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Journal : Proceeding of International Conference on Humanity Education and Society

SIMULATION OF DISCRETE-TIME MARKOV CHAIN SUSCEPTIBLE VACCINATED INFECTED RECOVERED SUSCEPTIBLE (DTMC SVIRS) STOCHASTIC EPIDEMIC MODEL ON THE SPREAD OF TUBERCULOSIS DISEASE IN CENTRAL JAVA Arnandya, Evelyn Regita; Respatiwulan, Respatiwulan; Susanti, Yuliana
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

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Abstract

One of the infectious diseases that is still a public health challenge in Indonesia is tuberculosis (TB). This study is intended to model the spread of TB disease in Central Java using the Discrete-Time Markov Chain Susceptible Vaccinated Infected Recovered Susceptible (DTMC SVIRS) stochastic epidemic model. This model categorizes the population into four groups: susceptible, vaccinated, infected, and recovered. The transition probabilities between these groups are obtained based on transmission, vaccination, vaccine failure, vaccine effectiveness, recovery, and waning immunity rates. Parameter values were estimated using TB data from the Central Java Health Profile. Simulations were performed with different transmission rate treatments to analyze their effect on epidemic dynamics. The results show that the higher transmission rate, the longer it takes to reach the peak of epidemic and the more individuals are infected, which indicates a more serious epidemic. The model predicts that the epidemic will continue timelessly due to waning immunity and remaining susceptibility. The SVIRS model provides an overview of the spread of TB in Central Java.
COMPARISON OF ROBUST REGRESSION RESULTS OF SCALE (S) ESTIMATION AND METHOD OF MOMENT (MM) ESTIMATION ON THE CLOSING PRICE OF ENERGY SECTOR STOCKS IN 2022 Hilyatul Hilwy, Sarah; Susanti, Yuliana; Nirwana, Muhammad Bayu
International Conference on Humanity Education and Society (ICHES) Vol. 3 No. 1 (2024): Third International Conference on Humanity Education and Society (ICHES)
Publisher : FORPIM PTKIS ZONA TAPAL KUDA

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Abstract

The development of the company is undoubtedly inseparable from financial factors. The company will issue shares that investors will purchase. Investors will consider the state of the company they invest in investment activities. Fundamental analysis can assess the company's condition by calculating company ratios. The existence of fundamental analysis can help investors make decisions. Capital market movements often experience fluctuations or extreme events in the stock market that cause outliers in stock price data. Outliers in the data can be overcome by using robust regression to reduce the impact of outliers on the data. This analysis uses S and MM estimations with Tukey Bisquare weights to estimate the model. Energy sector stock closing price data will be tested for classical assumptions, including normality, homoscedasticity, autocorrelation, and multicollinearity tests. If the energy sector stock closing price data does not meet normality, detect outliers and continue estimating data using S and MM estimations. The best model to estimate the data is the MM estimation with an adjusted R-Square value of 99.86%, fulfilling the parameter significance test, namely the t-test and F-test.