Unbalanced multiclass classification with adaptive synthetic multinomial naive Bayes approach

• Autorzy: Fauzi Fatkhurokhman , Ismatullah , Nur Indah Manfaati

Identyfikatory

DOI

10.35784/iapgos.3740

Warianty tytułu

PL

Niezrównoważona klasyfikacja wieloklasowa z adaptacyjnym syntetycznym wielomianowym naiwnym podejściem Bayesa

• Języki publikacji: EN

Abstrakty

EN

Opinions related to rising fuel prices need to be seen and analysed. Public opinion is closely related to public policy in Indonesia in the future. Twitter is one of the media that people use to convey their opinions. This study uses sentiment analysis to look at this phenomenon. Sentiment is divided into three categories: positive, neutral, and negative. The methods used in this research are Adaptive Synthetic Multinomial Naive Bayes, Adaptive Synthetic k-nearest neighbours, and Adaptive Synthetic Random Forest. The Adaptive Synthetic method is used to handle unbalanced data. The data used in this study are public arguments per province in Indonesia. The results obtained in this study are negative sentiments that dominate all provinces in Indonesia. There is a relationship between negative sentiment and the level of education, internet use, and the human development index. Adaptive Synthetic Multinomial Naive Bayes performed better than other methods, with an accuracy of 0.882. The highest accuracy of the Adaptive Synthetic Multinomial Naive Bayes method is 0.990 in Papua Barat Province.

PL

Należy przyjrzeć się i przeanalizować opinie związane z rosnącymi cenami paliw. Opinia publiczna jest ściśle związana z polityką publiczną Indonezji w przyszłości. Twitter jest jednym z mediów, których ludzie używają do przekazywania swoich opinii. Niniejsze badanie wykorzystuje analizę nastrojów, aby przyjrzeć się temu zjawisku. Opinia jest podzielona na trzy kategorie: pozytywną, neutralną i negatywną. Metody wykorzystane w tym badaniu to Adaptive Synthetic Multinomial Naive Bayes, Adaptive Synthetic k-nearest neighbours i Adaptive Synthetic Random Forest. Metoda Adaptive Synthetic służy do obsługi niezrównoważonych danych. Dane wykorzystane w tym badaniu to argumenty publiczne według prowincji w Indonezji. Wyniki uzyskane w tym badaniu to negatywne nastroje, które dominują we wszystkich prowincjach Indonezji. Istnieje związek między negatywnymi nastrojami a poziomem wykształcenia, korzystaniem z Internetu i wskaźnikiem rozwoju społecznego. Adaptive Synthetic Multinomial Naive Bayes działała lepiej niż inne metody, z dokładnością 0,882. Najwyższa dokładność metody Adaptive Synthetic Multinomial Naive Bayes wynosi 0,990 w prowincji Papua Barat.

Słowa kluczowe

EN

adaptive synthetic; classification; imbalance data; accuracy

PL

adaptacyjna synteza; klasyfikacja; danedotyczące nierównowagi; dokładność

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2023
• Tom: T. 13, nr 3
• Strony: 64--70
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Fauzi Fatkhurokhman

• Universitas Muhammadiyah Semarang, Department of Statistics, Semarang, Indonesia

• https://orcid.org/0000-0002-8277-8638

autor

Ismatullah

• Universitas Muhammadiyah Semarang, Department of Statistics, Semarang, Indonesia

• https://orcid.org/0009-0005-7472-1761

autor

Nur Indah Manfaati

• Universitas Muhammadiyah Semarang, Department of Statistics, Semarang, Indonesia

• https://orcid.org/0000-0002-1017-7323

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).