Background: Indonesia through its government National Health Insurance System has launched a non-communicable and chronic disease management program named Indonesian Chronic Disease Management Program (PROLANIS), with Type 2 Diabetes Mellitus (T2DM) and hypertension as the main focus. However, study that evaluates the clinical impact of PROLANIS in patients with T2DM is still scarce to this date. This study aims to evaluate the metabolic control and renal function of PROLANIS participants with T2DM every six month within the first 18-months of implementation. Methods: This study was a retrospective cohort study conducted at Wates sub-district, East Java using secondary data from PROLANIS group report from April 2018 to October 2019. The study population was T2DM patients who voluntarily joined the PROLANIS group in April 2018. The six-month-evaluation included metabolic parameters [body mass index (BMI), blood pressure, hemoglobin A1C, total cholesterol, high-density lipid, low-density lipid, and triglyceride (TG)] and renal parameters [blood urea nitrogen (BUN), creatinine serum, and urinary microalbumin]. Paired t-test and wilcoxon signed-rank test was used for the analysis, and the P-value was adjusted using Bonferroni correction. A P-value < .0015 was considered statistically significant, while a P-value between .0015 and .003 was considered as marginally significant. Results: A total of 30 participants were included in the analysis. Following the PROLANIS implementation, the only parameter of metabolic control that showed significant improvement was TG serum level ( P < .001). Despite the worsening status of other metabolic parameters, the changes were not statistically significant except for BMI that was marginally significant ( P = .002). From renal function, only BUN serum level was significantly deteriorated ( P < .001), while the others did not significantly change. Conclusion: PROLANIS implementation in our study population seems to be ineffective. Future study with more primary healthcare centers needs to be done to scrutinize the clinical impact of this program nationwide.
Universiti Brunei Darussalam, Jalan Tungku Link, BE1410 Brunei Darussalam 6 firman11@mhs.ee.its.ac.id 1 , mochar@ee.its.ac.id 2 , ketut@ee.its.ac.id 3 , alfaruqi@pens.ac.id 4 , elly@pens.ac.id 5 , anshari.ali@ubd.edu.bn 6Abstrack -Understanding public interest and opinion are necessary tasks in high intense political competition. Utilizing big data analytics from social media provide an important source of information that candidates can utilize, manage and even engage them in targeted political campaigning agenda. One of the sources in big data is social media's interactions. Social media empowers public to participate proactively in the campaigning activities. This paper examines trends gathered from data analytics of two contenders' group for Indonesian Election in 2019. It tracks the recent patterns of people engagement via social media analytic specifically Twitter. The study developed the analysis into the proposed model based on their trends and patterns. The study revealed that political parties are building online social networks to enable them to engage with the public, disseminate ideas and information, gauge public opinion, monitor trends, and obtain immediate feedback. It provides a platform as a listening tool that can be used to capture information and conversations about parties or candidates, and analysis of the data to monitor trends in a candidate's acceptability.
This study aims to improve classification accuracy by transforming continuous attributes into categories by randomly generating percentile values as categorization limits. Four algorithms were compared for the generation of percentile values and selected based on the small variability of the percentile values and the distribution of the highest revenue expectations. The distribution of testing and training data classification accuracy becomes the second consideration. Random forest (RF) classification is modeled from selected percentiles with three transformation variations. The results of the ANOVA test, the algorithm with three variations of the transformation, has a mean that is not significantly different from the best model and the original dataset model. However, in some variations of training data, RF classification with continuous attribute transformation was superior to the original dataset model. The effectiveness of this continuous attribute transformation algorithm was very well applied to the LR, MLP, and NB methods. In the tuition fee dataset, the application of the algorithm for the three methods each had an accuracy of 0.178, 0.204, and 0.318. The results of the attribute transformation give a significant increase in accuracy to 0.967, 0.949, and 0.594 for each method, respectively. In the date fruits dataset, the attribute transformation was effective in the MLP method with an accuracy of 0.193 (original attribute) to 0.690 (continuous attribute transformation). The transformation results are effectively applied to the LR, MPL, and NB methods for datasets with continuous and categorical mixed attributes.
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