Pengurutan data merupakan salah satu teknik dalam struktur data, dan struktur data tentu memiliki berbagai macam algoritmanya. Pada aplikasi e-commerce tentu sangat membutuhkan algoritma pengurutan data yang baik dan efisien dari segi performa kecepatan dan juga akurasi keakuratan pengurutan data. Pada penelitian ini dilakukan pengujian performa algoritma pengurutan data yaitu Algoritma Bubble Sort dan Quick Sort yang mana media pendukung pengujiannya adalah aplikasi e-commerce yang dibangun dengan framework Flutter dan Bahasa pemrograman Dart. Dataset yang digunakan diperoleh dari Optik Citra Abadi dan official store Tokopedia yaitu Optik Melawai, Optik Kasoem, dan Optik Seis. Hasil pengujian dari kedua algoritma yang telah dibagi menjadi 5 iterasi dan 10 kali pengambilan data dari setiap iterasi menunjukan bahwa terdapat perbedaan waktu eksekusi untuk panjang data 250, dimana algoritma bubble sort membutuhkan 0.468421 detik, sedangkan algoritma quick sort hanya membutuhkan waktu  0.008912 detik. Kedua algoritma tersebut memperoleh 100% akurasi dalam proses pengurutan data.

Based on information on the <span>BNPB website on 2 September 2020, the positive rate for coronavirus disease (COVID-19) in Indonesia reached 25.25% on 30 August 2020. This is a big challenge for the Indonesian government to reduce the positivity rate to meet the standards safe accepted by World Health Organization (WHO) is 5%. To ensure the accuracy of government policies, accurate data predictions are needed. Therefore, the prophet's machine learning algorithm can be used to see trends in the spread of COVID-19 in the next one year. This algorithm has a fairly high level of accuracy because the data contains time variables which are adjusted to the dataset. In several previous research, the dataset was vast uncertain and small. Meanwhile in this research, data was taken from 2 March 2020 to 12 February 2021 on the KawalCOVID19 website. This data is used to predict from 13 February 2021 to 12 February 2022. There are 3 data used; namely data confirmed, recovered and died. Based on the analysis, the confirmed patient was 22.60-42.11%, died amounted to 21.67%-39.00%, and recovered by 22.53-41.82%. The prediction percentage that the average cases died was 2.43% every day. The accuracy of data confirmed was 43.97%, died was 72.50% and recovered was 84.24%.</span>