Analisis Sentimen Prosesor AMD Ryzen menggunakan Metode Support Vector Machine

Aturan untuk penyedia layanan di internet atau Penyelenggara Sistem Elektronik di Indonesia mendapatkan pro dan kontra semenjak adanya Peraturan Pemerintah yang diterbitkan. Twitter sebagai media sosial, menjadi salah satu tempat menyampaikan pendapat secara bebas, tidak terkecuali pendapat untuk peraturan PSE. Penelitian ini bertujuan untuk mengidentifikasi karakteristik akun pengguna Twitter dengan mempertimbangkan sentimen tweet mereka terhadap Peraturan PSE. Analisis ini dilakukan dengan mengidentifikasi sentimen pada data tweet, kemudian melihat atribut dari akun pengguna tweet tersebut. Pada proses penggalian data didapatkan sebanyak 849 tweet, 679 diantaranya diberikan label sentimen yang kemudian akan digunakan untuk membangun model machine learning berbasis IndoBERT. Selanjutnya, sebanyak 335 data terkait akun pengguna yang terikat dengan data tweet diatas juga didapatkan. Model machine learning yang dihasilkan mencapai akurasi sebesar 71%, didapatkan juga model paling baik dalam mengklasifikasi tweet dengan sentiment negatif. Ditemukan bahwa akun yang mendukung PSE atau memiliki postingan tweet dengan sentimen positif berusia lebih muda dibandingkan yang bersentimen negatif. Meski begitu rata-rata jumlah tweet yang dihasilkan akun-akun bersentimen positif lebih banyak dibandingkan akun dengan sentimen negatif meskipun rata-rata usianya jauh lebih tua. Ini menunjukkan bahwa akun bersentimen positif lebih sering mengirim tweet dibandingkan akun bersentimen negatif.

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“…Hasil dari analisa tersebut digunakan untuk mencari tahu kesalahan nilai yang ada selama proses (Erlin et al, 2021).…”

Section: F1 Score Adalah Pengukuran Untuk Menunjukkan Keseiumbangan A...unclassified

Analisis Karakteristik Akun Twitter Berdasarkan Sentimen Pendapat Terkait Undang-Undang PSE

Profesi¹,

Rivaldi

Yudhantorro

et al. 2022

SATIN

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“…This is further strengthened by research that the SVM algorithm can be used in sentiment analysis in grouping public opinion. According to Erlin et al (2021) concerning Sentiment Analysis for AMD Ryzen Processors using the Support Vector Machine Method, which discusses public opinion trends towards AMD Ryzen Processors, the results of the SVM method were very good, with an accuracy of 96.76% In 2023 research conducted by Idris et al on Sentiment Analysis of the Use of the Shopee Application Using the Support Vector Machine (SVM) Algorithm, which evaluated comment data from Shopee users from the reviews provided with the Support Vector Machine algorithm, obtained an accuracy of 98% [10]. According to Erlin et al (2022) in the research on Sentiment Analysis for the Elimination of the National Examination Using the Support Vector Machine Method, which discusses public opinion regarding the abolition of the national exam, the accuracy results obtained with the SVM method were 96.97% [11].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Citayam Fashion Week Phenomenon Using Support Vector Machine

Muhammad Rosyadi,

Erlin

2023

ITJRD

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Citayam Fashion Week is a phenomenon that displays a model doing a fashion show using distinctive and unique clothing when crossing a zebra cross as a catwalk. This phenomenon has received extraordinary attention and discussion from various circles and led to numerous pros and cons among the public and observers of society in Indonesia. Therefore, it is great importance to conduct the study on sentiment analysis of this phenomenon to determine society's sentiment tendency to provide government references and help decision-makers improve their policies. Sentiment analysis was performed using the Support Vector Machine based on the polynomial kernel. The results shows that the accuracy, recall, precision and F1-Score value of 95.61%, 95.66%, 96% and 95.55%, respectively. This study proved that the Support Vector Machine classifier with the polynomial kernel provides higher algorithm performance on text classification. Therefore, the government can use the result of this study to evaluate the existence of the citayam fashion week which may be followed by other phenomena.

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Analisis Sentimen Prosesor AMD Ryzen menggunakan Metode Support Vector Machine

Cited by 2 publications

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Analisis Karakteristik Akun Twitter Berdasarkan Sentimen Pendapat Terkait Undang-Undang PSE

Analisis Karakteristik Akun Twitter Berdasarkan Sentimen Pendapat Terkait Undang-Undang PSE

Sentiment Analysis of Citayam Fashion Week Phenomenon Using Support Vector Machine

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