The Need for a Recommendation System

Oct 13, 2025 by talynt.org 61 views

Introduction

In the wake of the COVID-19 pandemic, many micro, small, and medium businesses (MSMEs) in Central Parsoburan Village, Habinsaran District, Toba Regency, North Sumatra Province, experienced a significant decline in income, particularly among the lower middle class. In an effort to alleviate the suffering of the community, the government launched a direct cash assistance program (BLT) for MSME actors. However, the limited budget and subjective selection process of BLT recipients led to a sense of injustice among citizens. Therefore, a recommendation system that can select recipients more objectively is urgently needed.

The Need for a Recommendation System

A recommendation system is a web-based personalization tool that can provide information on the list of recipients in accordance with certain criteria. In this study, the Weighted Product method was employed due to its ability to provide a different weight to each existing criterion, resulting in a more fair and transparent selection process.

Criteria Used in the Recommendation System

The following criteria were used in the recommendation system:

Income: This is the primary indicator of the financial capacity of the recipient.
Expenditures: This provides an overview of the costs borne by the SMEs.
Status of Residence: This determines whether the recipient lives in an area that is in need or not.
Value of Lifestyle: This describes a lifestyle that can affect financial needs.
Number of Family Members of Productive Age: This determines the potential of future generations in supporting family income.

Analysis and Results

From the analysis conducted, the most dominant criteria in this system are income and status of residence, which have the highest weight value. By using data from 10 residents, this system succeeded in filtering and producing 5 prioritized names to receive assistance.

Implementation and Benefits

The implementation of this recommendation system not only increases justice in the distribution of assistance but can also increase public confidence in the government. In addition, this approach provides more accurate data about the real needs of society, which can be the basis for future assistance policies. The use of technology in the distribution of this kind of assistance also indicates that the government is committed to improving the decision-making process that has a direct impact on the welfare of the community.

Conclusion

The recommendation system using the Weighted Product method can be the right solution to increase effectiveness and justice in the distribution of direct cash assistance (BLT) for MSMEs in Central Parsoburan Village. The implementation of this system is expected to be a model for other villages that face similar challenges, as well as increasing collaboration between the government and the community in restoring the post-Pandemic economy.

Future Directions

The use of this recommendation system can be expanded to other areas, including:

Integration with other systems: Integrating this system with other systems, such as the government's database, can provide more accurate and comprehensive data.
Use of machine learning: Using machine learning algorithms can improve the accuracy and efficiency of the system.
Expansion to other areas: Expanding this system to other areas can help to increase the effectiveness and justice in the distribution of direct cash assistance.

Recommendations

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

Implementation of the recommendation system: The government should implement the recommendation system in Central Parsoburan Village and other areas that face similar challenges.
Training and capacity building: The government should provide training and capacity building for the personnel involved in the implementation of the recommendation system.
Monitoring and evaluation: The government should monitor and evaluate the effectiveness of the recommendation system and make necessary adjustments.

Limitations

This study has several limitations, including:

Limited data: The study used data from only 10 residents, which may not be representative of the entire community.
Subjective criteria: The criteria used in the recommendation system may be subjective and may not accurately reflect the needs of the community.
Limited scope: The study only focused on Central Parsoburan Village and may not be applicable to other areas.

Future Research Directions

Future research directions include:

Expansion of the study: Expanding the study to other areas and using a larger sample size can provide more accurate and comprehensive data.
Use of machine learning: Using machine learning algorithms can improve the accuracy and efficiency of the system.
Integration with other systems: Integrating this system with other systems, such as the government's database, can provide more accurate and comprehensive data.
Frequently Asked Questions (FAQs) about the System for Cash Direct Assistance Recommendations (BLT) UMKM Kelurahan Parsoburan Tengah with the Weighted Product Method ===========================================================

Q: What is the purpose of the System for Cash Direct Assistance Recommendations (BLT) UMKM Kelurahan Parsoburan Tengah with the Weighted Product Method?

A: The purpose of this system is to provide a more objective and fair selection process for direct cash assistance (BLT) recipients among micro, small, and medium businesses (MSMEs) in Central Parsoburan Village, Habinsaran District, Toba Regency, North Sumatra Province.

Q: What are the criteria used in the recommendation system?

A: The criteria used in the recommendation system include:

Income: This is the primary indicator of the financial capacity of the recipient.
Expenditures: This provides an overview of the costs borne by the SMEs.
Status of Residence: This determines whether the recipient lives in an area that is in need or not.
Value of Lifestyle: This describes a lifestyle that can affect financial needs.
Number of Family Members of Productive Age: This determines the potential of future generations in supporting family income.

Q: How does the Weighted Product method work?

A: The Weighted Product method assigns a different weight to each existing criterion, resulting in a more fair and transparent selection process. The weights are determined based on the importance of each criterion.

Q: What are the benefits of using the recommendation system?

A: The benefits of using the recommendation system include:

Increased justice in the distribution of assistance: The system provides a more objective and fair selection process.
Increased public confidence in the government: The system demonstrates the government's commitment to improving the decision-making process.
More accurate data about the real needs of society: The system provides more accurate data about the needs of the community, which can be the basis for future assistance policies.

Q: How can the recommendation system be expanded?

A: The recommendation system can be expanded by:

Integrating with other systems: Integrating this system with other systems, such as the government's database, can provide more accurate and comprehensive data.
Use of machine learning: Using machine learning algorithms can improve the accuracy and efficiency of the system.
Expansion to other areas: Expanding this system to other areas can help to increase the effectiveness and justice in the distribution of direct cash assistance.

Q: What are the limitations of the study?

A: The study has several limitations, including:

Limited data: The study used data from only 10 residents, which may not be representative of the entire community.
Subjective criteria: The criteria used in the recommendation system may be subjective and may not accurately reflect the needs of the community.
Limited scope: The study only focused on Central Parsoburan Village and may not be applicable to other areas.

Q: What are the future research directions?

A: Future research directions include:

Expansion of the study: Expanding the study to other areas and using a larger sample size can provide more accurate and comprehensive data.
Use of machine learning: Using machine learning algorithms can improve the accuracy and efficiency of the system.
Integration with other systems: Integrating this system with other systems, such as the government's database, can provide more accurate and comprehensive data.

Q: How can the recommendation system be implemented in practice?

A: The recommendation system can be implemented in practice by:

Training and capacity building: Providing training and capacity building for the personnel involved in the implementation of the recommendation system.
Monitoring and evaluation: Monitoring and evaluating the effectiveness of the recommendation system and making necessary adjustments.
Integration with existing systems: Integrating the recommendation system with existing systems, such as the government's database, to provide more accurate and comprehensive data.