, incitement, and spreading false news. The solution to avoid divisions between factions that threaten the unity of the Indonesian nation needs to be done with sentiment analysis to group tweets that are included in HateSpeech or Non-HateSpeech. This study aims to analyze the HateSpeech sentiment of twitter service users with the Naïve Bayes Classifier method. The dataset used in this study was 5000 data processed with Python tools for the preprocessing stages (case folding, tokenization, stopword removal, normalization, dan stemming), Labeling, Term Weighting weighting with Term Frequency (TF), and Inverse Document Frequency (IDF). The K-Fold Cross Validation technique is carried out to validate data by dividing training and testing data into 3 test scenarios, namely 70% training data and 30% testing, 30% training data and 70% testing, and 50% training data and 50% testing of the Naïve Bayes Classifier Method. Based on evaluation using Confusion Matrix, the highest accuracy of 80%, precision 100%, recall 80%, and F1-Score 89% was obtained in the training data testing scenario of 70% and testing 30%.

show abstract

Section: Pendahuluanunclassified

Analisis Sentimen HateSpeech pada Pengguna Layanan Twitter dengan Metode Naïve Bayes Classifier (NBC)

2023

View full text Add to dashboard Cite

show abstract

“…Besarnya pengaruh dan manfaat dari analisis sentimen menyebabkan penelitian dan aplikasi berbasis analisis sentimen berkembang pesat. Bahkan di Amerika terdapat sekitar 20-30 perusahaan yang memfokuskan pada layanan analisis sentimen (Afrizal, 2019). Penelitian ini membahas bagaimana cara memanfaatkan opini pengguna Twitter terhadap film untuk analisis sentimen, kemudian membagi opini tersebut menjadi data sentimen positif, negatif dan netral.…”

Section: Pendahuluanunclassified

Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes

Nurtikasari

Alam

Hermanto

2022

INSOLOGI: Jurnal Sains dan Teknologi

View full text Add to dashboard Cite

Film is one of the most interesting topics to talk about. When someone writes an opinion on a film, all the elements in the film will be written down. Film opinion data in this study were taken from film comments written on twitter. The number of opinions written on Twitter requires classification according to the sentiments they have so that it is easy to get the tendency of the opinion towards the film whether it tends to have a positive, negative or neutral opinion. Recently, the spotlight on twitter media in Indonesia is a film with the title Horror-Ngeri Sedap. Ngeri-Ngeri Sedap is a 2022 Indonesian comedy-drama film directed and written by Bene Dion Rajagukguk. The film is set in the Batak Tribe, starring Arswendy Beningswara Nasution, Tika Panggabean, Boris Bokir Manullang, Gita Bhebhita Butar-butar, Lolox, and Indra Jegel. This causes differences in the views and opinions of twitter users towards the Horror Sedap film. So it is necessary to have a sentiment classification for the opinion. The use of the Naïve Bayes Algorithm was chosen in the analysis because it has the highest probability or opportunity value for data classification. Labeling on Twitter data is done manually by giving positive, negative, neutral sentiments to the raw dataset in Microsoft excel then the data enters the preprocessing transformation, tokenization and filtering stages. The tf-idf weighting is carried out when the data is complete in the transformation process, tf-idf is used to determine the number of occurrences of words, then the data classification is carried out using the Naïve Bayes Algorithm. confusion matrix testing is done after the data classification is complete using the orange tools. Based on the test results with the confusion matrix with orange tools, the average accuracy value is 0.65% and the precision value is 0.67%, and recall is 0.65%, and the percentage is neutral 0.83% in its classification. This proves that the public sentiment on the twitter platform towards the case of the film Horror Sedap is neutral and the Naïve Bayes Algorithm is considered reliable and valid in data processing.

show abstract

“…Naïve Bayes merupakan salah satu algoritma yang dapat digunakan pada klasifikasi data khususnya pada klasifikasi teks [19]. Algoritma tersebut tersebut terbukti dapat memperoleh hasil yang cukup memuaskan jika digunakan untuk klasifikasi teks [20], [21]. Salah satu varian dari Naïve Bayes yang digunakan dalam klasifikasi teks yaitu Naïve Bayes Multinomial.…”

Section: Pendahuluanunclassified

Analisis Sentimen Produk Kecantikan Jenis Serum Menggunakan Algoritma Naïve Bayes Classifier

Hamka

Alfatari

Sari³

2022

json

View full text Add to dashboard Cite

The increased consumption of beauty products as a lifestyle has increased public opinion on the beauty products used. Generally, reviews are given through posts on social media. This study discusses the classification of sentiment analysis on the use of serum beauty products on Twitter using the Naïve Bayes Multinomial algorithm. Sentiment analysis of serum beauty products is carried out to provide information and preferences to the public regarding the quality of a product. The results of the information and preferences become a reference for consideration in choosing the appropriate serum beauty product. The data used in this study were 27,587 tweets using three keywords, namely "serum," "face serum", and "beauty serum". Tweet data is divided into training data and test data with the number of training data as much as 22,070 tweets and test data as much as 5,518 tweets. The data is categorized using the lexicon senticnet 7 dictionary based on polarity values. The results of the analysis of positive sentiment are 35%, negative sentiment is 63.8%, and neutral sentiment is 1.2%. The classification results using Naïve Bayes Multinomial obtain the highest accuracy value of 80%. The Confusion Matrix results get the highest precision value of 88%, the highest recall of 81%, and the highest f1-Score of 86%.

show abstract

Implementasi Metode Naïve Bayes untuk Analisis Sentimen Warga Jakarta Terhadap

Cited by 4 publications

References 0 publications

Analisis Sentimen HateSpeech pada Pengguna Layanan Twitter dengan Metode Naïve Bayes Classifier (NBC)

Analisis Sentimen HateSpeech pada Pengguna Layanan Twitter dengan Metode Naïve Bayes Classifier (NBC)

Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes

Analisis Sentimen Produk Kecantikan Jenis Serum Menggunakan Algoritma Naïve Bayes Classifier

Contact Info

Product

Resources

About