Judul: Pemodelan Jaringan Syaraf Tiruan untuk Pengenalan Kata pada Berbagai Kondisi Kesehatan Pembicara
Abstrak: Researches on voice signals have been carried out using various signal processing methods, one of them known as Mel Frequency Cepstrum Coefficients (MFCC) which is based on Fourier transformation. MFCC as feature extraction combined with Artifical Neural Network (ANN) as classifier has been shown to have a very good accuracy. Based on previous research, this research is conducted with more complicated cases with the various medical conditions of the speaker. Bad medical conditions, such as cold, is a factor that can affect the human voice, making it more complicated to recognize using signal processing methods. From this research, it can be concluded that the use of MFCC method as the feature extraction combined with ANN as the classifier proved to have a good capability to recognize words from speakers with bad medical conditions. The accuracy that has been obtained is 99% using backpropagation neural network with 50 hidden neurons and using the Traingdx training methods.
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Judul: Perbandingan pemodelan Wavelet dan MFCC sebagai ekstraksi ciri pada pengenalan fonem dengan teknik jaringan syaraf tiruan sebagai classifier
Abstrak: Researches on voice signals have been carried out using various signal processing methods, such as Linear Prediction Coding (LPC), Mel Frequency cepstrum coefficients (MFCC), and Neural Predictive Coding (NPC) which the whole method is based on Fourier transformation. Therefore, comparisons will be made with other approaches based on Wavelet transformation. This research would be the comparison of two feature extraction types, that are Wavelet daubechies and MFCC. Wavelet Transformation has become increasingly popular in signal processing as image and speech. Wavelet transformation has demonstrated good time-frequency localization properties and are appropiate tools for the analysis of non-stationary signals like speech. MFCC feature extraction that computes the cepstral coefficients by considering the human hearing. ANN multilayer perceptron, known as backpropagation used as classifier. From the research that has been done, it can be concluded that the use of Daubechies Wavelet methods as feature extraction on phoneme recognition is not better than MFCC. The accuracy of Daubechies Wavelet method with a 220 hidden neurons ANN learning has achived 36% rate as the best test results. While on phoneme recognition with MFCC method achieved 100% accuracy rate with a 240 hidden neuron learning ANN.
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Judul: Epoksi dasi Olein Sawit sebagai Base Oil untuk Bio Grease Kalsium Kompleks
Abstrak: Pengembangan biogrease minyak sawit bertujuan mensubstitusi grease berbahan minyak bumi dengan bahan yang lebih ramah lingkungan. Peningkatan karakteristik biogrease dapat dilakukan dengan melakukan sintesis kimia. Penelitian ini bertujuan mendapatkan perbandingan mololein sawit dengan H2O2 dalam proses epoksidasi,dan analisis fisik produk biogrease yang dihasilkan. Proses epoksidasi menggunakan asam asetat dan H2O2 dengan variasi perbandingan mololein dan H2O2 adalah1:3,1:6,dan1:9. Perbandingan mol dipilih berdasarkan uji bilangan iod dan oksiran, kemudian diproses menjadi bio grease dengan penambahan sabun pengental. Oleinterepoksidasi dengan perbandingan mololein dan H2O2 yaitu1:9 dipilih karena memiliki rata-rata bilangan oksiran tertinggi 0.99 dan bilangan iod terendah 33.09mgI2/gserta berdasarkan uji ANOVA danLSD. Bilangan oksiran menunjukkan banyaknya senyawa epoksida yang dihasilkan. Bilangan iod menunjukkan ketidak jenuhan. Asam peroksi yang digunakan dalam proses epoksi dasi bereaksi dengan senyawa tidak jenuh sehingga semakin rendah bilangan iod pada olein teepoksidasi, semakin banyak senyawa epoksi dayangd ihasilkan. Biogrease dengan baseoil olein terepoksidasi dengan perbandingan mololein dan H2O2 1:9 memiliki warna kremmuda,densitas0.96g/cm3, viskositas31280mPa.s, unworkedpenetration 438 (0.1mm), worked penetration 443 (0.1mm), dropping point <26oC,daya tahan korosi 2c dan NLGI00.
Keyword: biogrease, epoksidasi, RBDPO, olein terepoksidasi