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Pradana ratnasari, Nanda Rizqia

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Author-ID : 7484450

Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Medicine & Pharmacology

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Meta-Analysis: Effectiveness of SSRIs vs SSRIs in conjunction with CBT in treating depression in young adolescents Kresnia, Gabriele Mustika; Hasuki, Winda; Pradana ratnasari, Nanda Rizqia
Indonesian Journal of Life Sciences 2020: IJLS Vol 02 No .01
Publisher : Indonesia International Institute for Life Sciences

Objective: To compare the efficacy of SSRI medication alone and SSRI+CBT combined. Methods: NCBI Pubmed, DARE, CSDR and NGC were searched October-November 2019. The population size, as well as the base and endpoint CGAS mean and standard deviation from the three studies included, are recorded. Statistical analysis was done in RStudio with the "meta" package. Results: For the SSRI only, the effect size was -1.82 with a 95% confidence interval between -2.28 and -1.37. For the SSRI and CBT combined, the effect size was -1.68 with a 95% confidence interval between -2.39 and -0.98. The effect size for both SSRI and SSRI + CBT didn't cross the null effect line, but the heterogeneity exceeds 50%. The result for the comparison of post SSRI vs. SSRI + CBT showed the effect size of -0.05 with a 95% confidence interval between -0.23 and 0.12. The size effect did cross the null effect line, but the heterogeneity was less than 50%. Conclusion: Both methods were shown to be effective. However, due to statistical inconsistencies, it couldn’t be concluded whether the combination of SSRI and CBT is better than treatment with SSRI alone.

ARI(1,1) Model for Predicting Covid19 in Indonesia Pradana ratnasari, Nanda Rizqia
Indonesian Journal of Life Sciences 2020: IJLS Vol 02 No .02
Publisher : Indonesia International Institute for Life Sciences

Covid19 modelling is needed to help people understanding the distribution or pattern of the data and doing the prediction. The data used for modelling in this study was ‘confirmed cases’ of Covid19 in Indonesia recorded from March 2 to August 23, 2020. Model obtained from analysis was ARI(1,1) with estimated parameter 0.9859 and standard error 0.0114. Maximum Likelihood was the method conducted to estimate the parameters. The model was good to predict the actual data of Covid19 confirmed cases in Indonesia. Keywords: Covid19, autoregressive, prediction.

Comparative Study of K-Mean, K-Medoid and Hierarchical Clustering using Data of Tuberculosis Indicators in Indonesia Pradana Ratnasari, Nanda Rizqia
Indonesian Journal of Life Sciences 2023: IJLS Vol 05 No .02
Publisher : Indonesia International Institute for Life Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54250/ijls.v5i02.181

Cluster analysis is an important topic and analysis in which the ultimate goal is to classify data into several groups based on similar basic. The most applied cluster methods or algorithms are k-means, k-medoids and hierarchical clustering methods. Therefore, this study aimed to compare methods in cluster analysis employing healthcare data on attributes related to TB. The best method will be assigned based on the level of accuracy for each algorithm and the number of clusters. There were four main steps in the clustering analysis used in this study, which were feature selection, clustering algorithm, cluster validation and interpretation. The clustering algorithm used are k-means, k-medoids and hierarchical clustering, with cluster sizes of 2, 3 and 4. The result showed that k-medoids have a higher accuracy than other clustering algorithms or methods. This study explained that compared to k-means and hierarchical clustering, k-medoid had the highest accuracy for both training and testing data. K-medoid was better than the other two algorithms as it was more robust to noise and outliers which were found in the datasets. This outcome was consistent with the training and testing datasets. In terms of the number of clusters, the two-cluster model was better than the three-cluster or the four-cluster model as this model could classify the groups vividly. The results were consistent in k-mean, k-medoid and hierarchical clustering methods, with the smallest sum of squares value of 24.7% for the k-mean. The smallest diameters and the average dissimilarities of k-medoid models were found in group 1. This result explained that group 1, in all algorithms, was more compact and more similar than other groups.

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