This study aims to develop an automatic detection system to improve lift safety through early detection and rapid response to sling failure. The research method uses an experimental approach by designing a 3-story lift prototype that integrates a Raspberry Pi Pico microcontroller as the main processing unit, an HC-SR04 ultrasonic sensor to detect lift position, a limit switch to detect sling failure, and a mechanical braking system using a servo motor. The system is equipped with an I2C LCD for real-time display, Telegram notifications for remote monitoring, and a buzzer alarm as an early warning. The test results show that the ultrasonic sensor has a high accuracy of 97.58% with an average error of 2.42%. The system successfully detects sling failure and activates mechanical braking automatically, preventing the basket from falling freely and allowing it to stop on the nearest floor. All control functions such as navigation buttons, motor rotation direction, and limit switch accuracy work well. This system provides an innovative solution to improve elevator safety through the integration of automatic detection technology, real-time monitoring, and direct physical response that can be applied to the development of future elevator safety technology.

Sistem pencatatan data dibutuhkan sebagai tindak lanjut aktifitas monitoring yang seringkali hanya menampilkan nilai-nilai pembacaan sensor hasil pengolahan mikrokontroler. Terdapat beberapa pilihan yang bisa digunakan, yaitu penyimpanan data secara langsung atau melalui platform Internet of things. Penelitian ini bertujuan merancang pencatatan data sensor suhu non kontak MLX90614 berdasarkan kartu identitas personal dengan memanfaatkan modul MFRC522 RFID. Prototipe ini menggunakan media kotak berukuran 20 x 10 x 5 cm yang diletakkan di dinding. Peletakan kedua modul diatur supaya pengguna dapat menjalankan kedua fungsi sekaligus, yaitu pemindaian suhu tubuh secara non kontak dan identifikasi kartu. Pengolahan data kedua masukan modul dijalankan oleh mikrokontroler ESP32 yang sekaligus juga dapat menjalankan pencatatan data secara online melalui platform Google sheet. Pencatatan data secara online dilakukan setiap kali ada pengguna yang melakukan pemindaian. Keluaran pengolahan sistem dibandingkan dengan thermo gun menghasilkan selisih error pembacaan antara 0,2 – 0,3 oC.