E-learning is electronic-based learning using computers or computer-based. One e-learning application that is widely known today is Ruang Guru. One way to find out the success of an application is to do a sentiment analysis of the application. In this study, sentiment analysis was taken from Twitter social media user comments on Ruang Guru of 513 tweets, after data cleaning, with 338 tweets of positive sentiment and 175 tweets of negative sentiment. The data was extracted using the Naive Bayes (NB) algorithm, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and feature selection with the Particle Swarm Optimization (PSO) algorithm. This study compares the NB, SVM, K-NN methods without using feature selection with the NB, SVM, K-NN methods that use feature selection and compares the Area Under Curve (AUC) values of these methods to find the most optimal algorithm. The test results get the results that the best optimization application in this model is the SVO-based PSO algorithm with an accuracy value of 78.55% and AUC of 0.853. This research succeeded in getting the most effective and best algorithm in classifying positive and negative comments related to Ruang Guru.

The development of e-sports education is not just playing games, but about start making, development, marketing, research and other forms education aimed at training skills and providing knowledge in fostering character. The opinions expressed by the public can take form support, criticism and input. Very large volume of comments need to be analyzed accurately in order separate positive and negative sentiments. This research was conducted to measure opinions or separate positive and negative sentiments towards e-sports education, so that valuable information can be sought from social media. Data used in this study was obtained by crawling on social media Twitter. This study uses a classification algorithm, Naïve Bayes and Support Vector Machine. Comparison two algorithms produces predictions obtained that the Naïve Bayes algorithm with SMOTE gets accuracy value 70.32%, and AUC value 0.954. While Support Vector Machine with SMOTE gets accuracy value 66.92% and AUC value 0.832. From these results can be concluded that Naïve Bayes algorithm has a higher accuracy compared to Support Vector Machine algorithm, it can be seen that the accuracy difference between naïve Bayes and the vector machine support is 3.4%. Naïve Bayes algorithm can thus better predict the achievement of e-sports for students' learning curriculum.

Increasingly sophisticated technology brings various conveniences both in transportation, information, education to the convenience of transactions in shopping, such as the development of E-wallet can now be easily done using a smartphone. From a number of e-wallet products, researchers took a case study, which is OVO product, which is currently being discussed by many groups, especially in the capital of Jakarta today. Customers or clients who are not satisfied with the services or products offered by a company will usually write their complaints on social media or reviews on Google play. However, monitoring and organizing opinions from the public is also not easy. For this reason, we need a special method or technique that is able to categorize these reviews automatically, whether positive or negative. The algorithm used in this study is Naive Bayes Classifier (NB), with the optimization of the use of Particle Swarm Optimization Feature Selection (FS). The results of cross validation NB without FS are 82.30% for accuracy and 0.780 for AUC. Whereas for NB with FS is 83.60% for accuracy and 0.801 for AUC. Very significant improvement with the use of Feature Selection (FS) Particle Swarm Optimization.

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Aplikasi zoom cloud meetings yang mulai booming digunakan sekarang ini karena adanya pandemi virus corona, sehingga membuat semua kegiatan dilakukan secara virtual. Zoom cloud meetings merupakan aplikasi yang memiliki berbagai fitur termasuk video & audio conference. Pada penelitian ini penulis menggunakan metode Naïve Bayes dan Support Vector Machine dalam menganalisa label sentimen positif atau negatif pada ulasan para pengguna aplikasi zoom di Google Play Store. Jumlah dataset setelah prepocessing menjadi 1.007 record. Data hampir seimbang dengan label positif sebanyak 546 dan label negatif 461 ulasan. Evaluasi model menggunakan 10 fold cross validation diperoleh nilai akurasi dan nilai AUC dari masing-masing algoritma yaitu untuk NB nilai akurasi = 74,37% dan nilai AUC = 0,659. Sedangkan untuk algoritma SVM nilai akurasi = 81,22% dan nilai AUC = 0,886. Dalam penelitian ini dapat diketahui bahwa tingkat akurasi yang didapatkan algoritma Support Vector Machine (SVM) lebih unggul 6,85% dibandingkan algoritma Naïve Bayes (NB). Kata Kunci— Zoom Cloud Meetings, Google Play Store, Virus Corona, Naïve Bayes, Support Vector Machine. Abstract— Zoom cloud meetings application that began to boom is used today because of the corona virus pandemic, so that all activities are carried out virtually. Zoom cloud meetings is an application that has various features including video & audio conferencing. In this study the authors used the Naïve Bayes method and Support Vector Machine in analyzing positive or negative sentiment labels on the zoom users' reviews on the Google Play Store. The number of datasets after prepocessing is 1,007 records. The data is almost balanced with 546 positive labels and 461 negative labels. Evaluation of the model using 10 fold cross validation obtained accuracy values and AUC values from each algorithm, namely for NB, the accuracy value = 74.37% and the AUC value = 0.659. As for the SVM algorithm the accuracy value = 81.22% and the AUC value = 0.886. In this study it can be seen that the accuracy obtained by the Support Vector Machine (SVM) algorithm is 6.85% superior to the Naïve Bayes (NB) algorithm.