PENERAPAN ALGORITMA FILTER UNTUK MENINGKATKAN AKURASI PEMBACAAN SENSOR SUHU MLX90614 PADA OBJEK DENGAN ELIMINASI PENGARUH SUHU LINGKUNGAN
DOI:
https://doi.org/10.23960/jitet.v13i3.6621Abstract Views: 89 File Views: 73
Abstract
Sensor suhu memainkan peran penting dalam berbagai aplikasi, terutama dalam pengukuran suhu tubuh dan lingkungan. Salah satu sensor yang banyak digunakan adalah MLX90614, sensor inframerah non-kontak ini mudah digunakan, meskipun akurasinya dipengaruhi oleh suhu lingkungan dan jarak pengukuran. Penelitian ini bertujuan untuk meningkatkan akurasi pembacaan suhu dengan menerapkan beberapa algoritma filter, yaitu Low Pass, Exponential Moving Average (EMA), Moving Average, Median, dan Kalman Filter. Berdasarkan analisis menggunakan Mean Squared Error (MSE), Moving Average menunjukkan kinerja terbaik dengan MSE terendah sebesar 10.5634, diikuti oleh Median Filter (10.8855). Filter lainnya, seperti EMA dan Low Pass, memiliki MSE yang lebih tinggi (40.2701), sementara Kalman Filter menunjukkan MSE tertinggi (799.6851). Hasil penelitian ini menunjukkan bahwa Moving Average adalah filter paling efektif dalam mengeliminasi pengaruh suhu lingkungan dan jarak, sehingga dapat digunakan untuk meningkatkan akurasi pengukuran suhu menggunakan sensor MLX90614.
Downloads
References
I. H. Tanjung, “Termometer Non Contact Menggunakan Sensor Suhu Infrared MLX90614 Sebagai Pengukur Suhu Tubuh Berbasis SMS GateWay,” IJEERE: Indonesian Journal of Electrical Engineering and Renewable Energy, vol. 2, no. 1, pp. 19–28, Jul. 2022, doi: 10.57152/ijeere.v2i1.176.
T. Hidayat, S. Riki Mustafa, and Basorudin, “Bilik Sistem Pengukuran Suhu Tubuh Dan Semprotan Hand Sanitizer Otomatis Menggunakan Sensor Suhu Mlx90614,” Riau Jurnal Teknik Informatika, vol. 3, no. 2, pp. 34–38, Jul. 2024, doi: 10.61876/rjti.v3i2.2881.
A. Ismamudi and W. Pramusinto, “PENERAPAN NODEMCU DAN SENSOR SUHU MLX90614 UNTUK HAND SANITIZER OTOMATIS BERBASIS IOT,” SKANIKA, vol. 6, no. 1, pp. 1–11, Jan. 2023, doi: 10.36080/skanika.v6i1.2995.
A. H. Kuspranoto and M. Ulin Nuha ABA, “PENERAPAN DIGITAL MOVING AVERAGE FILTER PADA SKIN SENSOR INFANT WARMER UNTUK MENGUKUR SUHU,” MEDIKA TRADA, vol. 5, no. 2, pp. 58–65, Dec. 2024, doi: 10.59485/jtemp.v5i2.93.
A. S. Fiddariani and B. Sumanto, “Studi Penerapan Filter Digital pada Sistem Pemantau Parameter di Kolam Ikan,” Jurnal Listrik, Instrumentasi, dan Elektronika Terapan, vol. 5, no. 1, p. 30, Apr. 2024, doi: 10.22146/juliet.v5i1.87377.
M. Y. Baihaqi and W. Wijaya, “Penerapan Filter Kalman untuk Meningkatkan Akurasi dan Presisi Sensor Suhu LM35,” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 1, no. 1, pp. 93–101, Apr. 2021, doi: 10.24002/konstelasi.v1i1.4282.
D. B. Nugroho, M. Dirayati, D. Y. L. Silaban, R. Oktavia, and O. F. Purba, “Analisis Pengaruh Jarak Elektrode terhadap Kinerja Sensor IDE Berlapis Grafit untuk Mengukur Suhu,” Jurnal Teori dan Aplikasi Fisika, vol. 12, no. 02, Jul. 2024, doi: 10.23960/jtaf.v12i02.429.
L. Wang, H.-B. Tao, H. Dong, Z.-B. Shao, and F. Wang, “A Non-Linear Temperature Compensation Model for Improving the Measurement Accuracy of an Inductive Proximity Sensor and Its Application-Specific Integrated Circuit Implementation,” Sensors, vol. 20, no. 17, p. 5010, Sep. 2020, doi: 10.3390/s20175010.
I. Paramudita, T. A. Wahyu Wijanarko, A. P. Amanda, and P. Bakti, “PENGARUH JARAK UKUR DAN JENIS TERMOMETER INFRAMERAH PADA HASIL PENGUKURAN SUHU TUBUH SEBAGAI SKRINING AWAL COVID-19,” Jurnal Standardisasi, vol. 23, no. 2, p. 133, Jul. 2021, doi: 10.31153/js.v23i2.884.
W. O. S. N. Alam, A. N. Aliansyah, F. E. Larobu, L. Mulyawati, A. Asminar, and I. Galugu, “Tingkat akurasi Sensor AMG8833 dan Sensor MLX90614 dalam Mengukur Suhu Tubuh,” JTEV (Jurnal Teknik Elektro dan Vokasional), vol. 8, no. 1, p. 169, May 2022, doi: 10.24036/jtev.v8i1.114543.
A. Imran and M. Rasul, “PENGEMBANGAN TEMPAT SAMPAH PINTAR MENGGUNAKAN ESP32,” 2020.
M. O. Sibuea, “Pengukuran suhu dengan sensor suhu inframerah MLX90614 berbasis Arduino,” 2019.
S. A. Nugroho, S. Aprilia, F. Anindyahadi, and N. S. Budi, “PERANCANGAN ALAT UKUR KADAR pH DAN SUHU AIR BERBASIS ARDUINO UNO,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, no. 2, Apr. 2025, doi: 10.23960/jitet.v13i2.6398.
Y. Ju, B. Wahlberg, and H. Hjalmarsson, “Excess Mean Squared Error of Empirical Bayes Estimators,” Mar. 2025, [Online]. Available: http://arxiv.org/abs/2503.11863
S. Bernard, J. Wang, and M. Fuge, “DETC2022-90065 MEAN SQUARED ERROR MAY LEAD YOU ASTRAY WHEN OPTIMIZING YOUR INVERSE DESIGN METHODS,” 2022.
P. Vos, “Rethinking Mean Square Error: Why Information is a Superior Assessment of Estimators,” Dec. 2024, [Online]. Available: http://arxiv.org/abs/2412.08475
M. F. Wicaksono, S. Syahrul, and M. Dwi Rahmatya, “Development of Laboratory Equipment Inventory System Using Radio Frequency and Internet of Things,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 7, no. 2, p. 249, Aug. 2021, doi: 10.26555/jiteki.v7i2.21114.
S. Makridakis and S. C. Wheelwright, “Adaptive Filtering: An Integrated Autoregressive/Moving Average Filter for Time Series Forecasting,” Journal of the Operational Research Society, vol. 28, no. 2, pp. 425–437, Jul. 1977, doi: 10.1057/jors.1977.76.
F. R. G. Cruz, C. C. Paglinawan, C. N. V. Catindig, J. C. B. Lamchek, D. D. C. Almiranez, and A. F. Sanchez, “Photoplethysmography Circuit Design for Peak-to-Peak Voltage Monitoring via Arduino Uno with Moving Average Filter,” in Proceedings of the 2019 9th International Conference on Biomedical Engineering and Technology, New York, NY, USA: ACM, Mar. 2019, pp. 6–12. doi: 10.1145/3326172.3326191.
Amneh A. Al-Mbaideen, “Application of Moving Average Filter for the Quantitative Analysis of the NIR Spectra,” Journal of Analytical Chemistry, vol. 74, no. 7, pp. 686–692, Jul. 2019, doi: 10.1134/S1061934819070013.
Amneh A. Al-Mbaideen, “Application of Moving Average Filter for the Quantitative Analysis of the NIR Spectra,” Journal of Analytical Chemistry, vol. 74, no. 7, pp. 686–692, Jul. 2019, doi: 10.1134/S1061934819070013.
M. R. Abujiya, M. H. Lee, and M. Riaz, “Improving the Performance of Exponentially Weighted Moving Average Control Charts,” Qual Reliab Eng Int, vol. 30, no. 4, pp. 571–590, Jun. 2014, doi: 10.1002/qre.1509.
M. I. Al-arrafi, A. Nurdiansyah, and A. Ramadhanu, “Optimalisasi Metode Median Filter untuk Mereduksi Noise pada Citra Kematangan Buah Jambu Madu,” Journal of Education Research, vol. 5, no. 4, pp. 5954–5958, Dec. 2024, doi: 10.37985/jer.v5i4.1858.
L. A. Kusumo, “Implementation of Median Filter in Data Processing of Temperature and Humidity Monitoring System with DHT 11 and DHT 22 Sensors,” Sistem Kendali & Jaringan) E-ISSN, vol. 4, pp. 2808–3520, 2025, doi: 10.58982/krisnadana.v4i2.723.
S. Dey, R. Bhattacharya, F. Schwenker, and R. Sarkar, “Median Filter Aided CNN Based Image Denoising: An Ensemble Approach,” Algorithms, vol. 14, no. 4, p. 109, Mar. 2021, doi: 10.3390/a14040109.
S. Soltani, H. Pakniat, and N. Yasrebi, “A wide stopband, high selectivity microstrip low-pass filter for wireless communications,” AEU - International Journal of Electronics and Communications, vol. 185, p. 155443, Oct. 2024, doi: 10.1016/j.aeue.2024.155443.
P. S. Tomar and M. S. Parihar, “The Design and Investigation of the Low-Pass Filter with High Selectivity and Ultra-Wide Stopband,” IETE J Res, vol. 70, no. 3, pp. 2366–2371, Mar. 2024, doi: 10.1080/03772063.2023.2185303.
S. Yildiz, A. Aksen, S. Kilinc, and S. B. Yarman, “Low pass filter design with improved stop‐band suppression and synthesis with transformer‐free ladders,” IET Circuits, Devices & Systems, vol. 15, no. 6, pp. 504–510, Sep. 2021, doi: 10.1049/cds2.12045.
E. J. Armando, D. Hanyurwimfura, O. Gatera, and A. Nduwumuremyi, “Enhancing Agricultural Internet of Things Data Accuracy: Evaluating Kalman Filter-Based Sensor Denoising Techniques,” Journal of Biosystems Engineering, vol. 50, no. 1, pp. 47–58, Mar. 2025, doi: 10.1007/s42853-025-00252-5.
B. Satria and M. Debora Br Barus, “Implementation of Angklung Beat Density With Arduino and Piezoelectric Sensor Using Kalman Filter Applied for Reduce Noise Sensor,” Technology and Social Sciencesinjects, vol. 3, pp. 346–5, Oct. 2022.
D. Sbarbaro, T. A. Johansen, and J. Yañez, “Adaptive Kalman Filtering for Compensating External Effects in On-Line Spectroscopic Measurements,” Sensors, vol. 25, no. 8, p. 2513, Apr. 2025, doi: 10.3390/s25082513.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jurnal Informatika dan Teknik Elektro Terapan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
