Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 144-152
Abstract
This independent research aimed to develop a model for predicting the ripeness level of durians based on knocking sounds. The ripeness levels were categorized into 3 levels are raw, unripe and ripe. Each level have unique sound responsed when knocking. To achieve the highest accuracy in model develop-ment, the researcher have compared the data feature extraction with 3 methods: Mel-Spectrogram, Short-time Fourier Transform (STFT) and Mel-Frequency Cepstral Coefficients (MFCC). In the model development phase, The Convolu-tional Neural Network (CNN) algorithm was selected bulding the prediction model. To evaluate the performance of the developed model, accuracy and F1 Score was measured by comparing along to data feature extraction 3 methods. It was found that the Short-time Fourier Transform (STFT) method yielded the highest both accuracy value and F1 Score. The resulted by training dataset give 99% of accuracy value and 94% of F1 Score. Along with the blind testing data give 99% of accuracy value and 92% of F1 Score.