Klasifikasi Suara Kucing Menggunakan Metode Convolutional Neural Network (Cnn) Dan Long Short-Term Memory (Lstm)

Fadhilah, Gusti Safinatunnajah (2022) Klasifikasi Suara Kucing Menggunakan Metode Convolutional Neural Network (Cnn) Dan Long Short-Term Memory (Lstm). Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

Cats become pets who are very close to humans, and they convey messages by producing identical sounds. Therefore, analysis of pet voices is important for a better relationship between cats. Animal communication through sound, especially in cats, depends on the situation or context in which the sound is made such as in a state of danger. Based on these problems, a classification method is needed to classify the similarity of characteristics in the resulting sound pattern. The classification methods used are Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) which can remember information for a long time and are used for a long time period. This study aimed to determine feelings or moods based on the sound produced into 4 categories: The Purr, The Meow, The Mating Call, and The Growl. The result of this study is that the best architectural model is to use 4 CNN convolution layers measuring 8-8-8-8 and 2 LSTM layers measuring 8-8 with an accuracy of 56.25%. Keywords : CNN, Cat, LSTM, Voice

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