Performance Analysis Of The K-Nearest Neighbor Method In The PPDB Zoning System

by ADMIN 80 views

Introduction

In the 2018-2019 school year, the government of Indonesia issued Ministerial Regulation Number 51 of 2018 relating to the Acceptance of New Students (PPDB). This regulation aimed to ensure the achievement of equitable quality education in all schools, especially for junior high schools (SMP) in Medan City. The PPDB zoning system is a complex process that involves the classification of new student admissions based on various attributes. In this study, we focus on the analysis of new student admission data from four school samples involving 1,725 training data that have been modeled previously.

The PPDB zoning system is a critical component of the education system in Indonesia, as it determines the distribution of students to various schools based on their characteristics. The system aims to provide equal access to quality education for all students, regardless of their background or location. However, the PPDB zoning system is often criticized for being complex and biased towards certain schools. Therefore, there is a need to develop a more efficient and effective method for classifying new student admissions.

Understanding the K-Nearest Neighbor Method

The K-Nearest Neighbor (KNN) method is a classification technique used to group data based on its proximity to other data. KNN works by finding the closest neighbor from a new data point and then determines which class is most often appearing among these neighbors. In the context of PPDB, this method can help determine which schools are most suitable for prospective students based on their characteristics.

The KNN method is a simple yet effective technique for classification. It works by calculating the distance between the new data point and each of the existing data points. The distance is calculated using a distance metric, such as Euclidean distance or Manhattan distance. The KNN method then selects the K nearest neighbors to the new data point and determines the class of the new data point based on the class of the K nearest neighbors.

KNN Implementation Process in PPDB

The KNN method can be implemented in the PPDB zoning system as follows:

  1. Data collection: Data on new student admissions from several schools is processed into a dataset that can be used. The data includes various attributes such as academic values, location, and demographic information.
  2. Dataset modeling: The dataset that has been collected is then modeled with relevant attributes for classification purposes.
  3. Data grouping: Using the KNN method, student data will be grouped based on the closest distance to the data center of each existing school.
  4. Performance test: The value of K is determined to test the performance of the model. Smaller K values may be more sensitive to noise, while larger K values can smooth predictions but may lose important details.

Performance Analysis of the KNN Method

The performance of the KNN method was analyzed by determining the value of K, which functions as a parameter in performance testing. By calculating the difference in accuracy value produced, researchers can draw conclusions about the effectiveness of the use of KNN in the PPDB zoning system.

The results of the analysis indicate a variation in accuracy depending on the value of K chosen. The results of this classification become a useful tool for the school and parents in determining the appropriate school choices based on the desired criteria.

Results and Conclusions

After carrying out the analysis using KNN, the results obtained indicate a variation in accuracy depending on the value of K chosen. The results of this classification become a useful tool for the school and parents in determining the appropriate school choices based on the desired criteria.

The final conclusion of the study confirms that the use of the K-Nearest Neighbor method shows good potential in the classification of PPDB zoning. Thus, KNN can be integrated as a tool in the process of admission of new students who are more just and measurable, and are able to help the government achieve more equitable and quality educational goals.

Future Research Directions

This study provides a foundation for future research in the area of PPDB zoning. Future studies can focus on the following areas:

  • Improving the accuracy of the KNN method: Future studies can focus on improving the accuracy of the KNN method by using more advanced techniques, such as ensemble methods or deep learning.
  • Developing a more efficient KNN method: Future studies can focus on developing a more efficient KNN method that can handle large datasets and provide faster results.
  • Applying the KNN method to other areas: Future studies can focus on applying the KNN method to other areas, such as student placement or teacher assignment.

Conclusion

In conclusion, this study provides a comprehensive analysis of the K-Nearest Neighbor method in the PPDB zoning system. The results of the study indicate that the KNN method shows good potential in the classification of PPDB zoning. Therefore, KNN can be integrated as a tool in the process of admission of new students who are more just and measurable, and are able to help the government achieve more equitable and quality educational goals.

By understanding and implementing this method, it is hoped that the PPDB zoning system will be more efficient and effective in providing quality access to all students.

Q: What is the K-Nearest Neighbor method?

A: The K-Nearest Neighbor (KNN) method is a classification technique used to group data based on its proximity to other data. KNN works by finding the closest neighbor from a new data point and then determines which class is most often appearing among these neighbors.

Q: How does the KNN method work in the PPDB zoning system?

A: In the PPDB zoning system, the KNN method is used to classify new student admissions based on their characteristics. The method works by calculating the distance between the new data point and each of the existing data points. The distance is calculated using a distance metric, such as Euclidean distance or Manhattan distance. The KNN method then selects the K nearest neighbors to the new data point and determines the class of the new data point based on the class of the K nearest neighbors.

Q: What is the value of K in the KNN method?

A: The value of K in the KNN method is a parameter that determines the number of nearest neighbors to consider when classifying a new data point. A smaller value of K may be more sensitive to noise, while a larger value of K can smooth predictions but may lose important details.

Q: How does the KNN method improve the PPDB zoning system?

A: The KNN method improves the PPDB zoning system by providing a more efficient and effective way to classify new student admissions. The method helps to reduce bias and ensure that students are placed in schools that are a good match for their characteristics.

Q: What are the benefits of using the KNN method in the PPDB zoning system?

A: The benefits of using the KNN method in the PPDB zoning system include:

  • Improved accuracy: The KNN method provides a more accurate way to classify new student admissions.
  • Reduced bias: The KNN method helps to reduce bias and ensure that students are placed in schools that are a good match for their characteristics.
  • Increased efficiency: The KNN method is a fast and efficient way to classify new student admissions.

Q: What are the limitations of the KNN method in the PPDB zoning system?

A: The limitations of the KNN method in the PPDB zoning system include:

  • Sensitivity to noise: The KNN method may be sensitive to noise in the data, which can affect its accuracy.
  • Overfitting: The KNN method may overfit the data, which can lead to poor performance on new data.
  • Computational complexity: The KNN method can be computationally complex, especially for large datasets.

Q: How can the KNN method be improved in the PPDB zoning system?

A: The KNN method can be improved in the PPDB zoning system by:

  • Using more advanced techniques: Using more advanced techniques, such as ensemble methods or deep learning, can improve the accuracy and efficiency of the KNN method.
  • Optimizing the value of K: Optimizing the value of K can help to improve the accuracy and efficiency of the KNN method.
  • Using more robust distance metrics: Using more robust distance metrics can help to improve the accuracy and efficiency of the KNN method.

Q: What are the future research directions for the KNN method in the PPDB zoning system?

A: The future research directions for the KNN method in the PPDB zoning system include:

  • Improving the accuracy of the KNN method: Improving the accuracy of the KNN method by using more advanced techniques or optimizing the value of K.
  • Developing a more efficient KNN method: Developing a more efficient KNN method that can handle large datasets and provide faster results.
  • Applying the KNN method to other areas: Applying the KNN method to other areas, such as student placement or teacher assignment.