NUSLI, AFFAN DHARMAWAN (2023) PENERAPAN METODE SUPPORT VECTOR MACHINE DAN NAIVE BAYES DALAM MENGANALISIS SENTIMEN MASYARAKAT TERHADAP NON-FUNGIBLE TOKEN ( NFT) PADA TWITTER. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Assets are one of the important elements in our lives where assets have value and information in them. Assets themselves have a close relationship with individual wealth, where if we have many assets, the higher the wealth we have. In today's digital era, assets are not only tangible but already in digital form. One of the digital assets is the Non-Fungible Token (NFT). On Twitter, many people use it to express their opinion about this NFT and it becomes a hot topic of conversation on Twitter. Data from public opinion on Twitter plays a very important role as a sentiment analysis to obtain positive or negative public opinion on an object on Twitter so that it can be classified into several categories, namely Positive, Negative and Neutral. The process of sentiment analysis is carried out by preprocessing data, weighting words using the TF-IDF method, applying the algorithm using Support Vector Machine (SVM) and Naïve Bayes. Finally, Model Evaluation is carried out using the Confusion Matrix in the form of accuracy, precision, recall and f1-score values. Based on the test results of the two methods, the results obtained from the support vector machine method were an accuracy value of 97.9%, a precision value of 97%, a recall value of 96%, and an f1-score value of 97%. While the Naïve Bayes method produces an accuracy value of 94.6%, a precision value of 95%, a recall value of 89% and an f1-score value of 91%. Keywords: Asset, Non-Fungible Token, Twitter, Support Vector Machine, Naïve Bayes Aset merupakan salah satu unsur penting dalam kehidupan kita yang dimana aset memiliki nilai dan informasi didalamnya. Aset sendiri memiliki hubungan erat dengan kekayaan individu, yang dimana jika kita memiliki banyak aset maka semakin tinggi pula kekayaan yang kita miliki. Dalam era digital saat ini, aset tidak hanya berbentuk nyata namun sudah berbentuk digital, Salah satu aset digital yaitu Non-Fungible Token (NFT). Pada Twitter banyak sekali digunakan masyarakat untuk menyampaikan opininya terhadap NFT ini dan menjadi perbincangan hangat di Twitter. Data dari pendapat masyarakat pada twitter sangat berperan sebagai analisis sentimen untuk mendapatkan pendapat positif atau negatif masyarakat mengenai salah satu objek di twitter sehingga dapat diklasifikasikan menjadi beberapa kategori yaitu Positif, Negatif dan Netral. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan metode menggunakan Support Vector Machine (SVM) dan Naïve Bayes. Terakhir dilakukan Evaluasi Model menggunakan Confusion Matrix berupa nilai Accuracy, Precision, Recall dan F1- score. Berdasarkan hasil pengujian kedua metode, hasil yang didapatkan dari metode support vector machine berupa nilai akurasi sebesar 97.9%, nilai presisi sebesar 97%, nilai recall sebesar 96%, dan nilai f1-score 97%. Sedangkan metode Naïve Bayes mendapatkan hasil nilai akurasi sebesar 94.6%, nilai presisi sebesar 95%, nilai recall sebesar 89% dan nilai f1-score sebesar 91%. Kata Kunci: Aset, Non-Fungible Token, Twitter, Support Vector Machine, Naïve Bayes
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