Judul: Pengenalan pembicara dengan jaringan syaraf tiruan Backpropagation
Abstrak: Masalah pengenalan pembicara terbagi menjadi dua bagian, yaitu identifikasi pembicara (menentukan identitas pembicara) dan verifikasi pembicara (melakukan verifikasi identitas yang diklaim oleh pembicara). Pengenalan pembicara termasuk dalam masalah nonalgorithmic maka dapat digunakan jaringan syaraf tiruan (JST) untuk mengenali pola-pola suara pembicara. Dalam penelitian ini dibangun suatu sistem yang dapat melakukan pengenalan pembicara menggunakan JST backpropagation. Sebelum diproses dalam JST data suara terlebih dahulu diproses dengan proses-proses sinyal digital melalui suatu proses feature extraction ditambah dengan proses feature selection. Proses feature extraction dilakukan dengan analisis cepstral dan feature selection dilakukan dengan principal component analysis. Hasil dari JST selanjutnya diolah oleh model pembuatan keputusan. Model pembuatan keputusan dalam sistem identifikasi akan menentukan identitas pembicara dan dalam sistem verifikasi akan menerima atau menolak klaim yang diajukan oleh pembicara. Sistem pengenalan pembicara yang dibangun mampu mengidentifikasi dengan tingkat generalisasi tertinggi sebesar 92,3077% dan melakukan verifikasi dengan nilai equal error rate sebesar 6,5657%.
Keyword: 

Judul: Pemodelan Jaringan Syaraf Tiruan untuk Pengenalan Kata pada Berbagai Kondisi Kesehatan Pembicara
Abstrak: Researches on voice signals have been carried out using various signal processing methods, one of them known as Mel Frequency Cepstrum Coefficients (MFCC) which is based on Fourier transformation. MFCC as feature extraction combined with Artifical Neural Network (ANN) as classifier has been shown to have a very good accuracy. Based on previous research, this research is conducted with more complicated cases with the various medical conditions of the speaker. Bad medical conditions, such as cold, is a factor that can affect the human voice, making it more complicated to recognize using signal processing methods. From this research, it can be concluded that the use of MFCC method as the feature extraction combined with ANN as the classifier proved to have a good capability to recognize words from speakers with bad medical conditions. The accuracy that has been obtained is 99% using backpropagation neural network with 50 hidden neurons and using the Traingdx training methods.
Keyword: 

Judul: Gambaran darah komodo (Varanus komodoensis) di Taman Margasatwa Ragunan
Abstrak: This study was conducted to get the hematology and blood biochemistry profile of Komodo dragons at Ragunan Zoo. A total of 18 adult dragons were used. Blood was drawn as much as 2 mL whole blood for hematology and 3 mL for blood biochemistry in serum. Hematology examinations were done by automatic hemavet® machine except differential leukocytes were done manually and blood biochemistry examinations were done by biosystem® machine. Examinations results of erythrocyte parameters of total erythrocytes, hematocrit, hemoglobin, MCV, MCH, and MCHC in row are (1,24±0,21) × 106/mm3, (38,0±4,6) %, (13,3±1,6) g/dL, (311,4±49,9) fL, (109,4±18,7) pg, and (35,1±1,2) g/dL. Trombocytes count is (3,1±1.6) × 103/mm3 and erythrocyte sedimentation rate is (3,9 ± 1.7) mm/h. Leukocytes parameters indicate the values of total leukocytes, heterophils, lymphocytes, monocytes, eosinophils, and basophils in row are (6,53±9,47) × 103/mm3, (3,478±4,972) × 103/mm3, (2,959±4,694) × 103/mm3, (0,096±0,187) × 103/mm3, (0,00) /mm3, and (0,00) /mm3. Biochemical parameters indicate the values of total protein, albumin, globulin, AST, ALT, urea, and creatinine in row are (10,19±3,39) g/dL, (2,51±0,39) g/dL, (7,68±3,07) g/dL, (49,39±20,71) IU/L, (45,39±27,88) IU/L, (13,53±5,88) mg/dL, and (0,29±0,11) mg/dL. The result of examinations averagely showed values tend to normal. Some individual results demonstrated abnormalities which are suspected as acute infection in 1 dragon, foliculogenesis in 1 dragon, muscle disease in 2 dragons, and liver disease in 5 dragons.
Keyword: