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.
Keyword: 

Judul: Pengklasifikasian Genre Musik Berdasarkan Sinyal Audio Menggunakan Support Vector Machine
Abstrak: Musical genre is a label for musical art to characterize and categorize music. The determination of musical genre is done based on the similarity between the music. Most of the classification of musical genre is done manually, which requires a lot of effort and time especially when there is a big database of music. The purpose of this research is to build a support vector machine model to automatically classify the musical genre and implement the method to extract the musical surface and rhythm features. The developed model is then utilized to determine the genre from the unknown music. Support vector machine is a learning system whose classification uses a hypothesis space in the form of linear functions in a high dimension of feature space. The extracted musical surface and rhythm features are the centroid, roll off, flux, zero crossings, low energy, and four peaks of the signal‟s autocorrelation calculation. The result of this research is a classification model with an average accuracy of 65%.
Keyword: support vector machine, musical genre, audio signal

Judul: Penapisan awal senyawa aktif antioksidan dari spons asal Pulau Panggang, Kepulauan Seribu, Jakarta
Abstrak: Spons memproduksi metabolit sekunder untuk mempertahankan dirinya dari serangan predator dalam bentuk senyawa bioaktif. Senyawa-senyawa ini telah banyak diketahui mempunyai peranan yang besar dalam bidang kesehatan. Salah satunya adalah sebagai antioksidan.
Keyword: