@article{Safinatunnajah_Prasetiadi_Wibowo_2022, place={Purwokerto}, title={CLASSIFICATION OF CAT SOUNDS USING CONVOLUTIONAL NEURAL NETWORK (CNN) AND LONG SHORT-TERM MEMORY (LSTM) METHODS}, volume={3}, url={https://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/373}, DOI={10.20884/1.jutif.2022.3.5.373}, abstractNote={<p>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 and human. 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 Howl. 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. The precision value in this architecture is 0.68, the recall value is 1.00, the accurary value is 0.5625 and the f1-score value is 0.77. The small value of the confusion matrix is caused by the lack of dataset duration in the training process, resulting in underfitting.</p>}, number={5}, journal={Jurnal Teknik Informatika (Jutif)}, author={Safinatunnajah, Fadhilah Gusti and Prasetiadi, Agi and Wibowo, Merlinda}, year={2022}, month={Oct.}, pages={1349–1353} }