SMART RISK LEVEL PREDICTION PENYEBARAN COVID-19 BERDASARKAN SUHU TUBUH DAN JUMLAH CALON PENUMPANG BUS DENGAN METODE FUZZY LOGIC

RAHMA, NUR AMALIA (2021) SMART RISK LEVEL PREDICTION PENYEBARAN COVID-19 BERDASARKAN SUHU TUBUH DAN JUMLAH CALON PENUMPANG BUS DENGAN METODE FUZZY LOGIC. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

In early 2020, the world was shocked by the outbreak of a new pneumonia known as Coronavirus Disease 2019 (COVID-19). With the increasing number of the spread of COVID-19, a lot of research and new regulations are being made. One of them is Andrio's research entitled Fuzzy Logic Assisted COVID19 Safety Assessment of Dental Care and PERMENHUB No.41 of 2020 concerning transportation control in order to prevent the spread of COVID-19. Based on the journal and PERMENHUB, the authors developed research using the Fuzzy method, namely Fuzzy Mamdani, to predict the Risk Level of potential passengers spreading COVID-19 on public transportation. This Risk Level value can be used to control automatic doors. Where the Risk Level is predicted by the Fuzzy Logic method based on the number of prospective passengers and the condition of the prospective passengers. If the results of the Fuzzy Risk Level prediction are high, the door will not open and vice versa. Based on the tests carried out, it is known that the prototype has succeeded in making a Risk Level prediction system for potential passengers with Fuzzy Logic, where this system has an error of 0.31% compared to the MATLAB simulation results. The higher the body temperature and the number of passengers, the higher the Risk Level value. Based on the Risk Level value, the door prototype has been successfully controlled automatically and accordingly. Where the door will not open if the Risk Level of the prospective passengers is high, namely 65-100%. Based on MFRC-522 testing, it is known that this RFID module can receive a signal card with a maximum distance of 2.5 cm and the MLX90615 temperature sensor has the best distance at 4 cm with an error of 0.1%. The monitoring system for the number of passengers has 100% data accuracy. While the bus position monitoring system can be said to be not good because the NEO 6M GPS Module takes a long time to update the Bus position, which is an average of 4.57 minutes even though this GPS has 99% accuracy. Keywords: Body Temperature, COVID-19, Fuzzy Logic, GPS NEO 6M, MLX90615, Monitoring Sistem, Number of Passengers, RFID MFRC-522, Risk Leve Pada awal tahun 2020, dunia dikejutkan dengan mewabahnya pneumonia baru yang dikenal dengan Coronavirus Disease 2019 (COVID-19). Dengan semakin tinggi angka penyebaran COVID-19 banyak penelitian dan peraturan baru yang dibuat. Salah satunya penelitian Andrio yang berjudul Fuzzy Logic Assisted COVID19 Safety Assessment of Dental Care dan PERMENHUB No.41 Tahun 2020 tentang pengendalian transportasi dalam rangka pencegahan penyebaran COVID-19. Berdasarkan jurnal dan PERMENHUB tersebut, penulis mengembangkan penelitian dengan menggunakan metode Fuzzy yaitu Fuzzy Mamdani untuk memprediksi Risk Level calon penumpang menyebarkan COVID-19 di transportasi umum. Nilai Risk Level ini dapat digunakan untuk mengendalikan pintu otomatis. Dimana Risk Level ini diprediksi dengan metode logika Fuzzy berdasarkan jumlah calon penumpang dan kondisi calon penumpang. Apabila hasil prediksi Fuzzy Risk Level tinggi maka pintu tidak akan terbuka dan sebaliknya. Berdasarkan pengujian yang dilakukan diketahui bahwa prototype telah berhasil membuat sistem prediksi Risk Level calon penumpang dengan logika Fuzzy, dimana sistem ini memiliki erorr 0.31% dibandingkan dengan hasil simulasi MATLAB. Semakin tinggi suhu tubuh dan jumlah penumpang maka semakin tinggi nilai Risk Level. Berdasarkan nilai Risk Level tersebut prototype pintu telah berhasil dikendalikan secara otomatis dan sesuai. Dimana pintu tidak akan terbuka apabila Risk Level calon penumpang tinggi yaitu 65-100%. Berdasarkan pengujian MFRC-522 diketahui bahwa modul RFID ini dapat menerima signal card dengan jarak maksimum 2.5 cm dan sensor suhu MLX90615 memiliki jarak terbaik pada 4 cm dengan erorr 0.1%. Sistem monitoring jumlah penumpang memiliki keakuratan data 100%. Sedangkan sistem monitoring posisi bus dapat dikatakan kurang baik dikarenakan Module GPS NEO 6M membutuhkan waktu cukup lama untuk memperbarui posisi Bus yaitu rata-rata 4.57 menit walaupun GPS ini memilki keakuratan 99%. Kata Kunci: COVID-19, GPS NEO 6M, Jumlah Penumpang, Logika Fuzzy, MLX90615, RFID MFRC-522, Risk Level, Sistem Monitoring, Suhu Tubuh

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