Judul: Identifikasi Jenis Tangis Bayi menggunakan Codebook untuk Pengenal Pola dan MFCC untuk Ekstraksi Ciri
Abstrak: In this paper, we focused on automation of Dunstan Baby Language. This software uses MFCC as feature extraction and codebook as feature matching. The codebook of clusters is made from the proceeds of all the baby’s cries data, by using the k-means clustering. The scope of this research are: 1) the infant cries classification used is the version of the Dunstan Baby Language, 2) this software is used to identify the meaning of 0-3 month old infant cries. The methodology of this research consists of several stages of process: data collection, preprocessing, codebook modeling of infant cries, testing and analysis, and interface manufacturing. The data is taken from Dunstan Baby Language videos that has been processed. The data is divided into two, training data and testing data. There are 140 training data, each of which represents the 28 hungry infant cries, 28 sleepy infant cries, 28 wanted to burp infant cries, 28 in pain infant cries, and 28 uncomfortable infant cries (could be because his diaper is wet/too hot/cold air or anything else). The testing data is 35, respectively 7 infant cries for each type of infant cry. Silence cutting is in the preprocessing stage and the feature extraction uses MFCC method. The interface making of the infant cries identification is made based on the training data that produces the highest accuracy. The making of this research is using Matlab R2010b version 7.11.0.584 software. The research varying frame length: 25 ms/frame length = 275, 40 ms/frame length = 440, 60 ms/ frame length = 660; overlap frame: 0%, 25%, 40%; the number of codewords: 1 to 18, except for frame length 275 and overlap frame = 0% using 1 to 29 clusters. The identification of this type of infant cries uses the minimum distance of euclidean and mahalanobis distance. Accuracy value using euclidean distance is between 37% and 94%. Whereas, accuracy value using mahalanobis distance is between 9% and 83%. Codebook model and MFCC with the higher accuracy is: frame length = 440, overlap frame = 0.4, k = 18. Eventhough the distance using that produce the higher accuracy is euclidean distance. That model can produce accuracy recognition of infant cries with the higher about 94%. Sound ‘eh’ is the most familiar, whereas sound ‘owh’ is always missunderstood and generally it is known as ‘neh’ and ‘eairh’. The weakness point of this research is the silence is only be cut at the beginning and at the end of speech signal. Hopefully, in the next research, the silence can be cut in each sound segment so that it can produce more specific sound. It has impact on the bigger accuracy as well.
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Judul: Identifikasi Pembicara dengan MFCC sebagai Ekstraksi Ciri dan Codebook untuk Pengenalan Pola
Abstrak: Setiap orang mempunyai karakteristik suara yang berbeda-beda. Penelitian ini mengembangkan pembicara sistem yang menggunakan suara manusia sebagai fiturnya. Model ini menggunakan metode yang bergantung pada teks. Pembicara identifikasi memiliki masalah kompleks yang disebabkan oleh frasa yang salah diucapkan atau salah dibaca, mikrofon yang berbeda penempatan, gangguan latar belakang, ketidaksesuaian saluran, keadaan emosi ekstrem, penyakit, penuaan, mimikri oleh manusia dan tape recorder. Langkah pertama untuk mengembangkan sistem pengenalan pembicara adalah mengumpulkan data suara dari 10 pembicara (5 wanita dan 5 pria). Selanjutnya data suara diolah melalui proses standarisasi, diam penghapusan, penghilangan noise, ekstraksi fitur MFCC, pemodelan dan penggunaan pengenalan pola Buku Kode. Penelitian ini menghasilkan akurasi lebih dari 95% pada data suara asli. Akurasi terbaik diperoleh pada data latih : data uji proporsi 60% : 40% dengan nilai 97,92%. Untuk data dengan tambahan Noise 20dB dan noise 15dB masing-masing menghasilkan nilai akurasi sebesar 36,53% dan 24,72%. Menambahkan Kebisingan proses canceling (NC) sebelum ekstraksi fitur pada pengujian data menghasilkan penurunan sebesar 1,25% akurasi pada data asli, peningkatan akurasi data sebesar 10,28% dengan tambahan noise sebesar 20dB dan peningkatan akurasi data sebesar 16,25% dengan tambahan noise sebesar 15dB. Untuk penelitian lebih lanjut, seseorang dapat mencoba berbagai nilai untuk masukan NC dan MFCC atau mencari nilai lain. metode terkait untuk menangani masalah gangguan.
Keyword: Speaker identification, MFCC, Noise canceling, K-Means clustering, Codebook

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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