PENINGKATAN KECERMATAN(RESOLUTION) PEMBACAAN ALAT UKUR TEKANAN UDARA PADA SENSOR BMP180 MENGGUNAKAN METODE FILTER KALMAN BERBASIS ESP8266

Arman Hati, Buulolo and Frastica, Deswardani and M.Ficky, Afrianto PENINGKATAN KECERMATAN(RESOLUTION) PEMBACAAN ALAT UKUR TEKANAN UDARA PADA SENSOR BMP180 MENGGUNAKAN METODE FILTER KALMAN BERBASIS ESP8266. JOURNAL ONLINE OF PHYSICS. ISSN E-ISSN2502-2016 (Submitted)

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Abstract

Telah dilakukan penelitian yang berfokus pada peningkatan kecermatan pengukuran tekanan udara menggunakan sensor BMP180 yang terintegrasi dengan mikrokontroler ESP8266 melalui penerapan metode filter Kalman. Penelitian ini bertujuan untuk meningkatkan kecermatan pembacaan tekanan udara pada sensor BMP180 dengan menerapkan metode Filter Kalman berbasis mikrokontroler ESP8266. Sensor BMP180 dikenal luas dalam sistem berbasis Internet of Things (IoT) karena efisiensinya, tetapi rentan terhadap gangguan (noise) seperti suhu, kelembaban, dan getaran yang dapat menurunkan akurasi pembacaan. Penelitian ini dilakukan di Laboratorium Elektronika dan Instrumentasi Fakultas Sains dan Teknologi Universitas Jambi pada bulan Oktober 2024 hingga Juni 2025. Rancang bangun sistem dilakukan dengan menggabungkan sensor BMP180, ESP8266, LCD I2C, dan keypad dalam satu alat ukur. Data dari sensor diproses melalui algoritma Kalman Filter untuk meminimalkan noise, lalu ditampilkan di LCD dan dikirim secara real-time ke platform ThingSpeak. Pengujian dilakukan pada dua kondisi lingkungan: tekanan atmosfer normal dan dalam ruang vakum buatan. Hasil menunjukkan bahwa penerapan Filter Kalman mampu menyaring fluktuasi data, menghasilkan pembacaan tekanan yang lebih halus, stabil, dan presisi. Nilai parameter yang digunakan dalam Kalman Filter adalah Q = 0.001, R = 0.0009, dan P awal = 10, berdasarkan hasil analisis statistik dari 100 data mentah. Terjadi penurunan standar deviasi dari 0,3461 hPa menjadi 0,3425 hPa, yang berarti terjadi penurunan sebesar 1,04%. Meskipun penurunan ini tidak berpengaruh secara signifikan namun Filter Kalman menunjukkan efektivitas dalam meredam noise data yang tidak diinginkan. Kesimpulannya, metode Kalman Filter efektif dalam meningkatkan resolusi dan akurasi pembacaan tekanan udara pada sensor BMP180. Alat ini berpotensi digunakan dalam pemantauan cuaca, pendidikan, serta aplikasi lingkungan dan industri berbasis IoT. This research focuses on improving the accuracy of air pressure measurement using the BMP180 sensor integrated with the ESP8266 microcontroller through the implementation of the Kalman Filter method. The aim of this research is to enhance the precision of air pressure readings from the BMP180 sensor by applying the Kalman Filter method based on the ESP8266 microcontroller. The BMP180 sensor is widely used in Internet of Things (IoT) systems due to its efficiency, but it is vulnerable to noise such as temperature fluctuations, humidity, and vibrations, which can reduce reading accuracy. The research was conducted at the Electronics and Instrumentation Laboratory, Faculty of Science and Technology, Jambi University, from October 2024 to June 2025. The system was designed by integrating the BMP180 sensor, ESP8266, I2C LCD, and keypad into a single measuring device. Sensor data were processed using the Kalman Filter algorithm to minimize noise, then displayed on the LCD and transmitted in real-time to the ThingSpeak platform. Testing was carried out under two environmental conditions: normal atmospheric pressure and an artificial vacuum chamber. The results show that the application of the Kalman Filter successfully filtered data fluctuations, producing smoother, more stable, and precise pressure readings. The Kalman Filter parameters used were Q = 0.001, R = 0.0009, and an initial P = 10, based on statistical analysis of 100 raw data points. The standard deviation decreased from 0.3461 hPa to 0.3425 hPa, indicating a reduction of 1.04%. Although the decrease is not statistically significant, the Kalman Filter proved effective in suppressing unwanted data noise. In conclusion, the Kalman Filter method is effective in enhancing the resolution and accuracy of air pressure readings using the BMP180 sensor. This device has potential applications in weather monitoring, education, and various IoT-based environmental and industrial applications. kata kunci : ESP8266; Filter Kalman; Internet of Things (IoT); Mikrokontroler; Pemantauan cuaca; Pengukuran presisi

Type: Article
Subjects: Q Science > QC Physics
Divisions: Fakultas Sains dan Teknologi > Fisika
Depositing User: ARMAN HATI BU'ULOLO
Date Deposited: 07 Jul 2025 01:50
Last Modified: 07 Jul 2025 01:50
URI: https://repository.unja.ac.id/id/eprint/81291

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