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: Nilai Indeks Glikemik Produk Olahan Suweg (Amorphophallus campanulatus Bl.)
Abstrak: The general objective of this study was to analyze the glycemic indices of fried, boiled, and steamed suweg. The specific objectives of this study were: (1) to analyse nutrient composition in processed suweg; (2) to analyse glycemic indices of processed suweg; (3) to analyse any statistical differences of glycemic indices of fried, boiled, and steamed suweg. This research consisted of three stages covered: (1) processing of suweg into three treatments (fried, boiled, and steamed), (2) analyzing of nutrient contents in suweg products, (3) measuring of glycemic index of three processed. Treatment affects the moisture(wet based/ wb) content of suweg which were on steamed, boiled, and fried suweg 73.61%, 80.25%, and 55.92% respectively. Protein content (dry based/ db) were 5.38%, 5.67%, and 3.92% respectively. Fat content (db) were 0.27% for steamed, 0.39% for boiled, and 11.41% for fried suweg. Total carbohydrate by difference content (db) for steamed, boiled, and fried suweg were 87.47%, 87.87%, and 79.03% respectively. Total dietary fiber (db) content were 29.02% for steamed, 34.99% for boiled, and 25.58% for fried suweg. Insoluble dietary fiber (db) content for steamed, boiled and fried suweg were 19.67%, 27.06% dan 19.47%, while soluble dietary fiber (db) were 7.93%, 11.72% dan 6.12% respectively. Glycemic index values of steamed, boiled, and fried suweg were 37.36, 45.58, and 24.68 respectively. Those three products were classified as low glycemic index category. Statistical analysis showed that treatment effected the glycemic index value
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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