Judul: Ekstraksi Pektin dari Kulit Pisang
Abstrak: Dua cara ekstraksi pektin dari kulit pisang (Musa paridisisca L) telah diteliti dengan tujuan untuk meningkatkan daya guna limbah kulit pisang sehingga dapat meneangani limbah kulit pisang.
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Judul: Karakterisasi dan Uji Bioaktivitas Pektin dari Kulit Pisang Kepok (Musa Balbisiana) Hasil Ekstraksi dengan Berbagai Pelarut Asam.
Abstrak: Pisang (Musaceaea ) merupakan salah satu buah dengan produksi tertinggi di Indonesia. Produksi pisang di Indonesia pada tahun 2017 sebesar 7,162,685 ton. Limbah kulit pisang yang dihasilkan dapat mencapai 40% dari total produksi buah pisang segar. Kulit pisang kepok masih mengandung banyak nutrisi dan pektin. Kadar pektin pada kulit pisang berkisar antara 1.92% hingga 3.25% dari berat kering. Pektin adalah polisakarida kompleks yang tersusun atas unit-unit asam D-galakturonat yang terikat oleh ikatan α-1,4-glikosidik sehingga membentuk poligalaturonat. Penelitian ini bertujuan melakukan karakterisasi pektin dari kulit pisang kepok (Musa balbisiana) yang diekstrak menggunakan asam nitrat, asam sitrat, dan HCl serta penapisan potensi bioaktivitas pektin hasil ekstraksi. Rendemen pektin tertinggi diperoleh pada ekstraksi menggunakan buffer sitrat pH 4 yaitu 3.68 ± 1.23 %. Secara umum karakteristik pektin kulit pisang kepok terbaik yaitu pektin yang diekstrak dengan HNO3 pH 4 dengan derajat metoksil 3.74 ± 0.34 %, kadar galakturonat 87.64 ± 8.36%, dan derajat esterifikasi 24.24 ± 0.098%. Berdasarkan nilai LC50 nya pektin hasil ekstraksi dapat digolongkan menjadi dua yaitu pektin komersil dan pektin HCl pH 1.5 yang mempunyai bioaktivitas rendah, serta pektin HNO3 pH 4 dan pektin buffer sitrat pH 4 yang mempunyai bioaktivitas tinggi.
Keyword: Galakturonat, metoksil, pektin pisang kepok, sitotoksisitas

Judul: The Effects of Variance of Interest rate to Variance of Annuity Future Values
Abstrak: Time value of money is a concept that refers to the difference of the value of money due to time difference. The value of money for present time would not be the same as the value of money in the future. The value of money for present time is called as Present Value (PV) of the money and the value of money for future time is called Future Value (FV) of the money. Determination the value of money, for present and future time, depends on the time and rate of return. The value of money is determined by several factors such as inflation, fluctuation of interest rates, and government policy in securities and taxation. Due to fluctuation of the value of money we need a FV formulation, it is useful for knowing whether an investment can be profitable or not, and this information is useful also in budget analysis. Annuity is defined as a series of payments made for a specific period. The payment, fixed or varied, can be made at the beginning or at the end of the period. Annuity paid at the beginning of the period is called annuity-due and annuity paid at the end of the period is called immediate annuity-certain. The accumulated value of the annuity after few years later is called as a final value or a future value. Burnecki et al. (2003) have discussed about the FV of a fixed-rate annuity in which payments are made according to the arithmetic or geometric series. They have also examined FV for annuities in which payments are made according to arithmetic or geometric series with random rates of interest. This paper examines FV for annuity-due in which payments are made according to arithmetic or geometric series with interest rates as a random variable, which is normal distribution ,  . Future value formula is expressed in recursive form. FV for annuity-due which payments are made according to the arithmetic or geometric series with random rates of interest is calculated by theoretical way and simulation way. The random rates of interest is denoted as k k i  i  , where k i an independent random variable; i is a constant and k  is a random variable which has normal distribution ,  . By using the determined parameters, a sequence of random rates of interest are generated for theoretical calculation. Meanwhile for the simulation, the random rates of interest is generated 1000 times for the simulation. The generated data give mean value, variance, and standard deviation. Moreover, error of mean value, variance, and standard deviation from the theoretical and simulation results are calculated. Error calculation uses Symmetric Mean Absolute Percentage Error (SMAPE). The smaller the value of SMAPE is the more accurate the simulation. The interest rate variances are varied from 0.000036 to 0.0001 to study the relationship between variance of rates of interest and variance of future value. The theoretical and simulation results show that when the variance of random rates of interest increases, then the variance of FV increases linearly. FV of the annuity-due in
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