Perbandingan Kernel Support Vector Machine (Svm) Dalam Penerapan Analisis Sentimen Vaksinisasi Covid-19

Aulia, Thalita Meisya Permata; Nur, Arifin; Mayasari, Rini

doi:10.31598/sintechjournal.v4i2.762

Cited by 8 publications

(7 citation statements)

References 6 publications

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“…Dalam proses klasifikasi menggunakan Support Vector Machine, terdapat kernel trick yang bisa digunakan dalam mengklasifikasi sebuah sentimen untuk mendapatkan hasil yang lebih optimal [11]. Dalam penelitian Aulia T dan kawan-kawan, terdapat empat jenis kernel yang bisa digunakan dalam algoritma Support Vector Machine yaitu kernel linear, kernel rbf, kernel polynomial, dan kernel sigmoid [12]. Oleh karena itu, algoritma Support Vector Machine digunakan pada penelitian ini untuk menguji performa kernel yang ada pada Support Vector Machine dalam mengklasifikasi dan menganalisis sentimen terhadap Telkomsel.…”

Section: Pendahuluanunclassified

Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter

Fremmuzar,

Baita

2023

Komputika

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Telkomsel is an internet service provider in Indonesia which was launched in 1995. As an internet service provider with the most users, Telkomsel has become the center of attention of internet users in Indonesia. This invites user opinions and perspectives on Telkomsel, which is commonly referred to as sentiment. One of the media commonly used to express an opinion and point of view is Twitter. Twitter is a social media platform that is often a place for sharing and spreading the news, and discussing ideas, and opinions of Twitter users. In this study, the algorithm used is the Support Vector Machine. In the Support Vector Machine, there is a kernel trick that will be used to determine kernel performance and analyze sentiment. The sentiments analyzed amounted to 537 tweets collected by scraping. The collected tweets will go through the preprocessing stage, namely cleaning, case folding, tokenizing, normalization, stemming, stopword removal, and detokenizing. A sentiment is classified into 2 labels, namely positive and negative. Based on the test results, the sigmoid kernel has the best performance with an accuracy value of 0.950, a precision of 0.945, a recall of 0.860, an f1-score of 0.896, and sentiment tend toward negative.

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Section: Pendahuluanunclassified

Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter

Fremmuzar,

Baita

2023

Komputika

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“…Pada data yang bersifat linear, SVM berdimensi tinggi dan kemampuan generalisasinya tinggi. Namun pada data yang bersifat non-linear, dibutuhkan kernel [17]. Kernel berfungsi untuk mentransformasi data sehingga ruang dimensinya menjadi tinggi dan memisahkan data secara linear.…”

Section: Metode Support Vector Machineunclassified

Pengenalan Bangunan Bersejarah Pura dengan Menggunakan Local Binary Pattern dan Support Vector Machine

Jochsen,

Angeline,

Herwindiati

et al. 2023

JoSYC

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One area that has a rich cultural heritage is Bali. Bali is very well known as a very beautiful place and is often visited by tourists in Indonesia and outside Indonesia. Temple buildings in Bali have unique characteristics that reflect the richness of Indonesian culture. So many tourists are interested in vacationing there. However, due to the uniqueness of each temple building there, there is a lack of knowledge about the buildings being seen, so the main aim of this design is to develop a system for recognizing historical temple buildings in Indonesia through building images. More broadly, this design contribution can be applied in the development of similar systems for other historical regions in Indonesia, enriching efforts to preserve and promote cultural heritage nationally. Thus, this design not only paves the way for innovation in the field of image recognition, but also has a positive impact in preserving valuable cultural property. The method used for recognition is Local Binary Pattern as texture feature extraction from temple building images, while Support Vector Machine with a polynomial kernel is used to recognize temple buildings. It is hoped that the combination of these two methods can provide good results in recognizing temple buildings with the correct classification level. The accuracy of this design model using 90 percent training data and 10 percent test data was 45.93 percent, while when using 80 percent training data and 20 percent test data, the accuracy dropped slightly to 43.96 percent. When using 90 percent training data, the recognition of historical buildings produces a precision of 59 percent, a recall value of 71 percent, and an f1-score of 57 percent. On the other hand, with 80 percent training data, the recognition of historical buildings produces 62 percent precision, 72 percent recall value, and 57 percent f1-score.

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“…Proses ini dilakukan dengan tujuan untuk menghindari perbedaan penulisan yang tidak konsisten dalam satu dokumen. Cleansing text dilakukan untuk membersihkan teks dari tanda baca, angka, emoticon, serta white space [19]. Word normalization digunakan untuk mengubah kata tidak baku menjadi kata baku [20].…”

Section: Preprocessing Dataunclassified

Perbandingan Metode Klasifikasi Support Vector Machine Dan Naïve Bayes Pada Analisis Sentimen Kendaraan Listrik

Ernawati,

Satya Kumara,

Setiawan

2023

SPEKTRUM

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Electric vehicles are one of the solutions that can be used to deal with the problem of greenhouse gas emissions. Switching to electric vehicles can be an effective solution because electric vehicles have many advantages. However, the acceptance of electric vehicles in Indonesia depends on the opinions or sentiments given by the community. Sentiment analysis can provide a specific picture of how the sentiment or opinion given by the Indonesian people towards electric vehicles. The sentiment analysis process is carried out using the Python programming language. This research compares SVM and Naïve Bayes methods in sentiment analysis in terms of accuracy and time efficiency. A total of 717 data were used as test data and SVM correctly classified 150 negative data, 152 neutral data, and 277 positive data. Meanwhile, Naïve Bayes correctly classified 166 negative data, 143 neutral data, and 282 positive data. training time required for the SVM method is 37.42 seconds while Naïve Bayes is 0.10 seconds. Naïve Bayes is the best method in this study because of its high accuracy and fast training time.

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Perbandingan Kernel Support Vector Machine (Svm) Dalam Penerapan Analisis Sentimen Vaksinisasi Covid-19

Cited by 8 publications

References 6 publications

Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter

Uji Kernel SVM dalam Analisis Sentimen Terhadap Layanan Telkomsel di Media Sosial Twitter

Pengenalan Bangunan Bersejarah Pura dengan Menggunakan Local Binary Pattern dan Support Vector Machine

Perbandingan Metode Klasifikasi Support Vector Machine Dan Naïve Bayes Pada Analisis Sentimen Kendaraan Listrik

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