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: Speaker Identification System Modeling Using MFCC as Feature Extraction and SVM as Pattern Recognition
Abstrak: Everyday people exchange information using voice may also exchange information with the media texts and tools. Voice signal every human has the character and qualities of different or unique. Indonesia has a diverse ethnic, communities and cultures, many problems are for the same word has different pronunciation patterns. Because of this problem could be a problem in the speaker identification system, so it is necessary to develop a system that is relatively more robust to the problem of intra-speaker variability and noise. Speaker identification system is more focused on the analysis of the two subsystems, namely Feature Extractor and Pattern Recogniser. Mel-Frequency Cepstrum Coefficients (MFCC) is one of feature extraction that is often used for processing the human voice for calculating the cepstral coefficients with the consideration of human hearing. Support Vector Machine (SVM) is one of the classification techniques of data with the supervised learning that is able to classify the multi-class so it is suitable for the classification of more than two classes. Data collection was performed using a microphone to record sound. Sound source was obtained from 10 adult speakers with differences in gender, age and ethnicity, which each speakers say 50 times the word "COMPUTER" so that obtained 500 data. Record duration is 2 seconds with a frequency of 16 KHz. Before data is processed, a preprocessing stage consisting of the elimination of silence, normalization and noise addition. Gaussian noise is added from the level of 80 dB to 0 dB. After the MFCC feature extraction is done, the next stage is SVM pattern recognition using QP and SMO algorithms. Kernel function tested for RBF, Linear, and Quadratic for each algorithm. Pattern Recognition using Kernel quadratic function with a ratio of 90 : 10 for the test data that the original sound without noise, SMO algorithm produces accuracy of 97.0% and the accuracy of the system can maintain above 70% up to 40dB noise addition. The number of errors for all 10 speakers using the test data of the original sound without noise is at most the number 9 speakers (Male, 41 years old, Java). The processing time SMO algorithm is better than the QP algorithm. Future studies may be added to increase the accuracy of Noise Cancelling the voice data is contaminated by noise.
Keyword: Speaker Identification, MFCC, SVM

Judul: Duodenal Histopathology of Pediatric Rat (Rattus norvergicus)&#160;Fed with Orange Juice (Citrus sinensis)
Abstrak: This research investigates the impact of orange juice consumption on the histological structure of the duodenum, a critical site for nutrient absorption and digestion. Focusing on pediatric animals, orange juice (with a pH of 3) was administered to post-weaning rats aged 4 weeks to 6 weeks at dosages ranging from 0g/kg BW to 20g/kg BW over two weeks. Duodenal histopathology was assessed, focusing on parameters such as the quantity of crypts of Lieberkuhn, as well as the length, height, width, and depth of villi (measured in µm). Anatomical pathology examination included measurements of the length and width of the duodenum (also in µm), as well as the color mean (OD). The research findings indicate no significant changes in histopathology or anatomy pathology of the duodenum. This suggests that young individuals can adapt to acidic challenges without compromising gastrointestinal health. These findings offer reassurance regarding the short-term consumption of acidic orange juice (with a pH of 3) with minimal risks to intestinal integrity, both during growth and development.
Keyword: acidic changes