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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Giri, Gst. Ayu Vida Mastrika
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Humanities Computer Science & IT Electrical & Electronics Engineering Energy Engineering Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Musical Instrument Classification using Audio Features and Convolutional Neural Network Giri, Gst. Ayu Vida Mastrika; Radhitya, Made Leo
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8058

Abstract

This research classifies acoustic instruments using Convolutional Neural Network (CNN). We utilize a dataset from Kaggle containing audio recordings of piano, violin, drums, and guitar. The training set consists of 700 guitar, percussion, violin, and 528 piano samples. The test set contains 80 samples of each instrument. Features such as Mel spectrograms, MFCCs, and other spectral and non-spectral characteristics are extracted using the Librosa package. Three feature sets"”spectral-only, non-spectral-only, and a combined set"”are employed to evaluate the efficacy of CNN models. Various CNN configurations are tested by adjusting the number of convolutional filters, learning rates, and epochs. The combined feature set achieves the highest performance, with a validation accuracy of 71.8% and a training accuracy of 76.9%. In comparison, non-spectral features achieve a validation accuracy of 68.4%, and spectral-only features achieve 69.3%. These findings highlight the benefits of using a comprehensive feature set for accurate classification.
Application of Gated Recurrent Unit in Electroencephalogram (EEG)-Based Mental State Classification Giri, Gst. Ayu Vida Mastrika; Sanjaya ER, Ngurah Agus; Suhartana, I Ketut Gede
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8825

Abstract

The classification of mental states based on electroencephalogram (EEG) recordings has recently gained significant interest in cognitive monitoring and human-computer interaction fields. Due to high signal variability and sensitivity to noise, correct classification is still tricky, even with advances in the analysis of EEG signals. Among deep learning models, Gated Recurrent Unit (GRU) models have established great potential for sequential EEG data analysis. The applications of the GRUs are less reviewed in tasks concerning classification cases of mental states compared to hybrid and convolutional models. Based on this paper, we will propose a method for developing a model based on the GRU network trained with raw EEG data in the classification tasks of mental states of concentration and relaxed conditions. We analyzed 400 EEG recordings taken from 10 subjects within a controlled environment and collected using the Muse EEG Headband. The mean, standard deviation, skewness, kurtosis, power spectral density, zero-crossing rate, and root mean square were extracted as statistical features from the raw EEG data. After parameter tuning, the GRU-based model achieved an excellent average accuracy value of 95.94% and also yielded precision, recall, and F1-scores within the range of 0.95 to 0.97 over 5-fold cross-validation. This shows that GRU works well in classifying mental states based on the EEG data.
Co-Authors Adiriyanto, Shiennyta Florensia Dwijayana, I Gede Diva I Gede Arta Wibawa I Ketut Gede Suhartana I Komang Gede Apriana, I Komang Gede Apriana I Made Satria Bimantara I Wayan Supriana Mahendra, I Putu Fajar Tapa Ngurah Agus Sanjaya ER Radhitya, Made Leo Simarmata, Ivan Luis