Penggunaan Metode Naïve Bayes Classifier pada Analisis Sentimen Facebook Berbahasa Indonesia

Suryani, Putu Sri Merta; Linawati, Linawati; Saputra, Komang Oka

doi:10.24843/mite.2019.v18i01.p22

Cited by 19 publications

(18 citation statements)

References 2 publications

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“…Dalam penelitian ini akan dilakukan pengujian dengan menggunakan metode Naive Bayes Classifier. Pengujian dengan metode Naive Bayes digunakan untuk mendapatkan sentiment probability dalam suatu perangkat lunak, sedangkan mengukur akurasinya menggunakan NMF Vector (Non-negatif Matriks Faktorisasi) [15]. adalah sekelompok algoritma di analisis multivariat dan aljabar linear di mana matriks V yang di faktorkan ke dalam (biasanya) dua matriks W dan H, dengan properti bahwa ketiga matriks tidak memiliki elemen negatif.…”

Section: Pengujianunclassified

Sentiment Analysis of the Community in the Twitter to the 2020 Election in Pandemic Covid-19 by Method Naive Bayes Classifier

Muzaki

Witanti

2021

J. Tek. Inform. (JUTIF)

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The 2020 regional elections in the midst of the COVID-19 pandemic are starting to get crowded starting from the real world and in cyberspace, especially on Twitter social media. Twitter's existence has been widely used by various communities in recent years. Twitter is one of the media that represents the public response regarding public issu. Ahead of the general election (PEMILU), there are usually some parties who want to know the results of public sentiment or response to the issue, namely academics, intellectuals or even political opponents. Nevertheless, the implementation of local elections is very polemic in the community, therefore this study tries to analyze tweets that talk about issue public, namely the 2020 elections in the wake of the COVID-19 Pandemic. The analysis usually uses the classification of tweets containing public sentiment about the issue. The classification method used in this research is Naive Bayes Classifier (NBC) And Support Vector Machine (SVM). Naive Bayes Classifier is combined with features that can detect weighting using probability. The classification of tweets in this study was obtained based on a combination of two classes namely sentiment class and category class. The classification of sentiment consists of positive and negative. Test results on built-in applications show that accuracy with Naive Bayes delivers better results than Support Vector Machine. However, overall the use of the Naive Bayes method has a good performance to classify tweets with an accuracy rate of 92.2%

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Section: Pengujianunclassified

Sentiment Analysis of the Community in the Twitter to the 2020 Election in Pandemic Covid-19 by Method Naive Bayes Classifier

Muzaki

Witanti

2021

J. Tek. Inform. (JUTIF)

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“…Dalam perkembangannya, kajian analisis sentimen menjadi holistik dengan topik dan studi kasus yang komprehensif, sepertihalnya ulasan tentang : pengguna gopay [78]; pemilihan presiden [79], [80]; pembangunan infrastruktur [81]; pembatasan sosial berskala besar [7]; pelayanan jasa internet [82]; pelanggan toko online [24], [83], [84]; pariwisata [85]- [90]; bakal calon [91], [92]; sarana transportasi [93]- [95]; penilaian kinerja dosen [96]; mengukur persepsi produk [97]; media sosial berbahasa indonesia [98]; maskapai [99]- [101]; layanan sistem informasi akademik [102]; layanan perusahaan [2]; layanan haiDJPb [103]; kurikulum [104]; kuliner [105], [106]; konten radikal [107], [108]; komparasi teknik klasifikasi [5], [48], [109], [110]. Dengan demikian, dapat diketahui bahwa kuantitas riset analisis sentimen yang populer ialah ulasan pelanggan toko online, pariwisata, opini publik tentang sarana transportasi, bakal calon presiden, penilaian dosen, mskapai penerbangan, kuliner, konten radikal dan komparasi teknik klasifikasi.…”

Section: Gambar 2 Hasil Klasifikasi Studi Kasus Kajian Analisis Sentimen Sumber : Hasil Penyandian Axial Menggunakan Nvivo 12 Plusunclassified

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

Singgalen

2021

Journal-ISI

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This article use Systematic Literature Review (SLR) to classify sentiment analysis based on case studies, methods, social media, and platforms. The coding stage is divided into three stages, namely the open, selective and axial coding. The literature study on sentiment analysis is divided into two parts: identifying gaps based on case studies and data sources and identifying gaps based on the methods or algorithms used. The gap identification results based on case studies and data sources show that popular review topics are synonymous with entertainment, economic and political content. Therefore, the quantity of research with review topics related to the implementation of education, the dynamics of the bureaucracy, health facilities and services, and non-governmental organization’s activities need to be increased. Meanwhile, the most dominant platforms used as data sources are website and mobile-based applications. The results of the gap identification based on the method and algorithm show that the quantity of research with the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) method or algorithm is more dominant than the k-Nearest Neighbor (k-NN) and Lexicon-based. Thus, it is necessary to increase the number of other classification methods such as Particle Swarm Optimization, BM25, Decision Tree, K-Means, and Neural Networks.

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“…Menurut Suryani, 2019 metode NBC ini sederhana namun lebih efektif digunakan dalam hal melakukan proses klasifikasi, serta tingkat akurasinya tinggi dalam klasifikasi teks. Sedangkan metode Lexicon Based metode ini digunakan untuk mengelompokkan pendapat ataupun opini berdasarkan sentimennya, yaitu positif dan negatif atau netral [7].…”

Section: Pendahuluanunclassified

Sentimen Analisis Terhadap Pembelajaran Jarak Jauh Menggunakan Metode Naïve Bayes Classifier dan Lexicon Based

Prianto¹,

Hamka²

2021

jik

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Sejak awal pandemic covid-19 ini segala bidang terkena dampak khusunya dibidang pendidikan, dimana saat ini proses pembelajaran dilakukan secara jarak jauh. Penelitian ini dilakukan untuk mengetahui tanggapan dari masyarakat yang berada dalam ruang lingkup pendidikan seperti siswa,mahasiswa serta pengajar. Adapun jumlah responden yang memberikan tanggapannya yaitu sebanyak 265 melalui pengisian kuesioner dalam bentuk google form. Berdasarkan penelitian ini diketahui bahwa dari 265 orang yang memberikan respon dengan jumlah pernyataan sebanyak 6, maka diperoleh 1.590 jawaban yang berbeda. Dari 1.590 data diolah kembali sehingga data akhir sebanyak 1.468. Adapun hasil dari pelabelan dengan kamus lexicon based ini berjumlah 162 positif, 516 bernilai negatif, dan 790 netral. Hasil pengujian dengan naïve bayes diperoleh tingkat akurasi 53.8% dengan menggunakan penggukuran efektifitas confusion matrix

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Penggunaan Metode Naïve Bayes Classifier pada Analisis Sentimen Facebook Berbahasa Indonesia

Cited by 19 publications

References 2 publications

Sentiment Analysis of the Community in the Twitter to the 2020 Election in Pandemic Covid-19 by Method Naive Bayes Classifier

Sentiment Analysis of the Community in the Twitter to the 2020 Election in Pandemic Covid-19 by Method Naive Bayes Classifier

Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

Sentimen Analisis Terhadap Pembelajaran Jarak Jauh Menggunakan Metode Naïve Bayes Classifier dan Lexicon Based

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