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All Journal Journal of Computer Science and Informatics Engineering (J-Cosine) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD
I Gusti Bagus Arya Pradnja Paramitha
Universitas Nusa Mandiri
Computer Science & IT

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Penerapan Finite State Automata Pada Desain Vending Machine Batu Permata Sapphire Alami I Gusti Bagus Arya Pradnja Paramitha; Windu Gata; Laela Kurniawati; Eni Heni Hermaliani; Jordy Lasmana Putra
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 5 No. 2 (2022): J-SISKO TECH EDISI JULI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v5i2.5677

Abstract

Batu Sapphire adalah salah satu jenis batu permata yang ada di dunia, yang paling di cari serta di minati oleh para penggemarnya adalah batu permata Sapphire berwarna biru. Untuk memudahkan penjualan batu permata Sapphire alami adalah dengan menggunakan Vending machine. Metode penelitian dengan melakukan penggambaran Finite State Automata menggunakan Deterministic Finite Automata, perancangan Diagram State tentang fitur-fitur dan desain antarmuka saat Vending machine diimplementasikan. Hasil penelitian menunjukkan penggunaan Deterministic Finite Automata pada desain Vending machine Batu Permata Sapphire Alami sehingga didapatkan sistem penjualan batu permata pada masa depan secara efektif dan nyaman. Dalam Vending machine Batu Permata Sapphire Alami menggunakan Finite State Automata dilengkapi dengan satu metode pembayaran yaitu menggunakan uang tunai. Perancangan Vending machine Batu Permata Sapphire Alami menggunakan Finite State Automata diharapkan dapat diterapkan dan dikembangkan di sentra – sentra penjualan oleh – oleh yang menjual batu – batu permata sehingga dapat dinikmati oleh masyarakat luas.
Performance Comparison of Deep Learning Algorithm for Speech Emotion Recognition I Gusti Bagus Arya Pradnja Paramitha; Hendra Budi Kusnawan; Muji Ernawati
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 6 No 2 (2022): December 2022
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v6i2.443

Abstract

One of the problems in Speech emotion recognition is related to time series data, while the feedforward process in neural networks is unidirectional where the results from one layer are directly channeled to the next layer. This kind of feedforward process cannot store past data. Thus, if Deep Neural Network (DNN) is used for Speech emotion recognition, some problems arise, such as the speech rate of the speaker. DNN cannot analyze the existing acoustic patterns and so cannot map different levels of speech rate. Another method that can take input at once while retaining relevant data in the previous process is the Recurrent Neural Network (RNN). This paper presents the characteristics of the RNN method consisting of LSTM and GRU techniques for Speech emotion recognition using the Berlin EMODB dataset. The dataset is divided into 80% for training and 20% for testing. The feature extraction methods used are Zero crossing Rate (ZCR), Mel Frequency Cepstral Coefficients (MFCC), Root Mean Square Energy (RMSE), Mel Spectrogram, and Chroma. This study compares the CNN, LSTM, and GRU algorithms. The classification results show that the CNN algorithm gets better results, namely 79.13%. Meanwhile, LSTM and GRU only got an accuracy of 55.76% and 55.14%, respectively
Co-Authors Eni Heni Hermaliani Hendra Budi Kusnawan Jordy Lasmana Putra Laela Kurniawati Muji Ernawati Windu Gata