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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Muhammad Auzan
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

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Spectrogram Window Comparison: Cough Sound Recognition using Convolutional Neural Network Dzikri Rahadian Fudholi; Muhammad Auzan; Novia Arum Sari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.75697

Abstract

 Cough is one of the most common symptoms of diseases, especially respiratory diseases. Quick cough detection can be the key to the current pandemic of COVID-19. Good cough recognition is the one that uses non-intrusive tools such as a mobile phone microphone that does not disable human activities like stick sensors. To do sound-only detection, Deep Learning current best method Convolutional Neural Network (CNN) is used. However, CNN needs image input while sound input differs (one dimension rather than two). An extra process is needed, converting sound data to image data using a spectrogram. When building a spectrogram, there is a question about the best size. This research will compare the spectrogram's size, called Spectrogram Window, by the performance. The result is that windows with 4 seconds have the highest F1-score performance at 92.9%. Therefore, a window of around 4 seconds will perform better for sound recognition problems.
Classification Methods Performance On Logistic Package State Recognition Muhammad Auzan; Dzikri Rahadian Fudholi; Paulus Josianlie P; M Ridho Fuadin
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 4 (2023): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.82697

Abstract

 In the distribution sector, logistic package experience activities, such as transport, distribution, storage, packaging, and handling. Even though those processes have reasonable operational procedures, sometimes the package experience mishandling. The mishandling is hard to identify because many packages run simultaneously, and not all processes are monitored. An Inertial Measurement Unit (IMU) is installed inside a package to collect three acceleration and rotation data. The data is then labeled manually into four classes: correct handling, vertical fall, and thrown and rotating fall. Then, using cross-validation, ten classifiers were used to generate a model to classify the logistic package status and evaluate the accuracy score. It is hard to differentiate between free-fall and thrown. The classification only uses the accelerometer data to minimize the running time. The correct handling classification gives a good result because the data pattern has few variations. However, the thrown, free-fall and rotating data give a lower result because the pattern resembles each other. The average accuracy of the ten classifications is 78.15, with a mean deviation of 4.31. The best classifier for this research is the Gaussian Process, with a mean accuracy of 94.4 % and a deviation of 3.5 %.
Co-Authors Dzikri Rahadian Fudholi M Ridho Fuadin Novia Arum Sari Paulus Josianlie P