Judul: Pengenalan Kata Berbahasa Indonesia dengan Hidden  Markov Model (HMM) menggunakan Algoritme Baum-  Welch
Abstrak: Pengenalan suara atau Speech Recognition adalah proses konversi sebuah sinyal akustik, yang berasal dari mikropon atau telepon, menjadi satu atau sekumpulan kata. Sinyal suara sendiri merupakan gelombang yang tercipta dari tekanan udara yang berasal dari paru-paru yang berjalan melewati lintasan suara menuju mulut dan rongga hidung, sehingga memiliki frekuensi yang berbeda. Agar sistem komputer dapat mengenali sebuah kata, maka dibutuhkan representasi yang tepat terhadap sinyal-sinyal yang masuk berikut perubahan frekuensinya dalam rentang waktu tertentu. Penelitian ini menggunakan Algoritme Baum-Welch dalam melatih HMM sebagai model kata. Adapun tujuan penelitian ini ialah menerapkan HMM menggunakan algoritme Baumwelch untuk mengenali sebuah kata. Kata yang digunakan terbatas pada 40 buah kata berbahasa Indonesia yang dibedakan ke dalam 2 macam jumlah suku kata, yaitu : 2 suku kata dan 3 suku kata. Pembicara pada penelitian ini juga dibatasi pada 2 orang pembicara perempuan dan 2 orang pembicara laki-laki yang telah terlatih sebelumnya.
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Judul: Pengembangan Hidden Markov Models untuk fonem berbahasa Indonesia pada sistem konversi suara ke teks
Abstrak: Speech to text transcription system is a system used to convert a voice signal from a microphone or a telephone into a single or a set of words. Research on speech to text transcription systems has been widely applied. But these systems tend to be developed based on words, hence they are inefficient when developed for a large vocabulary. This study uses Baum Welch algorithm for HMM training, Forward algorithm for HMM testing, and Mel-Frequency cepstral coefficient (MFCC) to extract voice features. Data used in this study consist of 5 words in Indonesian language. Phonemes are generated from the segmentation process, and then trained with Baum Welch algorithm to generate the model. This study produced 10 models. The best accuracy obtained is 82% generated by testing the HMM with 2 States and 5 epochs
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Judul: Growth, Concentration Chlorophyll-a, and Structure of Macroalgae Gracilaria edulis in the Culture Media Containing Copper
Abstrak: The physiological responses (specific growth rate, concentration of chlorophyll-a, morphological and anatomical structure of the vegetative cells) of the marine macroalga Gracilaria edulis at the different concentrations of copper (0.01 as control, 0.04, 0.06 and 0,5 ppm) delivered continuously for 28 days under controlled laboratory conditions were investigated. Result showed that the degree of copper toxicity depends on both factor the concentration and exposure time. Copper concentration and day of exposure decreased significantly the specific growth rate (SGR) of the Gracilaria edulis but only 0,5 ppm Cu decreased the chlorophyll-a content when compared to control. Of the various physiological end points measured, growth proved to be the most sensitive response than chlorophyll-a with reductions in specific growth rate (SGR). The reason for this could be related to a greater sensitivity of the enzymes involved in growth than those related to chlorophyll-a or a greater energy needs to maintain cellular integrity and metabolic processes under sublethal Cu stress. In all cases, the main effect on vegetative cells were structure changes in cortex and subcortex cells, compared to control, macroalgae exposed to copper showed depigmentation and blending in the lateral branches, heavy staining of the cell wall and severe damage of cell membrane, while the mucilage and the cell walls was densely fibrillar, thick and heavily stained, the cortex cell was clearly vacuolated, the presence of endophytic on the cell wall and endophytic spores in the mucilage. The presence vacuoles should be related to a metal immobilization mechanism, minimizing the Cu toxicity for cells. The epiphytic bacteria can act as a biofilter diminishing the availability of Cu for macroalgae accumulation.
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