ANALISIS SENTIMEN KLASIFIKASI TWEET VAKSIN COVID 19 DENGAN NAÏVE BAYES

Medsos Twitter memiliki pengguna sebanyak 332 juta dengan ± 500 juta tweet yang dikirimkan per harinya dan 200 miliar tweet per tahun. Pemerintahan sekarang juga tidak lepas dari komentar publik tentang kebijakan vaksin Covid 19. Kebijakan Indonesia dalam program vaksinasi Covid 19 kebalikan dari kebijaksanaan sejumlah negara, para ahli mengatakan kelompok pertama yang divaksinasi haruslah staf medis garis depan dan kemudian orang tua. Pemerintah Indonesia lebih memilih untuk menyuntik vaksin Covid 19 bagi tenaga kesehatan yang berada di garis terdepan melawan pandemi Covid-19. Penelitian ini bertujuan untuk mengimplementasi algoritma Naïve Bayes Classifier (NBC) dalam mengklasifikasikan sebuah tweet opini sehingga dapat diketahui termasuk ke dalam kelas bersentimen positif atau negatif dan menganalisis nilai dari ketepatan algoritma Naïve Bayes Classifier. Metode NBC untuk menjalankan klasifikasi tweet vaksin Covid 19 dalam klasifikasi bersentimen positif atau negatif dengan 275 data latih dan 25 data uji yang diperoleh menghasilkan ketepatan 96 % dengan jumlah klasifikasi sentimen positif adalah 24 dan jumlah klasifikasi sentimen negatif adalah 0. Hasil yang diperoleh dalam proses pengklasifikasian mendapatkan akurasi yang cukup tinggi yaitu 96 % maka dari itu metode NBC bisa dipergunakan dalam menjalankan klasifikasi tweet vaksin Covid 19 positif atau negatif secara otomatis

ANALISIS SENTIMEN KLASIFIKASI TWEET VAKSIN COVID 19 DENGAN NAÏVE BAYES

Medsos Twitter memiliki pengguna sebanyak 332 juta dengan ± 500 juta tweet yang dikirimkan per harinya dan 200 miliar tweet per tahun. Pemerintahan sekarang juga tidak lepas dari komentar publik tentang kebijakan vaksin Covid 19. Kebijakan Indonesia dalam program vaksinasi Covid 19 kebalikan dari kebijaksanaan sejumlah negara, para ahli mengatakan kelompok pertama yang divaksinasi haruslah staf medis garis depan dan kemudian orang tua. Pemerintah Indonesia lebih memilih untuk menyuntik vaksin Covid 19 bagi tenaga kesehatan yang berada di garis terdepan melawan pandemi Covid-19. Penelitian ini bertujuan untuk mengimplementasi algoritma Naïve Bayes Classifier (NBC) dalam mengklasifikasikan sebuah tweet opini sehingga dapat diketahui termasuk ke dalam kelas bersentimen positif atau negatif dan menganalisis nilai dari ketepatan algoritma Naïve Bayes Classifier. Metode NBC untuk menjalankan klasifikasi tweet vaksin Covid 19 dalam klasifikasi bersentimen positif atau negatif dengan 275 data latih dan 25 data uji yang diperoleh menghasilkan ketepatan 96 % dengan jumlah klasifikasi sentimen positif adalah 24 dan jumlah klasifikasi sentimen negatif adalah 0. Hasil yang diperoleh dalam proses pengklasifikasian mendapatkan akurasi yang cukup tinggi yaitu 96 % maka dari itu metode NBC bisa dipergunakan dalam menjalankan klasifikasi tweet vaksin Covid 19 positif atau negatif secara otomatis

The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts

Preprocessing is an essential task for sentiment analysis since textual information carries a lot of noisy and unstructured data. Both stemming and stopword removal are pretty popular preprocessing techniques for text classification. However, the prior research gives different results concerning the influence of both methods toward accuracy on sentiment classification. Therefore, this paper conducts further investigations about the effect of stemming and stopword removal on Indonesian language sentiment analysis. Furthermore, we propose four preprocessing conditions which are with using both stemming and stopword removal, without using stemming, without using stopword removal, and without using both. Support Vector Machine was used for the classification algorithm and TF-IDF as a weighting scheme. The result was evaluated using confusion matrix and k-fold cross-validation methods. The experiments result show that all accuracy did not improve and tends to decrease when performing stemming or stopword removal scenarios. This work concludes that the application of stemming and stopword removal technique does not significantly affect the accuracy of sentiment analysis in Indonesian text documents

Chatbot Informasi Penerimaan Mahasiswa Baru Menggunakan Metode FastText dan LSTM

New Student Admission (PMB) is an important stage in the continuity of education in an educational institution. The Faculty of Science and Informatics (FSI) at Jenderal Achmad Yani University (UNJANI) provides information services about PMB to prospective students and parents/guardians of prospective students but is still inefficient, so it is necessary to improve PMB information services by using Chatbots as a solution that is able to serve questions effectively and consistent. This study aims to develop a PMB information Chatbot system for FSI using the FastText and Long Short-Term Memory (LSTM) methods. Several methods have been used in Chatbot development research, such as Term Frequency–Inverse Document Frequency (TF-IDF), Bag of Words (BoW), and Convolutional Neural Networks (CNN). However, these studies still have certain limitations, such as the inability to grasp the meaning of words and difficulties in handling certain inputs. In this study, the text classification model uses the FastText method as the stage for representing words in vector form, then combined with several pre-processing methods (Tokenization & Casefolding) and LSTM for the classification stage. Then put it into the Chatbot component according to the architecture that was made. In testing, the Black Box Testing method is used to ensure the functionality of the Chatbot system. The test results show that the Chatbot system is able to understand the topic of questions asked by users properly. The interaction between users and Chatbots also runs smoothly, resulting in appropriate and informative responses. The results of this study are expected to be an effective and consistent solution for providing information about PMB to prospective students and parents/guardians of prospective students at FSI

The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage

Sentiment analysis is a part of text mining used to dig up information from a sentence or document. This study focuses on text classification for the purpose of a sentiment analysis on hospital review by customers through criticism and suggestion on Google Maps Review. The data of texts collected still contain a lot of nonstandard words. These nonstandard words cause problem in the preprocessing stage. Thus, the selection and combination of techniques in the preprocessing stage emerge as something crucial for the accuracy improvement in the computation of machine learning. However, not all of the techniques in the preprocessing stage can contribute to improve the accuracy on classification machine. The objective of this study is to improve the accuracy of classification model on hospital review by customers for a sentiment analysis modeling. Through the implementation of the preprocessing technique combination, it can produce a highly accurate classification model. This study experimented with several preprocessing techniques: (1) tokenization, (2) case folding, (3) stop words removal, (4) stemming, and (5) removing punctuation and number. The experiment was done by adding the preprocessing methods: (1) spelling correction and (2) Slang. The result shows that spelling correction and Slang method can assist for improving the accuracy value. Furthermore, the selection of suitable preprocessing technique combination can fasten the training process to produce the more ideal text classification model

Sentiment Analysis on Naija-Tweets

Examining sentiments in social media poses a challenge to natural language processing because of the intricacy and variability in the dialect articulation, noisy terms in form of slang, abbreviation, acronym, emoticon, and spelling error coupled with the availability of real-time content. Moreover, most of the knowledgebased approaches for resolving slang, abbreviation, and acronym do not consider the issue of ambiguity that evolves in the usage of these noisy terms. This research work proposes an improved framework for social media feed pre-processing that leverages on the combination of integrated local knowledge bases and adapted Lesk algorithm to facilitate pre-processing of social media feeds. The results from the experimental evaluation revealed an improvement over existing methods when applied to supervised learning algorithms in the task of extracting sentiments from Nigeria-origin tweets with an accuracy of 99.17%

Implementasi Metode Naïve Bayes untuk Analisis Sentimen Warga Jakarta Terhadap

Kegiatan riset ini bertujuan untuk menganalisis animo masyarakat Indonesia khususnya warga Jakarta atas munculnya transportasi massa umum MRT yang di resmikan oleh Pemerintah di bulan Maret 2019. Tahapan penelitian diawali proses crawling tweet dengan menggunakan tweetscrapper dari python. Kemudian dilakukan Preprocessing sehingga didapatkan data tweet yang siap untuk diproses pada pemisahan data yaitu data training dan data testing. Data training dilakukan proses pembobotan dengan TF-IDF, dan proses pembelajaran dengan naive bayes. Proses ini disebut dengan proses training yang bertujuan untuk menghasilkan model klasfikasi. Model klasifikasi digunakan untuk data testing melakukan proses klasifikasi yang menghasilkan label sentimen (positif/negatif). Proses ini dinamakan dengan proses testing. Hasil testing akan dilakukan perhitungan akurasi dari model yang sudah dibuat. Luaran dari penelitian ini berupa analisis sentimen animo warga Jakarta pada media sosial Twitter terhadap kehadiran layanan transportasi publik MRT, dan akurasi yang dihasilkan oleh metode naïve bayes yang diimplementasikan pada analisis sentime

A Study on Sentiment Analysis on Airline Quality Services: A Conceptual Paper

Airline quality service is crucial for airlines to remain competitive in the industry. The quality of the services of these airlines must meet customer satisfaction and other aspects of the overall service experience. The levels of service quality in an airline service may impact satisfaction and loyalty which may influence customer sentiment. Concerning the importance of airline quality service, customer sentiment towards the service must be investigated and one of the ways to analyze it is by using sentiment analysis. Sentiment analysis is the chosen tool nowadays to analyze comments or reviews made on these services, which may be positive, negative, or neutral. Using sentiment analysis, will not only help potential customers to view the overall sentiment portrayed, but organizations can also use the findings to improve their organization to be more competitive. Thus, this paper will focus on reviewing several recent works related to sentiment analysis as a tool for assisting organizations in assessing the quality of services in the airline industry. As a result, a new framework for assessing the quality of service for the organizations, especially the airline company will be proposed