Judul: Penerapan Learning Vector Quantization (LVQ) dan Ekstraksi Ciri Menggunakan Mel-Frequency Cepstrum Coeffecients (MFCC) untuk Transkripsi Suara ke Teks
Abstrak: Speech recognition by a computer is not an easy thing to do. Speech to text transcription is a technique that allows a computer to accept input in the form of spoken words and convert it into text. The purpose of this study is to model the neural network namely Learning Vector Quantization (LVQ) for speech to text transcription and determine the accuracy of speech recognition using MFCC feature extraction. The experiments are conducted by recognizing each syllable of the test data. The results show that the highest accuracy is 98.57% when the epoch value is 90, learning rate is 0.007, and learning rate decrement factor is 0.977. This accuracy is obtained by using the following MFCC parameters: sampling rate 11000 Hz, time frame 23.27 ms, overlap 0.39, and cepstral coefficients 13.
Keyword: voice, transcription, Mel-Frequency Cepstrum Coefficients (MFCC), Learning Vector Quantization (LVQ)

Judul: Klasifikasi genre musik menggunakan Learning Vector Quantization (LVQ)
Abstrak: Radio stations and music television have a milion of music tapes. A lot of musical genres create a problem when people wants to determine the right genre of a new kind of music. To classify the musical genre is not an easy task, because the musical genre is really difficult to standardization. Automatic musical genre classification can assist the human role in that process and help people to searching for the song acording to the genre that people want. This research using Mel Frequency Coefficient Cepstrum (MFCC) to obtain feature extraction. Learning Vector Quantization (LVQ), one kind of artificial neural network used for classification method. The number of genres that are used were four kind of musical genre, that is rock, classic, keroncong, and jazz with four different duration that is 5 second, 10 second, 20 second and 25 second. This research using k- fold cross validation to distribute dataset for training and testing set with the number of folds as much as 2 until 10 fold. This research succesfully implemented MFCC feature exraction and classification using LVQ. Based on this research, the accuracy of the classification using Learning Vector Quantization reaches 93,75% for the four type musical genre. The highest accuracy value was obtain from the experiments with a duration of 10 second and the number of fold 4. Training time for each duration is 30 minute for 5 second music duration, 45 minute for 10 second music duration 120 minute for 20 second music duration and 150 minute for 25 second music duration.
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Judul: Penanganan koksidiosis hati (Eimeria stiedae) pada kelinci
Abstrak: Koksidiosis merupakan penyakit parasiter yang menyerang hewan yang disebabkan oleh genus Eimeria. Pada kelinci terdapat dua bentuk koksidiosis, yakni koksidiosis intestinal dan koksidiosis hati. Eimeria stiedae adalah protozoa penyebab koksidiosis hati. Menyerang ganas pada kelinci muda (terutama umur 5-8 minggu) sedang hewan dewasa yang terserang cenderung menjadi karier dan mengekalkan penyakit. Hampir di seluruh dunia dilaporkan. Sekalipun kejadiannya jarang, namun menyebabkan kematian dengan mortalitas 50% sampai 100%. Kasus banyak bersifat subklinis. Pada kasus klinis gelaja yang tampak adalah: pembesaran abdomen, nafsu makan menurun, berat badan menurun, ikterus dan mencret. Penularan penyakit melalui makanan dan minuman atau bahan lain yang tercemar sporokista yang terbawa tinja penderita. Ookista ini berbentuk oval, berdinding licin dan double contour, berwarna merah kekuningan, mikrofil jelas, terang dan datar, ukuran (28-40 X 16-25) mikron. Siklus hidup mengalami tiga daur. Daur skizogoni, menghasilkan skizon dan merozoit. Daur gametogoni, menghasilkan gametosit, dan melalui fertilisasi terbentuk zigot yang berubah menjadi ookista dan keluar berturut- turut dari buluh empedu, usus dan tinja. ...
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