Judul: Penerapan mel-frequency cepstrum coefficients (mfcc) sebagai ekstraksi ciri pada transkripsi suara ke teks dengan self organizing maps (som)
Abstrak: Transkripsi suara ke teks adalah suatu teknik yang memungkinkan sebuah komputer untuk menerima input berupa kata yang diucapkan dan ditranskripsikan ke dalam sebuah teks. Tujuan penelitian ini adalah mengembangkan suatu sistem transkripsi suara ke teks dengan ekstraksi ciri mfcc dan pemodelan jaringan syaraf tiruan self organizing maps (som). Data yang digunakan adalah data suara yang telah direkam dari satu pembicara yang mengucapkan 15 kata untuk data latih dan 5 kata untuk data uji. Masing-masing kata diulang hingga mencapai 240 data latih dan 50 data uji. Kemudian ciri data suara diekstraksi dengan sampling rate 11000 hz, time frame 23.27 ms, overlap 0.39 ms, dan koefisien cepstral 13 untuk mendapatkan karakteristik dari sinyal suara dalam setiap frame. Percobaan dilakukan dengan mengenali tiap suku kata yang ada pada data uji. Hasil menunjukkan bahwa akurasi tertinggi yang diperoleh sebesar 95% pada kombinasi parameter epoch 10, 30, 50, 70, 90 dan 110, learning rate sebesar 0.5, penurunan learning rate 0.999, dan radius 0
Keyword: ekstraksi fitur, mel-frequency cepstrum coefficients (mfcc), self organizing map, pembicara tunggal, transkripsi

Judul: Penerapan Model Codebook untuk Transkripsi Suara ke Teks dengan Ekstraksi Ciri Mel-Frequency Cepstrum Coefficients (MFCC)
Abstrak: The advancement of information technology has triggered various demands in utilizing computer. One of them is to make computers able to communicate naturally with humans. This study uses MFCC as a feature extraction method and codebook as a pattern recognition method for voice-to-text transcription. The clustering technique used in this study is k-means. Data obtained from MFCC are clustered using the k-means method, and the model for classification is constructed using the codebook method. The utilized MFCC parameters are sampling frequency 11 000 Hz, time frame 23.27 ms, overlapping 39%. 300 voice data in WAV files with 5 seconds of duration each, are used as the training data and test data to determine the number of cepstral coefficients and the number of cluster that can produce the highest accurancy. The experiment is conducted by recognizing each syllable in 60 the test data with 240 training data. Simulation result shows that the maximum accurancy obtained is 98.3% at 26 cepstral coefficients and 12 clusters.
Keyword: voice transcription, MFCC, k-means, codebook

Judul: Manajemen Pemeliharaan Kuda (Equus caballus) Untuk Upacara Kenegaraan Dan Sarana Kesenjataan Di Detasemen Kavaleri Berkuda (DenKavkud) TNI-AD Parongpong
Abstrak: Cavalry Horse Detachement (Denkavkud) Indonesian National Army-Cavalary Parongpong is one of the Indonesian military units that utilize the horse and train it as a means of purpose of formal state ceremony and armed forces. Skills possessed by the horse cavalry was the result of care management and specialized training provided by the trainer. Aspects of care management are breeding, feeding, and management. Horses that are kept in the Denkavkud, Parongpong are stallions, mares, foals, and military horses. Total populations of horses at Denkavkud are 183 horses. Besides commited horse training (remonte), Denkavkud also did breeding program to produce horses that can be trained and used as a military horse. There are some issues contained in Denkavkud, such as some of the nutrients needed by the horses not being met from feed given. One of these is crude protein and lysine. Lactation mares was crude protein and lysine deficiency. The number of death in 2010 is higher than previous years. Based on the number of horses that died from January until June 2010 is 11 horses. The average mortality was caused by colic. Colic is caused by excessive eating, excessive drinking during hot, moldy food, and even by investment roundworms. Animal welfare in Denkavkud can be enhanced by improvement of feed given and improving the health of livestock through improved sanitation.
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