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Science and Technology Indonesia

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Published by Universitas Sriwijaya

ISSN : 25804405 EISSN : 25804391 DOI : -

Core Subject : Science, Social, Engineering,

Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Environmental Science Materials Science & Nanotechnology Physics

An international Peer-review journal in the field of science and technology published by The Indonesian Science and Technology Society. Science and Technology Indonesia is a member of Crossref with DOI prefix number: 10.26554/sti. Science and Technology Indonesia publishes quarterly (January, April, July, October). Science and Technology Indonesia is an international scholarly journal on the field of science and technology aimed to publish a high-quality scientific paper including original research papers, reviews, short communication, and technical notes. This journal welcomes the submission of articles that covers a typical subject of natural science and technology such as: > Chemistry > Biology > Physics > Marine Science > Pharmacy > Chemical Engineering > Environmental Science and Engineering > Computational Engineering > Biotechnology Journal Commencement: October 2016

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Articles 5 Documents

Search results for , issue "Vol. 5 No. 3 (2020): July" : 5 Documents clear

Simulation Study of Autocorrelated Error Using Bayesian Quantile Regression Nayla Desviona; Ferra Yanuar
Science and Technology Indonesia Vol. 5 No. 3 (2020): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

The purpose of this study is to compare the ability of the Classical Quantile Regression method and the Bayesian Quantile Regression method in estimating models that contain autocorrelated error problems using simulation studies. In the quantile regression approach, the data response is divided into several pieces or quantiles conditions on indicator variables. Then, The parameter model is estimated for each selected quantiles. The parameters are estimated using conditional quantile functions obtained by minimizing absolute asymmetric errors. In the Bayesian quantile regression method, the data error is assumed to be asymmetric Laplace distribution. The Bayesian approach for quantile regression uses the Markov Chain Monte Carlo Method with the Gibbs sample algorithm to produce a converging posterior mean. The best method for estimating parameter is the method that produces the smallest absolute value of bias and the smallest confidence interval. This study resulted that the Bayesian Quantile method produces smaller absolute bias values and confidence intervals than the quantile regression method. These results proved that the Bayesian Quantile Regression method tends to produce better estimate values than the Quantile Regression method in the case of autocorrelation errors. Keywords: Quantile Regression Method, Bayesian Quantile Regression Method, Confidence Interval, Autocorrelation.

Quantile Regression Approach to Model Censored Data Sarmada Sarmada; Ferra Yanuar
Science and Technology Indonesia Vol. 5 No. 3 (2020): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Abstract The censored quantile regression model is derived from the censored model. This method is used to overcome problems in modeling censored data as well as to overcome the assumptions of linear models that are not met. The purpose of this study is to compare the results of the analysis of the quantile regression method with the censored quantile regression method for censored data. Both methods were applied to generated data of 150, 500, and 3000 sample size. The best model is then chosen based on the smallest absolute bias and the smallest standard error as an indicator of the goodness of the model. This study proves that the censored quantile regression method tends to produce smaller absolute bias and a smaller standard error than the quantile regression method for all three group data specified. Thus it can be concluded that the censored quantile regression method could result in acceptable model for censored data. Keywords: Censored data; quantile regression; quantile regression censored; standard error; absolute bias.

Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach Yulia Resti; Firmansyah Burlian; Irsyadi Yani; Des Alwine Zayanti; Indah Meiliana Sari
Science and Technology Indonesia Vol. 5 No. 3 (2020): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%. Cans is one type of inorganic waste that can take up to hundreds of years to be decomposed on the ground so that recycling is the right solution for managing cans waste. In the recycling industry, can classification systems are needed for the sorting system automation. This paper discusses the cans classification system based on the digital images using the Naive Bayes method, where the input variables are the pixel values of red, green, and blue (RGB) color, and the image of the can is captured by placing it on a conveyor belt which runs at a certain speed. The average accuracy rate of the k-fold cross-validation which is less satisfactory from the classification system obtained using the original Naive Bayes model is corrected using the fuzzy approach. This approach succeeded in improving the average accuracy of the can classification system which was originally from 52.99% to 88.02% or an increase of 60.2%, where the standard deviation decreased from 15.72% to only 3%.

Transesterification Reaction from Rice Bran Oil to Biodiesel over Heterogeneous Base Calcium Oxide Nanoparticles Catalyst Nur Fatin Sulaiman; Abdul Rahim Yacob; Siew Ling Lee
Science and Technology Indonesia Vol. 5 No. 3 (2020): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

This research focused on the use of heterogeneous base catalyst, calcium oxide (CaO), an alkaline earth metal oxide to produce biodiesel. The aim of this research is to investigate the potential of commercial calcium carbonate, CM-CaCO3 to be transformed to nanostructured CaO and further used as a heterogeneous base catalyst for single step transesterification of rice bran oil to biodiesel. The CaO samples were calcined at temperatures of 100°C to 700°C under vacuum at 10-3 mbar. TGA-DTA result displayed that the calcination temperature for CM-CaCO3 to form CaO must be higher than 600°C. This was supported by FTIR results which indicated the complete formation of CaO at 700°C. XRD showed the rhombohedral CaCO3 and hexagonal Ca(OH)2 were totally disappeared, leaving only crystalline cubic CaO at 700oC. Interestingly, CaO obtained at 700°C (CaO-700) showed the larger BET surface area and highest basicity with 11.5 m2g-1 and 1.959 mmol/g, respectively. The prepared nanostructured CaO-700 was selected and applied for single step transesterification reaction of rice bran oil to produce biodiesel. NMR and GC-FID results further confirmed that biodiesel was successfully formed using CaO-700 as catalyst.

The Chitosan-Sodium Alginate Submicro Particles Loading Herbal of Ethanolic Exract of Leaves Senna Alata. L for Curing of Bacterial Infection on Skin Mardiyanto Mardiyanto; Indah Sholihah; Thio Gunawan Jaya
Science and Technology Indonesia Vol. 5 No. 3 (2020): July
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Abstract Mardiyanto 1*, Indah Solihah 1, and Thio Gunawan Jaya 1 1Department of Pharmacy Faculty of Science Sriwijaya University *Corresponding Author : mardiyantoUNSRI@gmail.com This research was performed to detect the activity of the optimum formula of chitosan and sodium alginate submicro particles loading of the ethanol extract of leaves ketepeng cina (Senna alata L.) to P. acne growth. Submicro manufactured particles was used three variations of the formula from three of volume used of calcium chloride (CaCl2): 20, 40, and 100 mL based on ionic gelation method. The results obtained of %EE in formulas 1, 2 and 3 were 78.56%, 81.71%, and 77.48%. Formula 2 with a value of % EE of 81.71% was used as an optimum formula which indicates that the particles are well protected by chitosan and sodium alginate polymers so as to prevent particle damage during the homogenization process. The results of the diameter measurements showed that the optimal formula enters in the submicro particle range with the value obtained is 525,9455 nm. The submicro formula of chitosan alginate particles loading the ketepeng cina leaf ethanol extract was barely homogeneous which based on the results obtained by the PSA was 0.433 of PDI. The zeta potential value was +3.5 mV. The results of X-ray diffraction analysis or X ̶ Ray Diffraction produce a pattern shaped amorph with the resulting peak does not have a wide distance and pattern. In vivo testing using ketepeng cina leaf extract as submicro was decreased the P. acne lesion faster than using only ketepeng cina leaf extract because the submicro particle preparation has a small particle that is below to 600 nm making it easier to penetrate the skin pores to reach the target. The results of the analysis of the curing of many lesions on the skin of mice have a significance value of homogeneity. The one-way ANOVA test of < 0.05 was a significant effect of the test group on the decreasing in the number of lesions on the skin of the mouse.

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