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: Perbandingan metode voting feature intervals dengan jaringan saraf tiruan dalam mengklasifikasi genre musik
Abstrak: Music genre is one of the important descriptions that have been used to classify digital music. The aim of this research is to compare Voting Feature Intervals (VFI) methode with the Neural Network (NN) methode in classifying music genre. There are 12 scenarios of feature extractions in this research. Three variations of MFCC coefficient number (7, 13 and 20 coefficients) and four variations of music length (1, 5, 10, and 30 seconds). From each of the feature vector, mean was calculated. For the NN methode after the feature vectors were extracted, normalization was applied using the cumulative normal distribution methode. This research shown that the optimal number of MFCC coefficients was 13 coefficients. NN predictions were better than VFI predictions. NN has an accuracy up to 95% which was obtained by using 30 neurons of hidden layer, 10 seconds length of music and 13 MFCC coefficients. While the VFI has an accuracy up to 85% which was obtained by using 30 seconds length of music and 7 MFCC coefficients. Both experiments that used 13 and 20 coefficients of MFCC feature obtained same accuracy using the NN method. Classic genre has an accuracy of 100% in VFI. The reliability of the system was 57,14% for disco up to 94,44% for classic.
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Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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