Implementasi K-NN Dalam Analisa Sentimen Riba Pada Bunga Bank Berdasarkan Data Twitter

Rasenda, Rasenda; Lubis, Hendarman; Ridwan, Ridwan

doi:10.30865/mib.v4i2.2051

Cited by 8 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A stopword is a common word that usually appears in large numbers and is considered to have no meaning [11]. c. Tokenization is the process of cutting or breaking a sentence into several words [29]. d. Case Folding is a process to change all documents' text to lowercase [30].…”

Section: Preprocessing Datamentioning

confidence: 99%

See 1 more Smart Citation

Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

Anam¹,

Mahendra

Agustin³

et al. 2022

intensif

View full text Add to dashboard Cite

The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%.

show abstract

Section: Preprocessing Datamentioning

confidence: 99%

“…d. Case Folding is a process to change all documents' text to lowercase [30]. e. Stemming is the stage to make suffix words into essential words according to correct Indonesian rules [29].…”

Section: Preprocessing Datamentioning

confidence: 99%

Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

Anam¹,

Mahendra

Agustin³

et al. 2022

intensif

View full text Add to dashboard Cite

show abstract

“…Text mining merupakan siklus untuk mendapatkan informasi yang berguna dari suatu catatan yang memiliki format tidak terstruktur. Proses ini digunakan untuk menangani permasalahan proses ekstraksi informasi dan dapat dimanfaatkan untuk mencari informasi yang belum ditemukan dan diketahui, secara umum text mining terdapat proses dalam pembersihan data, yaitu preprocessing data [3][4] [5]. Sedangkan analisis sentimen adalah metode yang melibatkan pemahaman, penggalian, dan pengolahan data sastra secara otomatis untuk mengambil data sentimen yang berada dalam kalimat opini.…”

Section: Pendahuluanunclassified

Analisis Sentimen dan Klasifikasi Tweet Terkait Mutasi COVID-19 menggunakan Metode Naïve Bayes Classifier

Dewandaru

Wibowo

2022

JTMI

View full text Add to dashboard Cite

Towards the end of 2019 in Wuhan City, China, a new type of Corona Virus was discovered which has the scientific name COVID-19 and is a type of virus that causes acute disorders in the human respiratory system. The spread of this virus is very fast and causes mutations of this virus to a more lethal stage than before. Thus, sentiment analysis is expected to be able to determine the trend of public assessment of the COVID-19 mutation. Naïve Bayes Classifier is a method used in research. This method can classify data or opinions into two sentiments, namely positive and negative. The research data comes from Twitter which is taken using the Twitter API with the keyword "covid mutation", for data processing several processes are carried out, namely sentiment classification, data cleaning, and preprocessing so that the final result is obtained. The test results from this study show that the Naïve Bayes Classifier method has an accuracy of 86.67% with an f1-score of 82.00% on positive sentiment and 89.00% on negative sentiment. Based on the results of the study, it can be concluded that the Naïve Bayes Classifier method can be used to analyze sentiment data from tweets about the COVID-19 mutation with an accuracy of 86.67%.

show abstract

“…[16]. Proses analisis dapat mencakup teks ulasan, forum, tweet, atau blog, dengan data preprocessing mencakup proses tokenization, stopword, penghapusan, stemming, identifikasi sentimen, dan klasifikasi sentimen [17][18] [19].…”

Section: Analisis Sentimenunclassified

Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes

Samsir

Ambiyar

Verawardina

et al. 2021

mib

View full text Add to dashboard Cite

The WHO announced that more than 52 million people tested positive for Covid-19, and 1.2 million died in the second week of November 2020. Meanwhile, Indonesia recorded 463 thousand individuals with 15,148 deaths that were confirmed positive. Strategy against pandemics by incorporating socialization. However, learning that was initially bold as a technique became controversial due to the briefness of the adaptation process. a wide continuum of social reactions has resulted in the sudden transition from face-to-face learning to bold learning on a large scale. This research focuses on public opinion on online learning during the Indonesian COVID-19 pandemic in early November 2020. The analysis was carried out on Twitter by mining document-based text that was interpreted using the Naïve Bayes algorithm. The results show that online learning has a positive sentiment of 30 percent, a negative sentiment of 69 percent, and a neutral 1 percent over the period. Due to community dissatisfaction about online learning, a significant amount of negative sentiment is created. Some tweets indicate disappointment with the words' stress 'and' lazy 'in the conversation being high-frequency words.

show abstract

Implementasi K-NN Dalam Analisa Sentimen Riba Pada Bunga Bank Berdasarkan Data Twitter

Cited by 8 publications

References 0 publications

Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA)

Analisis Sentimen dan Klasifikasi Tweet Terkait Mutasi COVID-19 menggunakan Metode Naïve Bayes Classifier

Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes

Contact Info

Product

Resources

About