Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM)

Arsi, Primandani; Waluyo, Retno

doi:10.25126/jtiik.0813944

Cited by 57 publications

(61 citation statements)

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“…Figure 1 shows the large process flow that was carried out to complete this research. Figure 2 shows the classification flow after the data has been structured through the text preprocessing process, involving two classification algorithms Naïve Bayes and Support Vector Machine [13].…”

Section: G Evaluationmentioning

confidence: 99%

Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method

2022

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Tweets from users in the form of opinions about a product can be used as a company evaluation of the product. To obtain this evaluation, the method that can be used is sentiment analysis to divide opinions into positive and negative opinions. This study uses 1000 data from Twitter related to an internet service provider company where the data is divided into two classes, namely 692 positive classes and 308 negative classes. In the Tweet there are still many words that are not standard. Therefore, previously carried out the initial process or preprocessing to filter out non-standard words. Before doing the classification, the data needs to be divided into training data and test data with a ratio of 90:10, then processed using the Support Vector Machine and Naïve Bayes techniques to get the results of the classification of positive opinions and negative opinions. The level of accuracy in the classification using the Support Vector Machine is 84% and using Naïve Bayes is 82%.

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Section: G Evaluationmentioning

confidence: 99%

Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method

2022

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“…Analisis Sentimen Analisis Sentimen merupan bagian dari pada proses text mining, dimana didalam mempelajari bagaimana menganalisis suatu teks, mengekstrak data tekstual seperti pendapat, sikap, evaluasi (Sodik & Kharisudin, 2021) yang tujuan adalah untuk mendapatkan informasi dari data teks untuk mengidentifikasi teks tersebut masuk kedalam kategori positif atau nefatif (Mahendrajaya et al, 2019). Inilah yang menjadikan analisis sentiment menjadi tolak ukur dalam proses pengambilan keputusan (Arsi & Waluyo, 2021).…”

Section: Adapununclassified

Analisis Sentimen Pada Ulasan Aplikasi Kredivo Dengan Algoritma SVM Dan NBC

Muhammadin

Sobari

2021

rpts

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Analisis sentimen Review Aplikasi Kredivo merupakan salah satu contoh proses untuk mengaplikasikan dari pada metode algoritma Support Vector Machine (SVM) dan Naive Bayes Classifier dalam mengklasifikasi sentiment yang tujuannya adalah membandingkan kedua metode tersebut mana yang lebih baik. Data penelitian ini diambil dari website Google Play Store, data yang diambil yaitu data teks ulasan dengan jumlah 10000 ulasan. Data tersebut melewati proses Data Preprocessing dan menggunakan algoritma Support Vector Machine (SVM) dan Naive Bayes Classifier (NBC). Setelah itu dilakukan pengujian menggunakan kombinasi dari pembagian data latih dan data uji, serta menggunakan sistem set validation, dimana 80% untuk data uji dan 20% untuk data testing. Pengujian menggunakan algoritma Support Vector Machine menghasilkan akurasi 83,3% dengan nilai presisi untuk kelas positif 77% dan kelas negatif 87% sedangkan nilai recall untuk kelas positif sebesar 89% dan 73% untuk kelas negatif. Kemudian untuk algoritma Naive Bayes Classifier sendiri menghasilkan nilai akurasi sebesar 80,8% dengan nilai presisi untuk kelas positif sebesar 81% dan untuk kelas negatif sebesar 87%, sedangkan nilai recall untuk kelas positif sebesar 88% dan untuk kelas negatif sebesar 79%. Jadi untuk tingkat keseluruhan dapat dilihat dari nilai akursi dengan algoritma SVM lebih tinggi dibanding Naive Bayes Classifier.

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“…Previous researchers used sentiment analysis to assess the opinion and tendency of an opinion on a topic, whether negative, neutral or positive [4] [5]. The data obtained to perform sentiment analysis is usually obtained from social media Twitter [6][7] [8].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination

Rahmaddeni¹,

Anam²,

Irawan³

et al. 2022

Ilk. J. Ilm.

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The coronavirus has become a global pandemic and has spread almost all over the world, including Indonesia. The spread of COVID-19 in Indonesia causes many negative impacts. Therefore, the government took vaccination measures to suppress the spread of COVID-19. The public's response to vaccination was quite diverse on Twitter, some were supportive, and some were not. The data used in this study came from Twitter which was taken using the emprit drone portal by using the keyword, "vaccination." The classification is conducted using the SVM and hybrid methods between SVM and XGBoost or what is commonly called XGBSVM. The purpose of this study is to provide an overview to the public on whether the Covid-19 vaccination tends to create positive, neutral, or negative opinions. The results of the sentiment evaluation show that SVM has the highest accuracy of 83% with 90:10 data splitting. On the other hand, the XGBSVM produces 79% accuracy with 90:10 data splitting.

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Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM)

Cited by 57 publications

References 1 publication

Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method

Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method

Analisis Sentimen Pada Ulasan Aplikasi Kredivo Dengan Algoritma SVM Dan NBC

Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination

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