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All Journal Informatik : Jurnal Ilmu Komputer G-Tech : Jurnal Teknologi Terapan
Nuryanti
Politeknik Manufaktur Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Rancang Bangun Sistem Akuisisi Data Untuk Dashboard Android Pada Sepeda Motor Listrik Ridwan; Nuryanti; Rifqi Radifan
G-Tech: Jurnal Teknologi Terapan Vol 7 No 2 (2023): G-Tech, Vol. 7 No. 2 April 2023
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (515.292 KB) | DOI: 10.33379/gtech.v7i2.2375

Abstract

Sistem akuisisi data saat ini telah menjadi tren teknologi yang banyak dijumpai dalam kehidupan, begitu pun pada sektor otomotif. Tujuan penelitian ini ialah melakukan rancang bangun sistem akuisisi data untuk mengirimkan data sepeda motor listrik kepada dashboard android dengan memanfaatkan protokol komunikasi data serial yaitu Universal Asynchronus Receiver Trasnmitter (UART). Sebuah perangkat Raspberry PI digunakan pada 1 sepeda motor listrik untuk melakukan tugas dalam penerimaan data dari berbagai perangkat sepeda motor listrik, melakukan pengolahan data serta melakukan pengiriman data yang telah diolah kepada sistem dashboard android melalui perangkat USB TTL FTDI. Penelitian ini telah berhasil dalam merancang sistem akuisisi data untuk mengirimkan data sepeda motor listrik kepada dashboard android sehingga dashboard dapat menampilkan fitur lebih lengkap daripada dashboard konvensional dengan rata-rata persentase error jeda waktu komunikasi data serial sebesar 1,072% dimana parameter ditentukan pada 0,1 detik.
Implementation Of Kalman Filter at IoT Animal Weighing Nuryanti; Danu Jaya Saputro; Ismail Rokhim; Hendy Rudiansyah; Sandy Bhawana Mulia; Wahyu Adhie Candra; M Wahyu Firmansyah
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.13604

Abstract

Animal weighing is an important aspect of livestock farming, as it plays a role in feed determination, growth monitoring, and economic evaluation. However, accuracy is often compromised by animal movement, which causes fluctuations in sensor data and unstable readings, making it difficult to determine the actual weight. To address this, this study proposes an IoT-based animal weighing system equipped with a Kalman Filter algorithm to reduce noise and improve measurement stability. The system utilizes four 200 kg load cells, connected via HX711 and controlled by an ESP32 microcontroller, which is supported by an RFID module for automatic animal identification, an RTC for time logging, and Firebase as a cloud storage platform with real-time visualization capabilities through Node-RED. Experimental for moving object weighing results show that the Kalman Filter reduces measurement errors to less than 2% and maintains the coefficient of variation below 2%, demonstrating high precision and stability. Therefore, this system is proven effective for automatic, accurate, and remote-accessible animal weight monitoring, with strong potential for implementation in modern livestock industries.
Co-Authors Danu Jaya Saputro Hendy Rudiansyah Ismail Rokhim M Wahyu Firmansyah Ridwan Rifqi Radifan Sandy Bhawana Mulia Wahyu Adhie Candra