Analisis Sentimen Prosesor AMD Ryzen menggunakan Metode Support Vector Machine
Keywords:
Analisis Sentimen; Support Vector Machine; AMD Ryzen; Akurasi; Twitter; Confusion Matrix
Abstract
Prosesor AMD menjadi salah satu pesaing prosesor Intel semenjak dikeluarkannya prosesor Ryzen generasi 3. Berbagai pendapat dan opini masyarakat mengenai prosesor ini sangat mudah ditemui pada media sosial Twitter. Opini ini dapat digunakan sebagai sistem untuk mendukung keputusan berkaitan dengan produk AMD Ryzen. Tujuan penelitian ini adalah untuk mengimplementasikan Analisis Sentimen dalam pendekatan Data Mining untuk menganalisa tekstual data yang terdapat pada Twitter menggunakan metode Support Vector Machine, mengeksplorasi dan memahami tren opini publik mengenai prosesor AMD Ryzen dan mengklasifikasikannya kedalam polaritas biner. Penelitian ini menggunakan Tweet dari Library Tweetscrapper. Pelabelan dilakukan oleh expert untuk diklasifikasikan menjadi sentimen positif dan negatif. Selanjutnya melakukan pra-pemrosesan data untuk menghilangkan noise, mendeteksi nilai data yang hilang, data duplikat dan tidak relevan. Selanjutnya, algoritma machine learning digunakan untuk memprediksi data baru. Model yang dihasilkan dievaluasi menggunakan confusion matrix. Hasil penelitian menunjukkan bahwa kinerja metode SVM sangat baik dalam hal akurasi, presisi, recall dan F1 Score dengan nilai masing-masing 96,67%, 96,43%, 100% dan 98,18%. Berdasarkan hasil yang diperoleh, sebagian besar publik memiliki opini yang positif terhadap prosesor AMD Ryzen. Penelitian ini juga membuktikan bahwa metode Support Vector Machine dapat digunakan sebagai algoritma cerdas untuk memprediksi sentimen di Twitter untuk data baru dengan cepat dan akurat.
References
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Chakraborty, K., Bhattacharyya, S., Bag, R., & Hassanien, A. A. (2019). Sentiment Analysis on a Set of Movie Reviews Using Deep Learning Techniques. In Social Network Analytics. Elsevier Inc. https://doi.org/10.1016/b978-0-12-815458-8.00007-4
Daeli, N. O. F., & Adiwijaya. (2020). Sentiment Analysis on Movie Reviews Using Information Gain and K-Nearest Neighbor. Journal of Data Science and Its Applications, 3(1), 1–7. https://doi.org/10.34818/JDSA.2020.3.22
Dictionary, O. (2021). Processor. In Oxford Dictionary. https://www.oxfordlearnersdictionaries.com/definition/english/processor?q=processor
Erlin, Rio, U., & Rahmiati. (2014). Two Text Classifiers in Online Discussion : Support Vector Machine vs Back-Propagation Neural Network. Telkomnika, 12(1), 189–200. https://doi.org/10.12928/TELKOMNIKA.v12i1.1798
Erlin, Rio, U., & Rahmiati. (2013). Text message categorization of collaborative learning skills in online discussion using support vector machine. Proceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications: “Recent Challenges in Computer, Control and Informatics”, IC3INA 2013, 295–300. https://doi.org/10.1109/IC3INA.2013.6819190
Fitri, E. (2020). Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine. Jurnal Transformatika, 18(1), 71. https://doi.org/10.26623/transformatika.v18i1.2317
Fitriyah, N., Warsito, B., & Maruddani, D. A. I. (2020). Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (SVM). Jurnal Gaussian, 9(3), 376–390. https://doi.org/10.14710/j.gauss.v9i3.28932
Gandhi, R. (2018). Support Vector Machine — Introduction to Machine Learning Algorithms SVM model from scratch. Towards Data Science. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47
Hariguna, T., Baihaqi, W. M., & Nurwanti, A. (2019). Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm. IJIIS: International Journal of Informatics and Information Systems, 2(2), 48–55. https://doi.org/10.47738/ijiis.v2i2.13
Isnain, A. R., Marga, N. S., & Alita, D. (2021). Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(1), 55. https://doi.org/10.22146/ijccs.60718
Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. In Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press. https://doi.org/10.1017/CBO9781139084789
Liu, N., & Shen, B. (2020). Aspect-based sentiment analysis with gated alternate neural network. Knowledge-Based Systems, 188(xxxx), 105010. https://doi.org/10.1016/j.knosys.2019.105010
Mamtesh, & Mehla, S. (2019). Sentiment Analysis of Movie Reviews using Machine Learning Classifiers. International Journal of Computer Applications, 182(50), 25–28. https://doi.org/10.5120/ijca2019918756
Mohd Nafis, N. S., & Awang, S. (2021). An Enhanced Hybrid Feature Selection Technique Using Term Frequency-Inverse Document Frequency and Support Vector Machine-Recursive Feature Elimination for Sentiment Classification. IEEE Access, 9(Ml), 52177–52192. https://doi.org/10.1109/ACCESS.2021.3069001
Nurdiansyah, Y., Bukhori, S., & Hidayat, R.(2018). Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method. Journal of Physics: Conference Series, 1008(1). https://doi.org/10.1088/1742-6596/1008/1/012011
Prastyo, P. H., Sumi, A. S., Dian, A. W., & Permanasari, A. E. (2020). Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel. Journal of Information Systems Engineering and Business Intelligence, 6(2), 112. https://doi.org/10.20473/jisebi.6.2.112-122
Putra, M. W. A., Susanti, Erlin, & Herwin. (2020). Analisis Sentimen Dompet Elektronik Pada Twitter Menggunakan Metode Naïve Bayes Classifier. IT Journal Research and Development, 5(1), 72–86. https://doi.org/10.25299/itjrd.2020.vol5(1).5159
scikit-learn. (2021). SVM. Scikit-Learn.Org. https://scikit-learn.org/stable/modules/svm.html
Singh, S. N., & Sarraf, T. (2020). Sentiment analysis of a product based on user reviews using random forests algorithm. Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and Engineering, 112–116. https://doi.org/10.1109/Confluence47617.2020.9058128
Stephenie, Warsito, B., & Prahutama, A. (2020). Sentiment Analysis on Tokopedia Product Online Reviews Using Random Forest Method. E3S Web of Conferences, 202, 1–10. https://doi.org/10.1051/e3sconf/202020216006
Suresh, A., & Bharathi, C. R. (2016). Sentiment classification using decision tree based feature selection. International Journal of Control Theory and Applications, 9(36), 419–425.
Towkeershah40. (2021). Role of processor in a PC. GeeksforGeeks. https://www.geeksforgeeks.org/role-of-processor-in-a-pc/
Uy, M. R., & Thomas, J. (2021). AMD Processors: the best AMD CPUs in 2021. TechRadar. https://www.techradar.com/news/amd-processors-the-best-amd-cpus-in-2021
Wahyudi, R., & Kusumawardana, G. (2021). Analisis Sentimen pada Aplikasi Grab di Google Play Store Menggunakan Support Vector Machine. Jurnal Informatika, 8(2), 200–207. https://doi.org/10.31294/ji.v8i2.9681
Yang, L., Li, Y., Wang, J., & Sherratt, R. S. (2020). Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning. IEEE Access, 8, 23522–23530. https://doi.org/10.1109/ACCESS.2020.2969854
Published
2021-12-28
How to Cite
Erlin, Josef Sianturi, Alyauma Hajjah, & Agustin. (2021). Analisis Sentimen Prosesor AMD Ryzen menggunakan Metode Support Vector Machine. SATIN - Sains Dan Teknologi Informasi, 7(2), 129-141. https://doi.org/10.33372/stn.v7i2.804
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