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.

One of the developments of information technology in Indonesia was the developments in Fintech (financial technology) that made it easy for people to access financial products, facilitated online transactions and also increased financial literacy. The development of fintech occurs when the use of cash was reduced to cashless when making payment transactions so that transactions would be more practical, easy, safe and comfortable. The purpose of this study to improve the quality of decision tree modeling and accuracy in the selection of digital payments. This research was focused on determining the fintech applications that were widely used by the people of Indonesia, namely Gopay or Ovo by comparing the advantages of applications and features of each fintech application. Optimization method of Decision Tree Algorithm (C4.5) and Particle Swarm Optimization by selecting several attributes including the level of ease of use, data security, application trust and convenience, maximum balance increase, discounts, cashback, ease of top-ups, range of existing merchants, return the money and customer complaint services. The results of the development of a decision tree algorithm based on particle swarm optimization provide a good classification and increase the validation value in the selection of digital payments.

Mathematical proof is a logically formed argument based on students' thinking process. A mathematical proof is a formal process which needs the ability of analytical thinking to solve. However, researchers still find students who complete the mathematical proof process through intuitive thinking. Students who have studied mathematical proof in the early semester should not have completed abstract algebraic proof intuitively. Therefore, the aim of this research is to explore students' thinking process in conducting mathematical proof based on Mason's framework. The instrument used to collect data was mathematical proof problems test related to abstract algebra and interviews. There are three out of 25 students who did abstract algebra through intuitive thinking as they only used two stages of the Mason's thinking framework. Then, two out of three students were chosen as the subjects of the study. The selection of research subjects is based on the student's ability to express intuitive thinking verbally process which were conducted while completing the test. It is found that students can form structural-intuitive warrant that they use to complete the mathematical proof of abstract algebra. Structural-intuitive warrant formed by students at the stage of attack and review are in the form of: institutional warrant and evaluative warrant, while at the entry and attack stage are a priori warrant and empirical warrant.

Media pembelajaran matematika berbasis android ini merupakan suatu media pembelajaran matematika dalam bentuk aplikasi pembelajaran. Media pembelajaran ini diharapkan dapat memudahkan siswa dalam belajar matematika di masa pandemic covid 19 saat ini. Penelitian ini bertujuan untuk mengembangkan media pembelajaran matematika berbasis android. Jenis penelitian ini merupakan penelitian pengembangan dengan menggunaan model ADDIE dengan beberapa tahapan yaitu analysys, design, development, implantation, evalution. Hasil penelitian menunjukkan bahwa media pembelajaran matematika berbasis android ini dikategorikan valid dengan rata-rata prsentase skor yaitu 77,94% dengan presentase rata-rata ahli media yaitu 80,88% dan presentase rata-rata ahli materi yaitu 75% sehingga media pembelajaran ini dapat diuji cobakan kepada siswa. Setelah media dinyatakan valid kemudia media yang telah dikembangkan diuji cobakan kepada siswa kemudian  siswa mengisi angket  respon  siswa yang telah disediakan oleh peneliti. Angket respon siswa menunjukkan bawa media ini praktis untuk digunakan siswa dengan prsentase rata-rata yaitu 74,63%.

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