Analisis Komputasi Paralel pada Image Encoding Framework untuk Konversi Citra Data Deret Waktu Sistem Kontrol Industri - Rahadian, Helmy; Wahid, Muhammad Rizalul; Arifin, Zaenal (Majalah Ilmiah Teknologi Elektro; Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro, 2023)
Sensors in industrial control systems send a series of data each time, known as time series data, to the controller. The data contains important information for the controller to determine the control signal for the actuator. The appearance of anomalies in time series data can be detected using the Convolutional Neural Network (CNN) method utilizing image encoding techniques such as Gramian Angular Field (GAF) and Markov Transition Field (MTF). This technique converts time series data into images through data preparation, encoding, and conversion. Dividing extensive data into many smaller segments requires repeated encoding and conversion processes. Repeated processes that are done serially take a long time, which slows down the detection of anomalies and the responses that must be taken. This research applies parallel computation with Joblib and Mpire libraries on the GAF and MTF image encoding provided by the Python-based pyts library. The n_jobs configuration determines the number of CPU logical cores used to execute the program. According to the number of CPU logic cores of the computer, applying the value of n_jobs = 8 can save an average processing time of 63% (Joblib) and 49% (Mpire), which theoretically will be able to detect anomalies that occur at least every 62.73 ms (Joblib) and 86.20 ms (Mpire) compared to 167.51 ms in serial computing.