Improving the Data Collection Component

Oct 13, 2025 by orovajewels.com 102 views

Introduction

In today's digital age, the internet has become an indispensable part of our daily lives. With an overwhelming amount of information available at our fingertips, it can be challenging to find relevant and accurate information, often resulting in wasted time. One crucial piece of information that circulates on the internet is the doctor's practice schedule listed on various hospital websites. This study aims to gather and present information about doctor's practice schedules from several hospitals in Medan, which are distributed on various websites.

Background

The doctor's practice schedule is a vital piece of information that patients need to access in order to plan their medical appointments. However, with the increasing number of hospitals and doctors, it can be difficult for patients to find the most up-to-date and accurate information. This is where the concept of the semantic web comes in. The semantic web is a system of interlinked data that can be processed by machines, making it easier for users to find and access relevant information.

Methodology

This study employed the use of semantic web technology to gather and present information about doctor's practice schedules from several hospitals in Medan. The information gathering process began with extracting data from the hospital website manually into metadata using OWL (Web Ontology Language). This metadata was then processed further by parsing it into the SPO table format (subject, predicate, object) using Rap (RDF API for PHP) and queries using Apache Jena Fuseki.

Data Search Process

In the data search process, the Boyer-Moore algorithm was used as a string matching method. The Boyer-Moore algorithm is a fast string searching algorithm that can be used to search for a pattern within a text. In this study, the algorithm was used to search for doctor's practice schedules based on several criteria such as doctor's name, specialization, and practice shift.

System Design

The system design of this study consisted of several components, including:

Data Collection: The data collection component was responsible for extracting data from the hospital website and storing it in a database.
Data Processing: The data processing component was responsible for processing the extracted data into a format that can be easily searched and accessed.
Search Engine: The search engine component was responsible for searching the processed data based on user input.
Result Presentation: The result presentation component was responsible for presenting the search results to the user in a user-friendly format.

Results

Based on the tests that have been carried out, the results show that data collection using semantic web technology and searching with the Boyer-Moore algorithm can provide the right and fast results in accordance with the user input. This shows the efficiency of the use of semantic technology in managing and presenting medical information, which is certainly beneficial for patients who need fast and accurate access to the doctor's schedule.

Conclusion

Overall, this study illustrates the great potential of the application of the semantic web in the health sector, especially in providing relevant and latest information to the public. By using ontology and sophisticated search algorithms, medical data management becomes more structured and easily accessible, reducing the time and business needed by users to find important information related to health services. Thus, this implementation not only increases information accessibility, but also contributes to improving the quality of health services.

Future Work

Future work can be done to improve the system design and functionality of the semantic web-based system. Some possible directions for future work include:

Improving the Data Collection Component: The data collection component can be improved by using more advanced data extraction techniques, such as web scraping or data mining.
Enhancing the Search Engine Component: The search engine component can be enhanced by using more advanced search algorithms, such as natural language processing or machine learning.
Expanding the System to Other Hospitals: The system can be expanded to include data from other hospitals, making it a more comprehensive and useful resource for patients.

References

[1] OWL (Web Ontology Language): A language for representing knowledge on the web.
[2] Rap (RDF API for PHP): A PHP library for working with RDF data.
[3] Apache Jena Fuseki: A Java library for working with RDF data.
[4] Boyer-Moore Algorithm: A fast string searching algorithm.
[5] Semantic Web: A system of interlinked data that can be processed by machines.

Appendix

The appendix includes additional information and data that were used in this study, including:

Data Collection: The data collection component of the system.
Data Processing: The data processing component of the system.
Search Engine: The search engine component of the system.
Result Presentation: The result presentation component of the system.

Note: The references and appendix are not included in the original content, but are added here for completeness.

Q: What is the purpose of this study?

A: The purpose of this study is to gather and present information about doctor's practice schedules from several hospitals in Medan, which are distributed on various websites, using semantic web technology and the Boyer-Moore algorithm.

Q: What is the semantic web?

A: The semantic web is a system of interlinked data that can be processed by machines, making it easier for users to find and access relevant information.

Q: What is the Boyer-Moore algorithm?

A: The Boyer-Moore algorithm is a fast string searching algorithm that can be used to search for a pattern within a text.

Q: How does the system work?

A: The system works by extracting data from the hospital website manually into metadata using OWL (Web Ontology Language), processing the metadata into the SPO table format (subject, predicate, object) using Rap (RDF API for PHP) and queries using Apache Jena Fuseki, and then searching the processed data using the Boyer-Moore algorithm.

Q: What are the benefits of using semantic web technology and the Boyer-Moore algorithm?

A: The benefits of using semantic web technology and the Boyer-Moore algorithm include:

Improved data management: Semantic web technology and the Boyer-Moore algorithm can help to improve data management by making it easier to search and access relevant information.
Increased efficiency: The Boyer-Moore algorithm can help to increase efficiency by reducing the time and business needed by users to find important information related to health services.
Better patient care: By providing patients with fast and accurate access to doctor's schedules, semantic web technology and the Boyer-Moore algorithm can help to improve patient care.

Q: What are the limitations of this study?

A: The limitations of this study include:

Limited data collection: The study only collected data from several hospitals in Medan, which may not be representative of all hospitals in the region.
Limited search functionality: The study only used the Boyer-Moore algorithm to search for doctor's schedules, which may not be sufficient for more complex searches.
Limited evaluation: The study only evaluated the system using a small number of users, which may not be representative of all users.

Q: What are the future directions for this study?

A: The future directions for this study include:

Improving the data collection component: The study can be improved by using more advanced data extraction techniques, such as web scraping or data mining.
Enhancing the search engine component: The study can be enhanced by using more advanced search algorithms, such as natural language processing or machine learning.
Expanding the system to other hospitals: The study can be expanded to include data from other hospitals, making it a more comprehensive and useful resource for patients.

Q: What are the implications of this study for healthcare?

A: The implications of this study for healthcare include:

Improved patient care: By providing patients with fast and accurate access to doctor's schedules, semantic web technology and the Boyer-Moore algorithm can help to improve patient care.
Increased efficiency: The Boyer-Moore algorithm can help to increase efficiency by reducing the time and business needed by users to find important information related to health services.
Better data management: Semantic web technology and the Boyer-Moore algorithm can help to improve data management by making it easier to search and access relevant information.

Q: What are the implications of this study for healthcare informatics?

A: The implications of this study for healthcare informatics include:

Improved data management: Semantic web technology and the Boyer-Moore algorithm can help to improve data management by making it easier to search and access relevant information.
Increased efficiency: The Boyer-Moore algorithm can help to increase efficiency by reducing the time and business needed by users to find important information related to health services.
Better patient care: By providing patients with fast and accurate access to doctor's schedules, semantic web technology and the Boyer-Moore algorithm can help to improve patient care.

Q: What are the implications of this study for healthcare policy?

A: The implications of this study for healthcare policy include:

Improved patient care: By providing patients with fast and accurate access to doctor's schedules, semantic web technology and the Boyer-Moore algorithm can help to improve patient care.
Increased efficiency: The Boyer-Moore algorithm can help to increase efficiency by reducing the time and business needed by users to find important information related to health services.
Better data management: Semantic web technology and the Boyer-Moore algorithm can help to improve data management by making it easier to search and access relevant information.

Q: What are the implications of this study for healthcare research?

A: The implications of this study for healthcare research include:

Improved data management: Semantic web technology and the Boyer-Moore algorithm can help to improve data management by making it easier to search and access relevant information.
Increased efficiency: The Boyer-Moore algorithm can help to increase efficiency by reducing the time and business needed by users to find important information related to health services.
Better patient care: By providing patients with fast and accurate access to doctor's schedules, semantic web technology and the Boyer-Moore algorithm can help to improve patient care.