ANALYZING TEMPEARTURE ANOMALIES IN MONITORING DATA USING CONVOLUTIONAL NEURAL NETWORK

Nurhidayah Puteri
Widi Astuti
Aditya Firman Ihsan


DOI: https://doi.org/10.29100/jipi.v10i1.5616

Abstract


Temperature is a tool that shows the degree or measure of how hot or cold an object is. Incorrect temperature measurement can be fatal and cause various problems. Abnormal temperatures can prevent the temperature detection system from running optimally. Therefore, it is necessary to classify temperatures into normal and anomalous. Machine learning can be used as an alternative for temperature classification. By utilizing machine learning methods, one of which is Convolutional Neural Network. 3688569 temperature data were tested, dividing the results into 80% training data and 20% testing data. Accuracy, Precision, Recall, and F1 Score get a score of 100% and the CNN model graph is very good.

Keywords


Convolutional Neural Network; Anomaly; Temperature; Classification; Machine Learning

Full Text:

PDF

Article Metrics :

References


G. Gethin, J. D. Ivory, D. Sezgin, H. Muller, G. M. O'Connor and A. Vellinga, "What is the ‘Normal’ Wound Bed Temperature? A Scoping Review and New Hypothesis," Wound Repair and Regeneration, vol. 29, no. 5, pp. 843-847, September 2021.

W. Kurniasih, "Pengertian Suhu: Rumus, Faktor, Alat Ukur dan Skala," 2021. [Online]. Available: https://www.gramedia.com/literasi/pengertian-suhu/.

R. P. Rasyid, M. Todingan and M. Sirman, "Analisis Hubungan Massa Jenis Dan Suhu Terhadap Kalkukasi," Andromeda, vol. 6, no. 1, 2022.

D. Y. Pimenov, A. Bustillo, S. Wojciechowski, V. S. Sharma, M. K. Gupta and M. Kuntoğlu, "Artificial Intelligence Systems for Tool Condition Monitoring in Machining: Analysis and Critical Review," Journal of Intelligent Manufacturing, vol. 34, no. 5, 2023.

Y. Bai, Y. Duan, M. Qian, S. Zhu, X. Ma, Y. Yang and H. Gao, "Temperature Dependence of Electrical Properties and Conduction Mechanism of SiNx-Based Resistive Random Access Memory," Physica Status Solidi A: Applications and Materials Science, vol. 220, no. 21, 2023.

B. M. Erland, . A. K. Thorpe and J. A. Gamon, "Recent Advances Toward Transparent Methane Emissions Monitoring: A Review," Environmental Science & Technology, vol. 56, no. 23, 2022.

J. Jhony, S. Amos and R. Prabhu, "Optical Fibre-Based Densors for Oil and Gas Applications," Sensors, vol. 21, no. 18, 2021.

H. Chen, "Data Anomaly Diagnosis Method of Temperature Sensor Based on Deep Neural Network," Mobile Information Systems, 2022.

C. Liu, X. Su and C. Li, "Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining," Electronics, vol. 10, no. 3, 2021.

A. T. M. Fisch, I. A. Eckley and P. Fearnhead, "A Linear Time Method For The Detection of Collective and Point Anomalies," Statistical Analysis and Data Mining, vol. 15, no. 4, 2022.

V. Mahato, M. Obeidi, D. Brabazon and P. Cunningham, "Detecting Voids in 3D Printing Using Melt Pool Time Series Data," Journal of Intelligent Manufacturing, vol. 33, no. 3, 2022.

S. Maiti and R. K. Chiluvuru, "A Deep CNN-LSTM Model for Predicting Interface Depth From Gravity Data Over Thrust and Fold Belts of North East India," Journal of Asian Earth Sciences, vol. 259, 2024.

K. Tzoumpas, A. Estrada, P. Miraglio and P. Zambelli, "A Data Filling Methodology for Time Series Based on CNN and (Bi)LSTM Neural Networks," IEEE Access, vol. 12, 2024.

I. Kurniawan, L. S. Silaban and D. Munandar, "Implementation of Convolutional Neural Network and Multilayer Perceptron in Predicting Air Temperature in Padang," Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 6, pp. 1165-1170, December 2020.

V. Farhangmehr, J. H. Cobo, A. Mohammadian, P. Payeur, H. Shirkhani and H. Imanian, "A Convolutional Neural Network Model for Soil Temperature Prediction under Ordinary and Hot Weather Conditions: Comparison with a Multilayer Perceptron Model," Sustainability, vol. 15, no. 10, 2023.

I. A. Rachimi and F. Utaminingrum, "Deteksi Masker dan Suhu Tubuh untuk Kendali Portal Otomatis Menggunakan CNN sebagai Pencegahan Penularan SARS-CoV-2," vol. 5, no. 8, pp. 3472-3477, 2021.

E. Tanuwijaya, R. L. Lordianto and R. A. Jasin, "Recognition Of Human Faces In Video Conference Applications Using The CNN Pipeline," Jurnal Teknik Informatika, vol. 3, no. 2, pp. 421-427, April 2022.

M. S. Gumilang and D. Avianto, "Recognition of Real-Time Handwritten Characters Using Convolutional Neural Network Architecture," Jurnal Teknik Informatika, vol. 4, no. 5, pp. 1143-1150, October 2023.