Analisis Sentimen Pada Twitter Mengenai Pasca Bencana Menggunakan Metode Naïve Bayes Dengan Fitur N-Gram

Rozi, Imam Fahrur; Firdausi, Annisa Taufika; Islamiyah, Khalimatul

doi:10.33795/jip.v6i2.316

Cited by 6 publications

(5 citation statements)

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“…II.5 SENTIMENT ANALYSIS Sentiment Analysis, is the process of analyzing, understanding, and classifying opinions, evaluations, judgments, attitudes, and emotions towards a particular entity such as products, services, organizations, individuals, events, topics, in order to obtain information [18]. Mood analysis in opinion is called Sentiment Analysis, which refers to the automatic analysis of evaluative texts by focusing on classifying texts based on their polarity.…”

Section: Results and Conclusionmentioning

confidence: 99%

Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition

Tarigan¹,

Gunawan²,

Hayadi

2023

ICoSTEC

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Javanese script is one of Indonesia's cultural heritages that are increasingly rarely used today. The difficulty of recognizing the shapes of letters, let alone writing them, is the main obstacle in using the Hanacaraka script. This research offers an alternative to Hanacaraka script recognition using a combination of image feature extraction and machine learning, where we utilize a pre-trained SquzeeNet model and Multilayer Backpropagation algorithm. Of the 18 models built using ReLu, Sigmoid, and Tanh activation functions, we found that the Tanh activation function, using the combination of 50-50-100 neuron configuration and 25 epochs, was the most optimal function used to classify the training data with accuracy, precision, and recall values of 93.8%. Meanwhile, the Tanh activation function, using the 50-100-50 neuron configuration and 50 epochs, is the most optimal function to classify the testing data, with accuracy, precision, and recall values of 89.1%, 89.5%, and 89.5%. All built models show a training and testing performance ratio below 10%. From this result, we conclude that all models have good reliability in the training and testing classification process.

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Section: Results and Conclusionmentioning

confidence: 99%

Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition

Tarigan¹,

Gunawan²,

Hayadi

2023

ICoSTEC

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“…Data pada penelitian ini bersumber dari cuitan masyarakat di Layanan Twitter. Developer Twitter menyediakan Application Programming Interface (API) [26] untuk pengambilan data tersebut. Data yang berhasil ditemukan pada penelitian ini sebanyak 5000 tweet, yang terdiri dari tanggal, nama pengguna, dan tweet.…”

Section: Metode Pengumpulan Dataunclassified

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

2023

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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%.

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“…The language used daily is not a collection of individual words but a collection of sequences of individual words that have meaning [14]. N-Gram is a combination of unigram and bigram, so the features obtained from both will be used as one [15].…”

Section: N-grammentioning

confidence: 99%

Optimal Number Data Trains in Hoax News Detection of Indonesian using SVM and Word2Vec

Asramanggala,

Prasetyowati,

Sibaroni

2023

bits

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Along with the development of the era of technological development also has an increase. Information dissemination occurs very quickly on social media, especially Twitter. On Twitter, only some news circulating is necessarily accurate information. Lots of information that is spread is hoax news that irresponsible individuals apply. In this research, the author will build a system to determine the optimal amount of data trained in the hoax news classification process. In this study, the authors will use the support vector machine and word2vec algorithms to classify hoax and non-hoax news on the system to be created. In this study, five experiments were carried out with the number of train data used as many as 5000, 10000, 15000, 20000, and 25000. 5000 data train results in an accuracy of 77.28%, 10000 data train produce an accuracy of 79.68%, data 15,000 trains produce an accuracy of 79.892%, 20,000 data trains produce an accuracy of 80,416%, and 25,000 data trains produce an accuracy of 81,184%, by using a combination of unigram with token full token selection. This research aims to build a hoax detection system that can determine the optimal amount of data training to use. Also, this research is used to see the performance of the Support Vector Machine algorithm with Word2Vec in detecting hoax news

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Analisis Sentimen Pada Twitter Mengenai Pasca Bencana Menggunakan Metode Naïve Bayes Dengan Fitur N-Gram

Cited by 6 publications

References 0 publications

Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition

Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition

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

Optimal Number Data Trains in Hoax News Detection of Indonesian using SVM and Word2Vec

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