Periodontitis is a serious infection in the gums that can damage the soft tissue and even the tooth supporting bone

Oct 13, 2025 by wbrum.org 37 views

Implementation of the Fuzzy Logic Method and the Naïve Bayes Method in the Expert System Diagnosis of Periodontitis Diseases to Active Smokers

Introduction

The expert system is a branch of computer science that aims to store knowledge from experts so that computers can simulate their expertise in solving certain problems. In the field of health, computer technology has a very important role, considering that health is a crucial aspect in human life. Although many people often ignore dental and oral health, periodontitis is a common problem faced by many people. However, in Indonesia, awareness of this disease is still low. Periodontitis is a serious infection in the gums that can damage the soft tissue and even the tooth supporting bone. One of the main risk factors that contribute to this disease is smoking. Identifying the initial symptoms of periodontitis is very important, especially for active smokers, so that they can take precautions before the condition becomes more serious. Therefore, an effective system is needed to detect the initial symptoms of periodontitis caused by smoking.

Background of the Study

Periodontitis is a chronic bacterial infection that affects the gums and bone supporting the teeth. It is a common problem faced by many people, especially in Indonesia, where awareness of this disease is still low. Smoking is one of the main risk factors that contribute to periodontitis. Smoking can damage the gums and bone supporting the teeth, making it difficult to treat the disease. Identifying the initial symptoms of periodontitis is very important, especially for active smokers, so that they can take precautions before the condition becomes more serious.

Methodology

In this study, the use of the Fuzzy Logic and Naïve Bayes methods to build an expert system that is able to diagnose periodontitis. The Fuzzy Logic method functions to handle data that is uncertain and close to diverse values, while Naïve Bayes is a statistical method that utilizes the Bayes theorem to classify based on existing data. This system was developed using the Java programming language, which allows the integration between algorithms and interactive user interfaces.

Fuzzy Logic Method

The Fuzzy Logic method is a type of artificial intelligence that uses fuzzy sets and fuzzy logic to handle data that is uncertain and close to diverse values. In this study, the Fuzzy Logic method was used to build an expert system that is able to diagnose periodontitis. The Fuzzy Logic method was chosen because it is able to handle data that is uncertain and close to diverse values, which is common in medical diagnosis.

Naïve Bayes Method

The Naïve Bayes method is a statistical method that utilizes the Bayes theorem to classify based on existing data. In this study, the Naïve Bayes method was used to build an expert system that is able to diagnose periodontitis. The Naïve Bayes method was chosen because it is able to classify data based on existing data, which is common in medical diagnosis.

Results

The final result of the study shows that the accuracy level of the fuzzy logic method reaches 93.33%, while the Naïve Bayes method has a higher accuracy rate, which is 96.67%. These numbers indicate the effectiveness of the two methods in diagnosing periodontitis in active smokers. With this system, it is expected that patients can more easily recognize the initial symptoms of this disease and immediately get the right treatment.

Discussion

The implementation of expert systems in the diagnosis of periodontitis not only provides benefits in terms of technology, but is also able to increase public awareness of the importance of maintaining oral health. With an easily accessible system, active smokers can be more aware of the negative impact of smoking on their gum health. In the future, this system can be integrated into broader health applications, so that it can reach more users and provide accurate and useful information in preventing periodontitis.

Conclusion

Through technological advances and the application of appropriate methods, the community is expected to be educated about the importance of maintaining oral health, as well as information related to potential risks from smoking habits. Further research can also be done to develop this system to be more adaptive and able to handle various types of dental and oral diseases, creating better health for the whole community.

Recommendations

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

The expert system developed in this study can be used as a tool for diagnosing periodontitis in active smokers.
The system can be integrated into broader health applications, such as dental clinics and hospitals, to provide accurate and useful information in preventing periodontitis.
Further research can be done to develop this system to be more adaptive and able to handle various types of dental and oral diseases.
Public awareness campaigns can be conducted to educate the community about the importance of maintaining oral health and the potential risks from smoking habits.

Limitations

This study has several limitations, including:

The study only focused on active smokers and did not include other risk factors for periodontitis.
The study only used two methods, Fuzzy Logic and Naïve Bayes, to build the expert system.
The study did not conduct a large-scale clinical trial to validate the results.

Future Research Directions

Future research can be done to develop this system to be more adaptive and able to handle various types of dental and oral diseases. Additionally, further research can be conducted to validate the results of this study and to explore the use of other methods, such as machine learning and deep learning, to build expert systems for diagnosing periodontitis.
Q&A: Expert System Diagnosis of Periodontitis Diseases to Active Smokers

Q: What is periodontitis and how does it affect the gums and teeth?

A: Periodontitis is a serious infection in the gums that can damage the soft tissue and even the tooth supporting bone. It is a common problem faced by many people, especially in Indonesia, where awareness of this disease is still low. Smoking is one of the main risk factors that contribute to this disease.

Q: What is the role of smoking in periodontitis?

A: Smoking is one of the main risk factors that contribute to periodontitis. Smoking can damage the gums and bone supporting the teeth, making it difficult to treat the disease. Identifying the initial symptoms of periodontitis is very important, especially for active smokers, so that they can take precautions before the condition becomes more serious.

Q: What are the symptoms of periodontitis?

A: The symptoms of periodontitis include:

Red, swollen, or bleeding gums
Bad breath
Loose teeth
Pain or tenderness in the gums
Changes in the way the teeth fit together

Q: How can the expert system diagnosis of periodontitis help active smokers?

A: The expert system diagnosis of periodontitis can help active smokers by:

Identifying the initial symptoms of periodontitis
Providing accurate and useful information in preventing periodontitis
Increasing public awareness of the importance of maintaining oral health
Providing a tool for diagnosing periodontitis in active smokers

Q: What are the benefits of using the Fuzzy Logic method in the expert system diagnosis of periodontitis?

A: The Fuzzy Logic method is a type of artificial intelligence that uses fuzzy sets and fuzzy logic to handle data that is uncertain and close to diverse values. The benefits of using the Fuzzy Logic method in the expert system diagnosis of periodontitis include:

Handling data that is uncertain and close to diverse values
Providing accurate and useful information in preventing periodontitis
Increasing public awareness of the importance of maintaining oral health

Q: What are the benefits of using the Naïve Bayes method in the expert system diagnosis of periodontitis?

A: The Naïve Bayes method is a statistical method that utilizes the Bayes theorem to classify based on existing data. The benefits of using the Naïve Bayes method in the expert system diagnosis of periodontitis include:

Classifying data based on existing data
Providing accurate and useful information in preventing periodontitis
Increasing public awareness of the importance of maintaining oral health

Q: Can the expert system diagnosis of periodontitis be integrated into broader health applications?

A: Yes, the expert system diagnosis of periodontitis can be integrated into broader health applications, such as dental clinics and hospitals, to provide accurate and useful information in preventing periodontitis.

Q: What are the limitations of the expert system diagnosis of periodontitis?

A: The limitations of the expert system diagnosis of periodontitis include:

The study only focused on active smokers and did not include other risk factors for periodontitis
The study only used two methods, Fuzzy Logic and Naïve Bayes, to build the expert system
The study did not conduct a large-scale clinical trial to validate the results

Q: What are the future research directions for the expert system diagnosis of periodontitis?

A: Future research can be done to develop this system to be more adaptive and able to handle various types of dental and oral diseases. Additionally, further research can be conducted to validate the results of this study and to explore the use of other methods, such as machine learning and deep learning, to build expert systems for diagnosing periodontitis.