Background: Understanding the pattern of COVID-19 infection progression is critical for health policymakers. Reaching the exponential peak of cases, flattening the curve, and treating all of the active cases are the keys to success in reducing outbreak transmission. The objective of this study was to determine the most effective model for predicting the peak of COVID-19 in Indonesia, using a deterministic model. Methods: The SEI2RS model considers five strategies for control, namely: large-scale social restriction (u 1), contact tracing (u 2), mass testing (u 3), case detection and treatment (u 4), and the wearing of face masks (u 5). Three scenarios were developed, each differentiated by the controls. The model used April 10, 2020, and December 31, 2020, as the initial and final times. Results: The simulation results indicated that the peak of COVID-19 cases for scenarios 1, 2, and 3 occur on the 59th day with 33,151 cases, on the 38th day with 37,908 cases, and on the 40th day with 39,305 cases. For all of the scenarios, the decline phase shows a slow downward slope and about 8000 cases of COVID-19 still active by the end of 2020. Conclusion: The study concludes that scenario 2, which consists of large-scale social restriction (u 1), contact tracing (u 2), case detection and treatment (u 4), and the wearing of face masks (u 5), is the most rational scenario to control COVID-19 spreading in Indonesia.

In this study, the retention index data of 146 compounds that are found in coal and petroleum-derived liquid fuels were grouped using the K-means clustering method, and the similarities between each cluster were analyzed. The psycho-chemical properties of each compound in the cluster were identified and compared with other clusters. Each compound’s retention index is grouped based on the similarity between the column polarity and heating rate of one compound to another. Based on the results of tests carried out on nine differentk values, it is known that the grouping with the value of k = 3 is the best determined from the obtained silhouette score = 0.568, where this score is higher than the score obtained on the other k values. The results of clustering with k = 3 obtained three clusters, namely cluster C1, cluster C2, and cluster C3. Cluster C1 and cluster C2 consist of chemical compounds that have a relatively low carbon number and molecular mass, but in cluster C2 the molecular mass of the compound is lower than in cluster C1. In contrast, the C3 cluster consists of chemical compounds that have a relatively high carbon number and molecular mass.

No abstract

No abstract

Scheduling algorithm is one of computation problem with high complexity. One of the problems is a difficulty to determine an optimal scheduling alternative that is adjusted with the constraints needed. This study aims to test whether the Genetic Algorithm (GA) program that was built was successful for maximizing the value of fitness. The data used in this study is data simulation from the GA Program. Based on the result of this study, we can concluded that the GA program can be used for scheduling because it has good accuracy. This can be seen from the fitness value of 1 and there is no clash between one schedule and another schedule and there are no restrictions that are violated.