Advantages of Using Not So Naive Algorithm

Oct 13, 2025 by thesweetdesigns.com 79 views

Introduction

The Hokkian language is a widely spoken language among the Chinese community in Medan, Indonesia. Despite its importance, the learning method for this language is still minimal and often done manually. In an effort to simplify the learning process of Hokkian language, it is essential to develop practical and easily accessible media. One of the right solutions is to create a smartphone application. For this reason, the authors designed an Android-based offline dictionary application that integrates the NOT SO NAIVE algorithm for exact string search (Exact String Matching) and the Burkhard-Keller Tree algorithm for approximate string search (approximate string matching). This application also utilizes the SQLite database management system to store data.

Background

The purpose of this study is to evaluate the advantages and disadvantages of each algorithm in terms of information access speed, as well as to show the ratio of running time between the two algorithms. Basically, these two algorithms have a different search mechanism. The not SO Naive algorithm first identifies two cases to determine the shift value, which can be done as many as two positions to the right. On the other hand, the Burkhard-Keller Tree algorithm conducts a search based on the upper and lower limits and the predetermined error tolerance.

Methodology

The results showed that the search time used the Burkhard-Keller Tree algorithm increased by 58.26% and 74% for the not SO Naive algorithm along with the increase in database size. In this case, the not SO Naive algorithm records the average execution time of 25.45 MS, while the Burkhard-Keller Tree algorithm has an average execution time of 59.95 ms.

Algorithm Analysis

Not So Naive Algorithm

The Not So Naive algorithm is a method that is quite efficient for string search. By identifying the shift position carefully, this algorithm can speed up the search process, especially for relatively short text. However, its performance can decrease when dealing with large data, because overhead associated with identification of positions and the amount of shift that may be needed.

Burkhard-Keller Tree Algorithm

Conversely, the Burkhard-Keller Tree algorithm is more suitable for more flexible search, especially when we want tolerance for errors in string search. This algorithm builds a data structure that allows rapid search based on predetermined criteria. Although the execution time tends to be longer than not so naive for small dataset, the benefits are clearly visible when processing a larger and more complex database.

Implementation in Android-Based Indonesian-Hokkian Dictionary Application

By applying these two algorithms to the Indonesian-Hokkian dictionary application, users will get a more effective and efficient learning experience. Application of the right algorithm is very important to improve application performance, ensuring that users can access information quickly and accurately. This research not only shows the importance of selecting algorithms in application development but also shows how technology can help in preserving and learning language that is less explored such as Hokkian.

Advantages of Using Not So Naive Algorithm

The Not So Naive algorithm has several advantages, including:

Efficient search: The Not So Naive algorithm is efficient for string search, especially for relatively short text.
Fast execution time: The Not So Naive algorithm has a fast execution time, with an average execution time of 25.45 MS.
Simple implementation: The Not So Naive algorithm is relatively simple to implement.

Disadvantages of Using Not So Naive Algorithm

The Not So Naive algorithm also has several disadvantages, including:

Decreased performance: The Not So Naive algorithm's performance can decrease when dealing with large data.
Overhead associated with identification of positions: The Not So Naive algorithm has overhead associated with identification of positions and the amount of shift that may be needed.

Advantages of Using Burkhard-Keller Tree Algorithm

The Burkhard-Keller Tree algorithm has several advantages, including:

Flexible search: The Burkhard-Keller Tree algorithm is more suitable for more flexible search, especially when we want tolerance for errors in string search.
Rapid search: The Burkhard-Keller Tree algorithm builds a data structure that allows rapid search based on predetermined criteria.
Benefits visible when processing larger and more complex database: The benefits of the Burkhard-Keller Tree algorithm are clearly visible when processing a larger and more complex database.

Disadvantages of Using Burkhard-Keller Tree Algorithm

The Burkhard-Keller Tree algorithm also has several disadvantages, including:

Increased execution time: The Burkhard-Keller Tree algorithm has an increased execution time, with an average execution time of 59.95 ms.
Complex implementation: The Burkhard-Keller Tree algorithm is relatively complex to implement.

Conclusion

In conclusion, the Not So Naive algorithm and the Burkhard-Keller Tree algorithm are both suitable for string search, but they have different search mechanisms and advantages and disadvantages. The Not So Naive algorithm is efficient for string search, especially for relatively short text, but its performance can decrease when dealing with large data. The Burkhard-Keller Tree algorithm is more suitable for more flexible search, especially when we want tolerance for errors in string search, but it has an increased execution time. By applying these two algorithms to the Indonesian-Hokkian dictionary application, users will get a more effective and efficient learning experience.

Recommendations

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

Use the Not So Naive algorithm for small datasets: The Not So Naive algorithm is efficient for string search, especially for relatively short text, and has a fast execution time.
Use the Burkhard-Keller Tree algorithm for larger and more complex databases: The Burkhard-Keller Tree algorithm is more suitable for more flexible search, especially when we want tolerance for errors in string search, and has benefits visible when processing a larger and more complex database.

Future Research Directions

Future research directions include:

Comparing the Not So Naive algorithm and the Burkhard-Keller Tree algorithm with other algorithms: It would be interesting to compare the Not So Naive algorithm and the Burkhard-Keller Tree algorithm with other algorithms, such as the Knuth-Morris-Pratt algorithm and the Rabin-Karp algorithm.
Applying the Not So Naive algorithm and the Burkhard-Keller Tree algorithm to other applications: The Not So Naive algorithm and the Burkhard-Keller Tree algorithm can be applied to other applications, such as text search and data compression.

Limitations of the Study

The limitations of this study include:

Limited dataset size: The dataset size used in this study was relatively small, which may not be representative of larger and more complex databases.
Limited algorithm comparison: This study only compared the Not So Naive algorithm and the Burkhard-Keller Tree algorithm, and did not compare them with other algorithms.

Conclusion

Introduction

The Not So Naive algorithm and the Burkhard-Keller Tree algorithm are two popular algorithms used for string search. In this article, we will answer some frequently asked questions (FAQs) about these algorithms.

Q: What is the Not So Naive algorithm?

A: The Not So Naive algorithm is a string search algorithm that is efficient for relatively short text. It works by identifying the shift position carefully, which can speed up the search process.

Q: What is the Burkhard-Keller Tree algorithm?

A: The Burkhard-Keller Tree algorithm is a string search algorithm that is more suitable for more flexible search, especially when we want tolerance for errors in string search. It builds a data structure that allows rapid search based on predetermined criteria.

Q: What are the advantages of the Not So Naive algorithm?

A: The Not So Naive algorithm has several advantages, including efficient search, fast execution time, and simple implementation.

Q: What are the disadvantages of the Not So Naive algorithm?

A: The Not So Naive algorithm also has several disadvantages, including decreased performance when dealing with large data, overhead associated with identification of positions, and the amount of shift that may be needed.

Q: What are the advantages of the Burkhard-Keller Tree algorithm?

A: The Burkhard-Keller Tree algorithm has several advantages, including flexible search, rapid search, and benefits visible when processing larger and more complex databases.

Q: What are the disadvantages of the Burkhard-Keller Tree algorithm?

A: The Burkhard-Keller Tree algorithm also has several disadvantages, including increased execution time, complex implementation, and the need for a larger and more complex database.

Q: When should I use the Not So Naive algorithm?

A: You should use the Not So Naive algorithm when you need to search for a string in a relatively small dataset.

Q: When should I use the Burkhard-Keller Tree algorithm?

A: You should use the Burkhard-Keller Tree algorithm when you need to search for a string in a larger and more complex database, or when you want tolerance for errors in string search.

Q: Can I use both algorithms together?

A: Yes, you can use both algorithms together to take advantage of their strengths. For example, you can use the Not So Naive algorithm for small datasets and the Burkhard-Keller Tree algorithm for larger and more complex databases.

Q: How do I implement the Not So Naive algorithm and the Burkhard-Keller Tree algorithm?

A: You can implement the Not So Naive algorithm and the Burkhard-Keller Tree algorithm using a programming language such as Java or Python. There are also many libraries and frameworks available that can help you implement these algorithms.

Q: What are some common use cases for the Not So Naive algorithm and the Burkhard-Keller Tree algorithm?

A: Some common use cases for the Not So Naive algorithm and the Burkhard-Keller Tree algorithm include text search, data compression, and string matching.

Q: What are some common mistakes to avoid when using the Not So Naive algorithm and the Burkhard-Keller Tree algorithm?

A: Some common mistakes to avoid when using the Not So Naive algorithm and the Burkhard-Keller Tree algorithm include using the wrong algorithm for the task, not considering the size of the dataset, and not optimizing the algorithm for performance.

Conclusion

In conclusion, the Not So Naive algorithm and the Burkhard-Keller Tree algorithm are two popular algorithms used for string search. By understanding their strengths and weaknesses, you can choose the right algorithm for your task and implement it effectively.