Application Of Fuzzy Logic With The Mamdani Method To Determine The Risk Zone For The Spread Of Covid-19 In North Sumatra Province

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Introduction

The COVID-19 pandemic has had a profound impact on the world, prompting various efforts to control its spread. One crucial step in this process is understanding and mapping the risk zone of the virus in different regions. In North Sumatra Province, this study utilizes the Fuzzy Mamdani logic method to determine the Risk Zone for the spread of COVID-19. The Fuzzy Mamdani method, a part of the Fuzzy Inference System, is particularly useful in making decisions in the midst of uncertainty.

Background and Literature Review

The COVID-19 pandemic has led to a significant increase in the number of cases worldwide. In Indonesia, North Sumatra Province has been one of the regions most affected by the pandemic. The province has implemented various measures to control the spread of the virus, including social distancing, mask mandates, and vaccination programs. However, these efforts have been hindered by the lack of accurate data on the risk zone of the virus in different regions.

Fuzzy logic is a mathematical approach that deals with uncertainty and imprecision. It is particularly useful in situations where data is incomplete or inaccurate. The Fuzzy Mamdani method is a type of fuzzy logic that uses a set of rules to make decisions. This method has been widely used in various fields, including medicine, finance, and engineering.

Methodology

This study uses weekly COVID-19 data from March 2021 to July 2021 for four selected areas: Medan City, Pematang Siantar City, Simalungun Regency, and Central Tapanuli Regency. The three main variables used in this model are:

*** Positive cases: ** The number of positive COVID-19 cases recorded in each region. *** Suspect cases: ** The number of Covid-19 suspected cases recorded in each region. *** Died Case: ** The number of cases of death due to Covid-19 recorded in each region.

Each variable is represented by a triangular curve and membership function is determined. The implication function of the min method is used to determine the rules applied in the model. The results of the implication function are composed between rules by taking the maximum value, then used to modify the fuzzy area so that the new membership function is obtained. The centroid method is used to get a crisp solution or the final value of the risk zone.

Data Analysis

This research utilizes the Matlab application to calculate the results of the Fuzzy Mamdani model. The results obtained were later compared to real risk zone data, showing an average similarity of 75% for the four regions in North Sumatra. This shows that the model made is good enough to determine the Risk Zone for the spread of COVID-19 in North Sumatra Province.

Additional Analysis and Explanation

The application of the Fuzzy Mamdani method in determining the Risk Zone of the spread of COVID-19 in North Sumatra has several advantages:

*** Handling of uncertainty: ** Fuzzy method can handle uncertain or incomplete data, such as Covid-19 data that may be inaccurate or late reported. ** Ease of implementation: ** This method is easy to implement and can be adjusted to the available data. *** Clear interpretation: ** The results of the fuzzy model can be easily interpreted in the form of risk zones, which facilitate decision making.

Benefits of Research

  • Provide accurate information about the Risk Zone for the Spread of Covid-19 in North Sumatra Province.
  • Assist the government in making the right policy to control the spread of COVID-19.
  • Increasing public awareness about the risk of the spread of Covid-19 in their region.

Conclusion

This research is the first step in developing an early warning system to anticipate the spread of COVID-19 in North Sumatra. The Fuzzy Mamdani model can continue to be developed and integrated with real-time data to increase accuracy and effectiveness in predicting and controlling the spread of viruses.

Recommendations

Based on the results of this study, the following recommendations are made:

  • The government should use the Fuzzy Mamdani model to determine the Risk Zone for the spread of COVID-19 in North Sumatra Province.
  • The model should be integrated with real-time data to increase accuracy and effectiveness in predicting and controlling the spread of viruses.
  • Public awareness about the risk of the spread of Covid-19 in their region should be increased through various channels, including social media and community outreach programs.

Limitations of the Study

This study has several limitations, including:

  • The use of limited data, which may not be representative of the entire province.
  • The lack of consideration of other factors that may affect the spread of COVID-19, such as population density and mobility.
  • The use of a simplified model that may not capture the complexity of the real-world situation.

Future Research Directions

Future research should focus on:

  • Developing a more comprehensive model that takes into account other factors that may affect the spread of COVID-19.
  • Integrating the Fuzzy Mamdani model with other models and data sources to increase accuracy and effectiveness.
  • Testing the model in other regions to determine its applicability and generalizability.

Conclusion

In conclusion, this study demonstrates the effectiveness of the Fuzzy Mamdani method in determining the Risk Zone for the spread of COVID-19 in North Sumatra Province. The model provides accurate information about the risk zone and can be used to assist the government in making the right policy to control the spread of COVID-19. Future research should focus on developing a more comprehensive model and integrating it with other models and data sources to increase accuracy and effectiveness.

Q: What is the Fuzzy Mamdani method and how does it work?

A: The Fuzzy Mamdani method is a type of fuzzy logic that uses a set of rules to make decisions. It works by first defining a set of fuzzy variables and their corresponding membership functions. Then, it uses a set of rules to determine the output of the system based on the input values. The output is then defuzzified to obtain a crisp value.

Q: What are the advantages of using the Fuzzy Mamdani method in determining the Risk Zone for the spread of Covid-19?

A: The Fuzzy Mamdani method has several advantages, including:

  • Handling of uncertainty: Fuzzy method can handle uncertain or incomplete data, such as Covid-19 data that may be inaccurate or late reported.
  • Ease of implementation: This method is easy to implement and can be adjusted to the available data.
  • Clear interpretation: The results of the fuzzy model can be easily interpreted in the form of risk zones, which facilitate decision making.

Q: What are the limitations of the Fuzzy Mamdani method?

A: The Fuzzy Mamdani method has several limitations, including:

  • The use of limited data, which may not be representative of the entire province.
  • The lack of consideration of other factors that may affect the spread of COVID-19, such as population density and mobility.
  • The use of a simplified model that may not capture the complexity of the real-world situation.

Q: How accurate is the Fuzzy Mamdani model in determining the Risk Zone for the spread of Covid-19?

A: The Fuzzy Mamdani model has been shown to be accurate in determining the Risk Zone for the spread of Covid-19, with an average similarity of 75% for the four regions in North Sumatra Province.

Q: Can the Fuzzy Mamdani model be used in other regions?

A: Yes, the Fuzzy Mamdani model can be used in other regions, but it may require adjustments to the model and the data used.

Q: What are the benefits of using the Fuzzy Mamdani model in determining the Risk Zone for the spread of Covid-19?

A: The benefits of using the Fuzzy Mamdani model include:

  • Providing accurate information about the Risk Zone for the Spread of Covid-19 in North Sumatra Province.
  • Assisting the government in making the right policy to control the spread of COVID-19.
  • Increasing public awareness about the risk of the spread of Covid-19 in their region.

Q: How can the Fuzzy Mamdani model be improved?

A: The Fuzzy Mamdani model can be improved by:

  • Developing a more comprehensive model that takes into account other factors that may affect the spread of COVID-19.
  • Integrating the Fuzzy Mamdani model with other models and data sources to increase accuracy and effectiveness.
  • Testing the model in other regions to determine its applicability and generalizability.

Q: What are the future research directions for the Fuzzy Mamdani model?

A: Future research directions for the Fuzzy Mamdani model include:

  • Developing a more comprehensive model that takes into account other factors that may affect the spread of COVID-19.
  • Integrating the Fuzzy Mamdani model with other models and data sources to increase accuracy and effectiveness.
  • Testing the model in other regions to determine its applicability and generalizability.

Q: Can the Fuzzy Mamdani model be used in other fields?

A: Yes, the Fuzzy Mamdani model can be used in other fields, such as medicine, finance, and engineering, where uncertainty and imprecision are present.

Q: What are the implications of the Fuzzy Mamdani model for public health policy?

A: The Fuzzy Mamdani model has implications for public health policy, including:

  • Providing accurate information about the Risk Zone for the Spread of Covid-19 in North Sumatra Province.
  • Assisting the government in making the right policy to control the spread of COVID-19.
  • Increasing public awareness about the risk of the spread of Covid-19 in their region.

Q: How can the Fuzzy Mamdani model be used in practice?

A: The Fuzzy Mamdani model can be used in practice by:

  • Using the model to determine the Risk Zone for the spread of Covid-19 in North Sumatra Province.
  • Integrating the model with other models and data sources to increase accuracy and effectiveness.
  • Testing the model in other regions to determine its applicability and generalizability.