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Abstrak Virus Corona telah menjadi masalah kesehatan yang marak terjadi sejak awal tahun 2020, bermula dari negara China, Wuhan. Indonesia telah menjadi salah satu negara di Asia dengan angka kematian ( Death Rate ) tertinggi di dunia. Banyaknya kasus yang masih belum terdeteksi dan terlaporkan membuat situasi di Indonesia menjadi lebih buruk. Jumlah pasien yang terus meningkat dan keterbatasan fasilitas, alat dan tenaga kesehatan menjadi kendala bagi Indonesia untuk menghadapi COVID-19. Berdasarkan permasalahan di atas, penulis tertarik membuat sistem prediksi jumlah pasien COVID-19 menggunakan metode Trend Least Square berbasis web . Proses prediksi dilakukan dengan menggunakan tool RStudio. Hasil prediksi akan diimplementasikan ke dalam website. Analisa hasil prediksi dilakukan dengan menghitung nilai Mean Absolute Percentage Error (MAPE). Sayangnya, nilai rata-rata persentase MAPE prediksi pasien COVID-19 di Indonesia sebesar 59,2 % menunjukkan prediksi dengan metode Trend Least Square tergolong buruk. Sistem prediksi ini dapat memprediksi pasien COVID-19 sesuai waktu yang tersedia dan terproses sebelumnya menggunakan RStudio. Uji coba website menggunakan metode Black Box memiliki hasil sukses untuk setiap skenario uji coba. Tujuan dari penelitian ini adalah membuat sistem prediksi jumlah pasien COVID-19 menggunakan metode Trend Least Square berbasis web. Sistem ini dapat memprediksi perkembangan jumah pasien yang terjangkit, sembuh, dan meninggal terkait COVID-19 khususnya di wilayah Indonesia, sehingga pemerintah daerah dapat menyiapkan sarana dan prasarana serta kebijakan yang tepat untuk menangani epidemi COVID-19. Kata Kunci: COVID-19, prediksi, trend least square, rstudio, data mining Abstract The Coronavirus has becomed a rife health problem since the beginning of 2020, starting in China, Wuhan. Indonesia has become one of the countries in Asia with the highest death rate in the world. The large number of cases that have not been detected and reported has made the situation in Indonesia even worse. The increasing number of patients and limited facilities, equipment, and health personnel are obstacles for Indonesia to deal with COVID-19. Based on the  problems, the authors are interested in making a prediction system for the number of COVID-19 patients using the web-based Trend Least Square method. The prediction process is carried out using the RStudio tool. The prediction results will be implemented on the website. Analysis of the prediction results is done by calculating the value of Mean Absolute Percentage Error (MAPE). Unfortunately, the average value of the predicted MAPE percentage for COVID-19 patients in Indonesia is 59.2%, indicating that the prediction using Trend Least Square method is poor. This prediction system can predict COVID-19 patients according to the available time and are processed in advance using RStudio. Testing the website using the Black Box method has successful results for each test scenario. The purpose of this study was to create a prediction system for COVID-19 patients using the web-based Trend Least Square method. This system predicts the development of the number of patients who are infected, recovered and related to COVID-19, especially in the Indonesian region, so that local governments can prepare the right facilities and infrastructure and policies for the COVID-19 epidemic. Keywords: COVID-19, prediction, trend least square, rstudio, data mining

Sistem Prediksi Jumlah Pasien Covid-19 Menggunakan Metode Trend Least Square Berbasis Web