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: 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: Kajian Penggunaan Zeolit dan Kalsium Oksida sebagai Sumber Pemanas Kemasan Kaleng Panas Sendiri (self-heating can).
Abstrak: Penelitian ini mengkaji penggunaan zeolit sebagai alternatif sumber pemanas pada kemasan kaleng panas sendiri yang umumnya menggunakan kalsium oksida. Pada penelitian ini telah dilakukan proses kalsinasi pada suhu 550 oC selama 24 jam terhadap zeolit dan kalsium oksida untuk pembakaran awal dengan tujuan menghilangkan zat-zat yang tidak diinginkan. Kemudian telah dilakukan pengukuran suhu reaksi campuran sejumlah sampel dengan sejumlah air pada masing-masing perbandingan. Hasil penelitian menunjukkan reaksi air dengan kalsium oksida menghasilkan suhu tertinggi pada perbandingan 3:1 dengan ratarata perubahan suhu 46.37 oC. Sedangkan reaksi air dengan zeolit menghasilkan suhu tertinggi pada perbandingan 1:2 dengan rata-rata perubahan suhu 39.27 oC. Massa kalsium oksida yang digunakan 5.00 gram dan zeolit yang digunakan 28.00 gram. Kemudian telah dilakukan pengukuran perubahan volume sampel yang direaksikan dengan air, hasilnya kalsium oksida dan air memiliki kenaikan volume 1.00 ml sedangkan zeolit memiliki kenaikan volume 2.00 ml. Dari hasil penelitian ini dapat disimpulkan bahwa zeolit ternyata belum mampu untuk menggantikan kalsium oksida sebagai sumber pemanas self-heating can dari aspek kapasitas panas yang dihasilkan dan nilai ekonomis penggunaan bahan.
Keyword: kaleng panas sendiri, kalsium oksida, kemasan aktif, sumber pemanas, zeolit