Judul: Noise Reduction on Voice Commands Classification Model using Complete EEMD with Adaptive Noise (CEEMDAN)
Abstrak: The application of voice recognition systems is currently widely used in various fields, one of which is voice commands classification. Voice classification is basically done by using feature extraction to recognize the characteristics of each signal. The MFCC method is used in this study. MFCC is a feature extraction method that calculates the cepstral coefficient by imitating the human hearing systems. In this study, the LSTM model with a bidirectional scheme (Bi-LSTM) is used to build a voice command classification model. This method is considered to be able to accommodate the characteristics of voice commands which have a short duration and sensitive to noise. In order for the classification model to maintain high accuracy, CEEMDAN is used as a noise reduction method. CEEMDAN is considered capable of decomposing noisy signals and separating the original signal from its noise component. The results showed that the Bi-LSTM model had an accuracy of 96% and an F1-score of 95%, but experienced a significant decrease on performance when given by 10 Signal to Noise Ratio (SNR) to 0 SNR noise level. In this study, CEEMDAN has successfully improve the performance of the model on noised data. CEEMDAN is able to increase the accuracy and F1-score by an average of 29.57% and 33.43%, which makes the classification model more robust in classifying voice commands at certain noise levels. The results of the comparison with other noise handling methods show that CEEMDAN performs better in noise reduction.
Keyword: Bi-LSTM, CEEMDAN, MFCC, Penanganan Noise, Perintah Suara

Judul: Application of Mel Frequency Cepstrum Coefficients (MFCC) as Feature Extraction on Phoneme Recognition with Probabilistic Neural Network (PNN) as Classifier
Abstrak: Voice recognition (speech recognition) is one field of study in voice processing. This technology can convert voice signals into a form of written information (text). With this technology, people can interact with a computer. MFCC feature extraction computes the cepstral coefficients by considering the human hearing. This research is a phoneme recognition using feature extraction MFCC and PNN as feature matching model. This study compares the coefficient, overlap, and the test data without noise and with noise. MFCC is used with 13, 20, 26 coefficients and 0%, 25%, 50% overlap. Noise is added by 30 dB, 20 dB and 10 dB. From the comparison of the three overlaps, produced the best accuracy at 50% overlap with an accuracy of 94,71%. From comparing the three coefficients, better accuracy resulting in coefficients of 20 and 26 with an accuracy of 97,12% at 50% overlap. After comparing between the coefficients of 20 and 26 with overlap of 25% then the coefficient 26 is obtained that better accuracy of 94,23%. This shows that in this study the coefficient 26 is the best. In this research, there are three noise variables. The variables are 10 dB, 20 dB, and 30 dB. The best accuracy reached when the noise variable is 30 dB rather than 10 dB or 20 dB because the accuracy has the closest accuracy compared by the accuracy when noise was not added into the data. When the noise variable is 30 dB, the percentage of accuracy is 85,3%.
Keyword: 

Judul: Tingkat Kelahiran Ikan Plati Sunset Xiphophorus maculatus (Günther, 1866) pada Beberapa Tingkat Suhu Air
Abstrak: Ikan plati merupakan ikan hias air tawar yang bereproduksi dengan cara melahirkan (viviparous) dalam beberapa waktu. Memproduksi ikan plati dalam skala besar ternyata dihadapkan pada kendala yang besar, yaitu belum ada teknologi kelahiran anak serentak dalam satu waktu. Hal ini berdampak pada umur anak ikan tidak seragam dan pola tanam produksi terganggu, sehingga berujung pada ketidakefisienan dalam produksi. Pada penelitian ini dilakukan upaya penyerentakkan proses kelahiran oleh induk plati melalui rangsangan eksternal berupa peningkatan suhu secara mendadak. Penelitian ini menggunakan rancangan faktorial dengan empat perlakuan sebanyak tiga ulangan. Perlakuan kenaikan suhu yang diberikan adalah 30-32°C selama 12 dan 24 jam. Hasil yang diperoleh perlakuan kenaikan suhu ke 30°C selama 12 jam perlakuan memberikan tingkat induk melahirkan tertinggi.
Keyword: Ikan hias, Kelahiran, Poecillidae, Suhu, Xiphophorus maculatus