Judul: Nilai Indeks Glikemik Produk Olahan Gembili (Dioscorea esculenta).
Abstrak: Gembili (Dioscorea esculenta) is a local food source of carbohydrates that can be grown in tropical regions such as Indonesia. Gembili has a potential to be used as a functional food because it contains inulin which may act as a prebiotic. The objective of this research was to analyze the glycemic index values of gembili products. The research consisted of three stages covered : (1) processing of gembili into three treatments (boiled, steamed, and fried), (2) analyzing of nutrient contents in gembili products, (3) measuring of glycemic index of three processed food. Moisture content (wet based/wb) in boiled, steamed and fried gembili were 68.09%, 62.11%, and 49.09% respectively. Ash content (dry based/db) in boiled, steamed and fried gembili were 1.62%, 2.15%, and 2.13% respectively. Fat content (dry based/db) were 0.63%, 0.37% and 7.75%, while protein content (dry based/db) were 3.71%, 2.99%, and 4.25%. Insoluble dietary fiber content (dry based/db) between 11.79% to 13.43%, while was higher than soluble dietary fiber content (dry based/db) between 5.84% to 10.88%. Total dietary fiber content (dry based/db) in boiled, steamed and fried gembili were 19.01%, 18.15%, and 24.30% respectively, while carbohydrate by difference content (dry based/db) were 91.05%, 93.33%, and 88.88% respectively. The results of this study indicated that all glycemic index values of gembili products were high (>70). Glycemic index values gembili boiled, steamed, and fried gembili were 85.56, 87.56, and 83.61 respectively. Analysis of variance test One Way ANOVA showed that those treatments did not significantly affect the glycemic index value (p> 0.05)
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Judul: Formulasi dan Nilai Indeks Glikemik Cookies Ganyong (Canna edulis Kerr.)
Abstrak: Ganyong (Canna edulis Kerr.) is one of the local food products which is potential to be developed. Ganyong has high fiber content and its amylose content is higher than amylopectin, so it is estimated that to have low glycemic index value. However, until now there has been no study on the glycemic index value of ganyong. The aim of this study is to determine the glycemic index value of ganyong (Canna edulis Kerr.) cookies. There were 3 products that were analyzed for its glycemic index, in example standard cookies, ganyong cookies and boiled ganyong. Physical properties analyzed of control cookies and ganyong cookies were breaking strength and color degree. Chemical properties analyzed of standard cookies, ganyong cookies, and boiled ganyong were proximate analysis, amylose content, and total dietary fiber content. The study showed that ash content and amylose content of ganyong cookies were significantly higher (p<0.05) than control cookies. No significant found among other parameters. The glycemic index values of control cookies and ganyong cookies were 41 and 35 respectively, and were in the category of food with low glycemic index value. Boiled ganyong (GI=65) was in the category of food with medium glycemic index value. This study suggested that the use of ganyong in the making of cookies can decrease glycemic index of cookies.
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Judul: Estimating Parameter and the Convergence of Parameter Estimator of Poisson Hidden Markov Model
Abstrak: Model hidden Markov terdiri dari sepasang proses stokastik yaitu proses observasi dan proses yang memengaruhi observasi. Proses stokastik yang memengaruhi observasi ini diasumsikan membentuk rantai Markov dan tidak diamati. Model Poisson hidden Markov (MPHM) adalah salah satu model hidden Markov diskret dan proses observasinya jika diketahui proses yang memengaruhinya diasumsikan menyebar Poisson., A hidden Markov model (HMM) consists of a pair of stochastic processes, that is the observation process and process that affected observation. The stochastic process which causes this observation is assumed to form a Markov chain and is not observed. Poisson hidden Markov models (PHMM) is one of the discrete HMM and the observation process given the cause is assumed having a Poisson distribution.
Keyword: Poisson hidden Markov model, Forward-Backward algorithm, Expectation Maximization algorithm