The Need for Indonesian Corpus Pornography

Oct 13, 2025 by wbrum.org 40 views

Building Indonesian Corpus Pornography: A Step Towards Filtering Negative Content

Introduction

In today's digital age, the internet has become a vast repository of information, with millions of websites and online platforms catering to diverse interests and needs. However, this vast expanse of digital content also poses a significant challenge in terms of filtering out negative and inappropriate information, particularly those related to pornography. Despite the importance of this issue, research has shown that there is a lack of Indonesian corpus specifically focused on pornography, which can hinder the process of content filtering and analysis.

The Need for Indonesian Corpus Pornography

Pornography can be defined as the depiction of the human body or sexual behavior aimed at arousing lust. While this topic may be considered sensitive or taboo in some cultures, it is an essential aspect of human behavior and society. The lack of Indonesian corpus on this topic is surprising, given the growing importance of technology and digital content in modern society. According to research by Gunarso et al. (2016), there is currently no Indonesian corpus that specifically addresses the theme of pornography. This corpus is crucial because it serves as a collection of text that has been arranged neatly and can be read by machines, facilitating the process of filtering negative content, especially those related to pornography.

The Importance of Corpus Pornography

Building the corpus of Indonesian-language pornography is not only essential for screening negative content but also for various other interests, such as social research, education, and information technology development. With this corpus, researchers and developers can more easily analyze sexual behavior trends in society and the impact of the media on the views of the community about sexuality. This knowledge can be used to develop effective strategies for promoting healthy attitudes towards sex and preventing the spread of misinformation.

The Development of Indonesian Corpus Pornography

In an effort to create a corpus of Indonesian-language pornography, this study aims to develop a system that can effectively screen negative content. This process is carried out by classifying content into two categories: pornography and non-pornography. To achieve this goal, the K-Nearest Neighbors (KNN) algorithm is used, which allows the analysis and categorization of text with adequate accuracy. In this study, the KNN algorithm showed the results of accuracy of 66.5%, with parameters K = 1, precision 75%, and F1-Score 67%.

Data Collection and Analysis

The data used in this study was taken from the trustpositive.kominfo.id.id site, which included 150 domains with a total of 1000 adult story articles. Data collection from trusted sources is essential so that the results of content analysis and classification have a higher validity. The data was then analyzed using the KNN algorithm, which works by identifying the closeness between the data being analyzed and existing training data.

The Role of Technology in Content Analysis

The use of the KNN algorithm in the classification of pornographic content provides an overview of how technology can be applied to understand text more deeply. KNN works by identifying the closeness between the data being analyzed and existing training data. Although the accuracy obtained from this study is not perfect, the results achieved have shown great potential for further development. This study demonstrates the effectiveness of using machine learning algorithms in content analysis and classification, which can be applied to various fields, including education, social research, and information technology development.

Conclusion

The development of Indonesian-language pornography corpus is an important step in an effort to filter negative content and prevent the dissemination of inappropriate information. With the support of appropriate technology, such as the KNN algorithm, we can be more effective in dealing with sensitive issues related to pornographic content. In addition, this research opens a way for further studies that can help understand and overcome various social challenges faced by modern society.

Future Directions

This study has shown the potential of using machine learning algorithms in content analysis and classification. Future studies can build upon this research by exploring the use of other algorithms, such as deep learning, in content analysis and classification. Additionally, further research can be conducted to improve the accuracy of the KNN algorithm and to develop more effective strategies for filtering negative content.

Limitations and Challenges

This study has several limitations and challenges. Firstly, the data used in this study was taken from a single source, which may not be representative of the broader population. Secondly, the accuracy of the KNN algorithm is not perfect, which may lead to errors in content classification. Finally, the development of Indonesian-language pornography corpus is a complex task that requires significant resources and expertise.

Recommendations

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

Further research: Further research is needed to improve the accuracy of the KNN algorithm and to develop more effective strategies for filtering negative content.
Data collection: Data collection from multiple sources is essential to ensure the validity and representativeness of the results.
Algorithm development: The development of new algorithms, such as deep learning, can improve the accuracy of content analysis and classification.
Collaboration: Collaboration between researchers, developers, and policymakers is essential to develop effective strategies for filtering negative content and promoting healthy attitudes towards sex.

References

Gunarso, R., et al. (2016). Indonesian Corpus of Pornography. Journal of Indonesian Language and Literature, 1(1), 1-10.

Appendices

Appendix A: Data Collection Methodology

Appendix B: KNN Algorithm Implementation

Appendix C: Results of Content Analysis and Classification
Frequently Asked Questions: Building Indonesian Corpus Pornography

Introduction

The development of Indonesian corpus pornography has been a topic of interest in recent years, particularly in the context of content filtering and analysis. However, there are still many questions and concerns surrounding this issue. In this article, we will address some of the most frequently asked questions related to building Indonesian corpus pornography.

Q1: What is the purpose of building Indonesian corpus pornography?

A1: The primary purpose of building Indonesian corpus pornography is to create a collection of text that can be used to filter out negative content, particularly those related to pornography. This corpus can be used to analyze and categorize text with adequate accuracy, making it easier to identify and remove inappropriate content from online platforms.

Q2: Why is it important to have an Indonesian corpus of pornography?

A2: Having an Indonesian corpus of pornography is essential because it allows researchers and developers to analyze and understand the trends and patterns of sexual behavior in Indonesian society. This knowledge can be used to develop effective strategies for promoting healthy attitudes towards sex and preventing the spread of misinformation.

Q3: What is the K-Nearest Neighbors (KNN) algorithm, and how is it used in content analysis?

A3: The KNN algorithm is a machine learning algorithm that works by identifying the closeness between the data being analyzed and existing training data. In the context of content analysis, the KNN algorithm is used to classify text into two categories: pornography and non-pornography. The algorithm has shown great potential in content analysis and classification, with an accuracy of 66.5% in this study.

Q4: What are the limitations and challenges of building Indonesian corpus pornography?

A4: Some of the limitations and challenges of building Indonesian corpus pornography include the need for significant resources and expertise, the potential for errors in content classification, and the difficulty of collecting data from multiple sources.

Q5: How can the accuracy of the KNN algorithm be improved?

A5: The accuracy of the KNN algorithm can be improved by using more advanced machine learning algorithms, such as deep learning, and by collecting more data from multiple sources. Additionally, the algorithm can be fine-tuned to improve its performance on specific tasks.

Q6: What are the potential applications of Indonesian corpus pornography?

A6: The potential applications of Indonesian corpus pornography are vast and varied. Some of the potential applications include:

Content filtering and analysis
Social research and analysis
Education and awareness-raising
Information technology development

Q7: How can the development of Indonesian corpus pornography be supported?

A7: The development of Indonesian corpus pornography can be supported by:

Providing funding and resources for research and development
Collaborating with researchers, developers, and policymakers
Sharing knowledge and expertise
Encouraging the use of machine learning algorithms in content analysis and classification

Q8: What are the potential risks and consequences of building Indonesian corpus pornography?

A8: Some of the potential risks and consequences of building Indonesian corpus pornography include:

The potential for errors in content classification
The potential for misuse of the corpus
The potential for negative impact on individuals and communities

Q9: How can the potential risks and consequences of building Indonesian corpus pornography be mitigated?

A9: The potential risks and consequences of building Indonesian corpus pornography can be mitigated by:

Ensuring that the corpus is developed and used responsibly
Implementing robust content classification and filtering systems
Providing education and awareness-raising about the potential risks and consequences of building Indonesian corpus pornography

Q10: What is the future direction of research on Indonesian corpus pornography?

A10: The future direction of research on Indonesian corpus pornography is likely to involve the development of more advanced machine learning algorithms, the collection of more data from multiple sources, and the exploration of new applications and uses of the corpus.

Conclusion

Building Indonesian corpus pornography is a complex and challenging task that requires significant resources and expertise. However, the potential benefits of this corpus, including improved content filtering and analysis, social research and analysis, education and awareness-raising, and information technology development, make it an important area of research and development. By addressing the frequently asked questions and concerns surrounding this issue, we hope to provide a better understanding of the importance and potential of Indonesian corpus pornography.