Judul: Produksi Etanol Oleh Saccharomyces Cerevisiae Var. Ellipsoideus Dari Sirup Dekstrin Pati Sagu (Metroxylon Sp.) Menggunakan Metode Aerasi Penuh Dan Aerasi Dihentikan
Abstrak: Indonesia is known as the largest owner land of sago, with the area around 1 million ha, or 50% of 2 million ha of world sago area (Marsudi and Aprilia, 2006). The great potential of sago in Indonesia has not been used optimally. Thus far, only about 10% of the total national sago area that has been used to meet food and industry requirements. When sago is cultivated properly, it’s dried starch productivity would reach 25 ton/ha/year, much higher as compared to cassava 1,5 tons/ha/year, potatoes 2,5 tons/ha/year, and corn 5,5 tons/ha/year. (Sumaryono, 2007) This research is aimed to find the possibility of using dextrin as substrate for ethanol production, to see potentiality of Saccharomyces cerevisiae var. ellipsoideus as the ethanol producer, also the selection of aeration rate and total sugar concentration for fermentation. More over, this research also aims to determine the influence of bioprocess engineering (full and stopped aeration) to biomass and ethanol production. In the first study conducted, fermentation was treated at different substrate concentrations (18%, 24%, 30%, 36% w/v) and two regimes of aeration (1 vvm and 2 vvm). It is found that the best treatment was obtained from fermentation at 30% substrate concentration and 1 vvm aeration rate, which produced the highest amount of biomass (2,98 g/l) with the value of μmaks was 0,29 hour-1. For the first six hour cultivation, biomass growth was still in log phase. The residual sugar content in substrate and the pH value decreased during the fermentation process. The decline of residual sugar contents in substrate showed the activity of cells that consumed dextrin as substrate. While changes in pH was due to the release of H+ during the consumption of NH4 +, also the use of amino acids as nitrogen source and the accumulation of by products such as organic acids from carbohydrate metabolism. Bioprocess engineering was done on the treatment selected, with the stop of aeration after its first 6 hours cultivation. In 24 hours of fermentation, ethanol produced was 24,94±0,16 g/l. This results was much higher than the treatment with full aeration which only produced ethanol as much as 21,25±0,55 g/l. The pH at the end of fermentation in full aeration reached 3,05, while in stop aeration was 3,15. The extreme low pH can prevent the growth of microorganisms. The stop of aeration after the first 6 hour aeration change the environment of the fermentation conditions from aerob to be anaerob, so that the fermentation process for the formation of ethanol can be maximized.
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Judul: Pembuatan Bioetanol Dari Dekstrin Dan Sirup Glukosa Sagu (Metroxylon Sp.) Menggunakan Saccharomyces Cerevisiae Var. Ellipsoideus
Abstrak: There are some factors of fossil fuel problems in Indonesia namely the decreasing of fossil fuel production, the increasing of domestic energy consumption, the high price of fossil fuel and the harmless of fossil fuels itself to the environment. The research of energy alternative source should be carried out as an effort to reduce the energy supply based on fossil fuels. Bioenergy is the main alternative to overcome the energy crisis based on fossil fuel. Bioenergy is one of alternatives energy based on biomass derivate; material from living organism (plants, animals and microorganism). The development of the bioenergy is absolutely appropriate to be implemented in Indonesia because it is supported by the abundant of natural resources potency and also the availability of the field. One of bioenergy which can be developed is bioethanol which can replace the use of gasoline. The main purpose of this research was to produce bioethanol from sago starch by using Saccharomyces cerevisiae var. ellipsoideus in batch system. The particular purposes of this research were to determine the kind of substrate and substrate sugar concentration based on yield of ethanol produced and value of biomass, and also its influence to the other fermentation parameters (total residual glucose and pH). Besides that, this research was also aimed to know the influence of process manipulation (full agitation dan stop agitation) towards ethanol yield, biomass quantity, total residual glucose and pH. Saccharomyces cerevisiae var. ellipsoideus can utilize dextrin and glucose syrup to produce biomass and ethanol. Fermentation of glucose syrup 24 % using Saccharomyces cerevisiae var. ellipsoideus for 24 hours produce the highest amount of biomass 2,84 g/l and the highest ethanol yield 28,3 g/l. At the 6th hour, the yeast is still in log phase, but at 12th hour it had been in stationary phase. The total residual glucose and pH were decreasing during fermentation process. Bioprocess manipulation was carried out by using glocose syrup 24 % as substrate and stopped the agitation at 6th hour. Ethanol and biomass quantity produced by full agitation was higher than that produced by stop agitation at 6th hour. The full agitation could produce ethanol up to 26,96+1,80 g/l and stop agitation at 6th hour could produce ethanol up to 21,21+2,83 g/l. At the 24th hour, the average of pH values on full agitation was 3,22+0,02, meanwhile the mean of pH values on stop agitation at 6th hour was reached at 3,23+0,00. The lower of pH values could inhibit the microorganism growth. The values of Y x/s, Y p/s dan Y p/x on full agitation were 0,04+0,00, 0,41+0,00 and 10,41+0,34, meanwhile on stop agitation at 6th hour were 0,04+0,00, 0,34+0,07 and 9,48+1,45. The agitation stopped at the 6th hour caused inhomogeneous condition so the process of fermentation was not well done.
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Judul: Quantum Convolutional Neural Network (QCNN) for Classification Brain Tumor MRI Image
Abstrak: Penelitian ini bertujuan untuk mengembangkan model deteksi tumor otak menggunakan citra MRI dengan metode Quantum Convolutional Neural Network. Data yang digunakan adalah citra MRI otak yang terdiri dari citra tumor otak glioma, meningioma, hipofisis, dan negatif tumor yang dibagi kedalam data latih dan uji dengan rasio 80:20. Selanjutnya data latih dilakukan penyeimbangan dengan teknik oversampling. Dilakukan pengembangan model untuk mendeteksi tumor otak (biner), dan juga model untuk mengklasifikasikan jenis tumor otak (multiclass). Parameter yang digunakan dalam pengembangan model adalah jumlah ukuran filer = 2 dan kedalaman = 4, dengan qubit 4, dan dilatih dengan jumlah epoch sebanyak 10. Data akan diproses dengan metode Quantum Convolutional Neural Network yang terdiri dari lapisan quantum convolution, lapisan flatten, dan lapisan dense. Model multiclass mendapatkan akurasi 52%, setelah data latih diseimbangkan akurasi model meningkat menjadi 62%, sedangkan model biner mendapatkan akurasi 88%, setelah data latih diseimbangkan akurasi model meningkat menjadi 89%.
Keyword: Citra MRI, Klasifikasi Citra, QCNN, Tumor Otak