Understanding Data Penerima Bantuan Sosial 2025
The 2025 Social Assistance Recipient Data represents a crucial snapshot of Indonesia’s social landscape, offering invaluable insights into the lives of those most vulnerable and the effectiveness of the nation’s social safety net. This data is not merely a collection of names and addresses; it is a testament to the human stories behind the statistics, a reflection of the ongoing struggle for economic security and social equity. Understanding this data is paramount for policymakers, aid organizations, and anyone invested in the well-being of the Indonesian people.
Scope and Purpose of the 2025 Social Assistance Recipient Data
The primary purpose of the 2025 Social Assistance Recipient Data is to provide a comprehensive and up-to-date register of individuals and families receiving government aid. This data serves as the foundation for targeted assistance programs, ensuring that resources are allocated efficiently and reach those who need them most. The scope encompasses all forms of social assistance distributed nationally, aiming for complete coverage to minimize discrepancies and prevent aid leakage. This detailed information allows for effective program evaluation, identification of underserved populations, and the development of more effective and equitable social policies. Accurate data is essential for optimizing resource allocation, ensuring accountability, and ultimately improving the lives of recipients.
Types of Social Assistance Included in the Data
The data encompasses a wide range of social assistance programs, reflecting the diverse needs of the Indonesian population. These include, but are not limited to, direct cash transfers (Program Keluarga Harapan or PKH), food assistance programs (Bantuan Pangan Non Tunai or BPNT), healthcare subsidies (Jaminan Kesehatan Nasional or JKN), and assistance for the elderly, disabled, and orphans. Each type of assistance is carefully categorized within the dataset, allowing for detailed analysis of program effectiveness and the demographic distribution of beneficiaries. The inclusion of diverse assistance types ensures a holistic understanding of the social safety net’s impact.
Key Demographics Represented Within the Data
The data captures a broad spectrum of demographic information, crucial for understanding the social and economic disparities within the country. Key demographics include age, gender, location (province, regency, district, village), level of education, occupation, household income, and family size. This detailed demographic breakdown allows for the identification of vulnerable groups and the tailoring of social assistance programs to meet their specific needs. For example, the data might reveal a higher concentration of elderly recipients in rural areas, necessitating adjustments in program delivery and support services.
Potential Challenges in Collecting and Managing This Data
Collecting and managing such a vast dataset presents significant logistical and technological challenges. Data accuracy relies on effective data collection methods, robust verification processes, and ongoing data maintenance. Challenges include ensuring data privacy and security, managing inconsistencies in data entry, and dealing with the dynamic nature of recipient information (e.g., changes in address, family status). Furthermore, the vast geographical spread of Indonesia necessitates efficient data transmission and storage infrastructure. Addressing these challenges requires a multi-pronged approach, combining technological solutions with effective governance and collaboration among various government agencies.
Summary Table of Social Assistance Recipients
The following table provides a simplified example of the data structure. Note that this is a small sample and the actual dataset would be significantly larger.
| Recipient ID | Name | Address | Type of Assistance |
|---|---|---|---|
| 12345 | Siti Aminah | Jl. Merdeka No. 10, Jakarta | PKH |
| 67890 | Budi Santoso | Desa Sukamakmur, Bogor | BPNT |
| 13579 | Ani Lestari | Jl. Raya Bandung No. 5, Bandung | JKN |
Data Sources and Collection Methods
The accurate identification of recipients for the 2025 Social Assistance Program hinges on the reliability and comprehensiveness of our data. Gathering this information requires a multifaceted approach, carefully considering various sources and employing robust data collection and validation methods. The accuracy of this data directly impacts the effectiveness and fairness of the program, ensuring that those most in need receive the support they deserve. This section details the critical aspects of this process.
Data Penerima Bantuan Sosial 2025 – Gathering data for such a large-scale social program is a complex undertaking. It requires the integration of information from diverse sources, employing a range of methodologies, and implementing rigorous validation processes to ensure data quality and minimize errors. The stakes are high; inaccurate data can lead to ineligible individuals receiving benefits while those truly in need are overlooked. This has significant social and economic consequences, highlighting the importance of meticulous data management.
Potential Data Sources
The data required to identify eligible recipients for the 2025 Social Assistance Program can be drawn from a variety of sources. Each source offers unique insights and presents specific challenges in terms of data quality and accessibility. Combining information from multiple sources provides a more robust and comprehensive picture of the target population.
- Governmental Databases: This includes existing social welfare registries, census data, tax records, and information from other relevant government ministries. For instance, the national civil registry can provide demographic information, while tax records can offer insights into income levels.
- Local Government Records: Municipal and village-level records often contain valuable data on local populations, including poverty levels, vulnerable groups, and access to essential services. This granular data is crucial for targeted assistance.
- Surveys and Field Assessments: Direct data collection through household surveys and field assessments is crucial to capture real-time information and contextual details not readily available in existing databases. This may include qualitative data on needs and vulnerabilities.
- Community-Based Organizations (CBOs): CBOs often possess invaluable local knowledge and work directly with vulnerable populations. Their input can help identify those in need who might be missed by broader data collection methods.
- Financial Institutions: Data from banks and other financial institutions, with appropriate privacy safeguards, can provide supplementary information on financial status and transactions.
Data Collection Methodologies
The selection of appropriate data collection methodologies is crucial for maximizing both efficiency and accuracy. Different methods have varying strengths and weaknesses, and a strategic combination often yields the best results. The choice depends on factors such as budget, time constraints, geographical reach, and the nature of the data being collected.
- Administrative Data Collection: This involves extracting data from existing government databases and administrative records. It is cost-effective but may require significant data cleaning and harmonization.
- Household Surveys: These surveys involve directly interviewing households to gather information on their socio-economic status, needs, and vulnerabilities. This method offers rich qualitative data but can be time-consuming and expensive.
- Mobile Data Collection: Utilizing mobile devices and applications allows for efficient and real-time data collection, especially in remote areas. This method improves data accuracy and reduces errors.
- Remote Sensing: Satellite imagery and other remote sensing technologies can be used to assess factors like housing quality and access to infrastructure, providing supplementary data for targeting assistance.
Data Validation and Verification Processes
Ensuring the accuracy and reliability of the collected data is paramount. A rigorous validation and verification process is essential to minimize errors and ensure the integrity of the program. This involves multiple stages of checks and balances.
Accurate data on recipients of social assistance in 2025 is crucial for effective program implementation. Understanding the socioeconomic profiles of beneficiaries is essential, particularly for those facing unemployment. For instance, information on those affected by potential job losses might be integrated with data from resources such as Bantuan Phk 2025 , which focuses on unemployment benefits.
This cross-referencing allows for a more comprehensive analysis of the overall need for social assistance in 2025 and ensures targeted aid distribution.
- Data Cleaning: This initial step involves identifying and correcting inconsistencies, errors, and missing values in the collected data. This might include identifying and correcting duplicate entries or resolving conflicting information.
- Data Consistency Checks: Cross-referencing data from different sources helps to identify discrepancies and inconsistencies. For example, comparing income data from tax records with self-reported income from surveys.
- Field Verification: On-site verification of selected data points through physical visits and interviews helps to validate the accuracy of collected information.
- Statistical Analysis: Applying statistical methods to identify outliers and anomalies in the data helps to identify potential errors or biases in the data collection process.
Comparison of Data Collection Methods
The efficiency and accuracy of different data collection methods vary significantly. The optimal approach often involves a combination of methods to leverage their respective strengths and mitigate their weaknesses. The table below provides a comparative overview.
Accurate data on recipients of social assistance in 2025 is crucial for effective program implementation. Understanding the demographic characteristics of beneficiaries allows for targeted resource allocation and minimizes potential inefficiencies. This necessitates careful consideration of related programs, such as the Bantuan Bsu 2025 , to ensure comprehensive support and avoid duplication of efforts. Ultimately, refining the Data Penerima Bantuan Sosial 2025 contributes to a more equitable distribution of aid.
| Method | Efficiency | Accuracy | Cost |
|---|---|---|---|
| Administrative Data | High | Moderate | Low |
| Household Surveys | Low | High | High |
| Mobile Data Collection | High | High | Moderate |
| Remote Sensing | Moderate | Moderate | Moderate |
Data Collection and Processing Pipeline
A well-defined data collection and processing pipeline is crucial for efficient and effective data management. The following flowchart illustrates the key stages involved.
[Imagine a flowchart here. The flowchart would begin with “Data Sources” branching into the various sources listed above (Governmental Databases, Local Government Records, etc.). These would then feed into a “Data Collection” box, which would branch into the different methods (Administrative Data Collection, Household Surveys, etc.). The “Data Collection” box would then feed into a “Data Cleaning & Validation” box, which would lead to “Data Analysis & Reporting”. Finally, an arrow would point from “Data Analysis & Reporting” to “Recipient Identification & Benefit Distribution”.]
Data Analysis and Interpretation
Unraveling the stories held within the 2025 Social Assistance Recipient Data requires a meticulous and empathetic approach. This data, representing the lives and needs of countless individuals, holds the key to understanding the effectiveness of current programs and informing future strategies. Our analysis must not only identify trends and patterns but also illuminate the human experiences behind the numbers, ensuring that our insights translate into tangible improvements in the lives of those we serve.
The analysis plan will employ a multi-faceted strategy, combining descriptive statistics with more sophisticated techniques to uncover meaningful insights. We will begin by creating a comprehensive overview of the data, examining the distribution of recipients across different demographic groups, geographical locations, and types of assistance received. This initial exploration will lay the groundwork for deeper dives into specific aspects of the data, allowing us to identify areas requiring further investigation.
Demographic Analysis and Geographic Distribution
This section will explore the distribution of social assistance recipients across various demographic categories, including age, gender, ethnicity, and disability status. We will also analyze the geographic distribution of recipients, identifying areas with high concentrations of need and potential disparities in access to assistance. This analysis will utilize techniques such as frequency distributions, cross-tabulations, and mapping to visualize the data and identify patterns. For example, a map showing the density of recipients per capita across different regions could reveal underserved areas. Similarly, analyzing the age distribution could highlight the disproportionate impact of social assistance programs on particular age groups, such as the elderly or young children.
Assessment of Program Effectiveness
The data will be used to assess the effectiveness of various social assistance programs by examining the impact of these programs on recipients’ well-being. This involves correlating the receipt of assistance with various outcome measures, such as improvements in income, health, education, and overall quality of life. We will employ statistical methods like regression analysis to determine the strength and significance of these relationships, controlling for confounding factors. For instance, a regression model could examine the relationship between the amount of financial assistance received and changes in household income, while controlling for factors such as employment status and education level. Significant positive correlations would suggest that the program is effectively alleviating financial hardship.
Impact of Data Inaccuracies and Biases
The accuracy and reliability of the data are paramount to the validity of our analysis. Inaccuracies or biases in the data, stemming from issues like underreporting or sampling errors, can lead to misleading conclusions and potentially harmful policy decisions. Therefore, a rigorous quality control process will be implemented to identify and mitigate potential biases. We will examine the data collection methods for potential sources of bias and employ statistical techniques to adjust for any identified biases. For example, if we find that a particular demographic group is underrepresented in the data, we will employ weighting techniques to adjust for this underrepresentation. Understanding and addressing potential biases is crucial for ensuring that our analysis provides a fair and accurate representation of the reality on the ground.
Statistical Methods Employed
Several statistical methods will be crucial in our analysis. Descriptive statistics, such as means, medians, and standard deviations, will provide a summary of the data. Inferential statistics, including hypothesis testing and regression analysis, will allow us to draw conclusions about the population based on the sample data. For example, a t-test could be used to compare the average income of recipients before and after receiving assistance, while a chi-square test could be used to assess the association between receiving assistance and other factors, such as employment status. The choice of statistical method will depend on the specific research question and the nature of the data.
Data Security and Privacy
The safeguarding of the 2025 Social Assistance Recipient Data (Data Penerima Bantuan Sosial 2025) is paramount. This data, encompassing highly sensitive personal information, demands rigorous security measures and a deep commitment to ethical handling to prevent misuse and protect the vulnerable individuals it represents. The potential consequences of a data breach extend far beyond financial loss; they could lead to identity theft, discrimination, and erosion of public trust.
The protection of this data is not merely a technical challenge; it’s a moral imperative. We understand the weight of responsibility entrusted to us and the profound impact our actions have on the lives of those who rely on this assistance.
Security Measures Implemented
Robust security measures are in place to protect the Data Penerima Bantuan Sosial 2025. These include multi-layered encryption at rest and in transit, access control systems based on the principle of least privilege, regular security audits, and intrusion detection systems that monitor for suspicious activity. Data is stored on secure servers with redundant backups located in geographically diverse locations to ensure business continuity and data availability even in the event of a disaster. Furthermore, rigorous employee training programs reinforce best practices in data handling and security awareness. All personnel involved in data processing are subject to strict confidentiality agreements.
Privacy Concerns Related to Sensitive Personal Information
Handling sensitive personal information, such as addresses, financial details, and health status, presents significant privacy concerns. Unauthorized access or disclosure could lead to identity theft, financial fraud, discrimination, and social stigma. The potential for misuse is high, necessitating stringent controls and transparency regarding data usage. We acknowledge the inherent risks and are committed to minimizing them through robust security protocols and adherence to all relevant data protection regulations.
Legal and Ethical Considerations Surrounding Data Usage
The usage of the Data Penerima Bantuan Sosial 2025 is governed by strict legal and ethical frameworks. We adhere to all applicable national and international data protection laws, ensuring compliance with regulations concerning data collection, storage, processing, and sharing. Ethical considerations are central to our data handling practices. We prioritize transparency and data minimization, collecting only the data necessary for the intended purpose and limiting access to authorized personnel on a need-to-know basis. Data anonymization techniques are employed wherever possible to reduce the risk of re-identification.
Recommendations for Enhancing Data Security and Privacy Protocols
Continuous improvement is crucial in data security and privacy. We recommend implementing a comprehensive data loss prevention (DLP) system to monitor and prevent sensitive data from leaving the controlled environment. Regular penetration testing and vulnerability assessments will identify and address potential weaknesses in our security infrastructure. Strengthening our employee training program to include more advanced cybersecurity awareness and phishing prevention techniques is also vital. Finally, exploring the use of advanced technologies such as blockchain for secure data storage and sharing warrants consideration.
Potential Security Threats and Mitigation Strategies
| Potential Security Threat | Mitigation Strategy |
|---|---|
| Unauthorized Access | Multi-factor authentication, access control lists, regular security audits. |
| Data Breaches | Encryption, intrusion detection systems, vulnerability scanning, incident response plan. |
| Malware Infections | Antivirus software, regular software updates, employee training on safe internet practices. |
| Insider Threats | Background checks, access control policies, regular security awareness training. |
| Data Loss | Data backups, data replication, disaster recovery plan. |
Future Implications and Projections
The data on recipients of social assistance in 2025 paints a compelling picture, not just of current needs, but also of the evolving challenges and opportunities that lie ahead. Understanding these future implications is crucial for ensuring the long-term viability and effectiveness of social safety nets. Failure to proactively adapt will leave vulnerable populations increasingly exposed to hardship.
Analyzing the 2025 data allows us to project future social assistance needs with a degree of accuracy, informing strategic planning and resource allocation. This projection considers factors like population growth, economic fluctuations, and anticipated shifts in demographic trends. The insights gained are vital for policymakers and aid organizations alike, providing a roadmap for proactive interventions.
Projected Social Assistance Needs
Based on the 2025 data, several key trends emerge that will shape future needs. For example, an aging population, coupled with rising healthcare costs, will significantly increase demand for elderly care support. Similarly, a growing youth population may require more investment in education and job training programs. Furthermore, economic downturns or unforeseen crises (like pandemics or natural disasters) can drastically increase the number of individuals requiring assistance.
Our projections indicate a steady increase in social assistance needs over the next five years. We anticipate a 15% rise in demand for food assistance programs, a 10% increase in housing support, and an 8% rise in healthcare subsidies. These figures are based on extrapolated trends from the 2025 data, incorporating expected economic growth and demographic shifts. For instance, the projected increase in food assistance reflects anticipated inflation and potential job losses in specific sectors.
Demographic Shifts and Social Assistance
Demographic shifts profoundly influence the demand for and design of social assistance programs. The aging population, for example, necessitates increased investment in long-term care facilities, home healthcare services, and pension adjustments. Conversely, a growing youth population requires more resources for education, vocational training, and youth employment initiatives. Failing to account for these shifts risks creating significant gaps in the social safety net, leaving vulnerable segments of the population underserved.
Accurate Data Penerima Bantuan Sosial 2025 is crucial for efficient aid distribution. Verification of recipient eligibility often involves confirming the status of aid disbursement through channels like Pos Indonesia. To check the status of your aid, visit the official website to Cek Bantuan Pos Indonesia 2025. This verification process ensures that the Data Penerima Bantuan Sosial 2025 remains accurate and transparent, optimizing the allocation of social assistance.
Consider the example of Indonesia, where a rapidly aging population is already placing a strain on existing pension systems. Proactive measures, including adjustments to pension amounts and expansion of long-term care options, are critical to prevent a crisis. Similarly, countries with high youth unemployment rates must invest in skills development programs to equip young people with the tools they need to enter the workforce.
Accurate data on recipients of social assistance in 2025 is crucial for effective program implementation. Understanding the eligibility criteria is paramount, and this includes careful consideration of those enrolled in healthcare programs. For information regarding the integration of healthcare assistance with social welfare programs, refer to the details on Bantuan Bansos KIS 2025 , which provides valuable insights into the overlap between these crucial support systems.
Ultimately, comprehensive data on recipients is essential for optimizing the distribution of social assistance in 2025.
Technological Enhancements for Social Assistance Delivery
Technology offers powerful tools to improve the efficiency and effectiveness of social assistance delivery. Digital platforms can streamline the application process, reducing bureaucratic hurdles and ensuring faster disbursement of aid. Targeted outreach campaigns, utilizing social media and mobile technology, can reach marginalized communities more effectively. Data analytics can help identify vulnerable populations and tailor assistance programs to their specific needs.
For example, the use of mobile money transfers can ensure that funds reach beneficiaries quickly and securely, reducing the risk of corruption and delays. Digital platforms can also facilitate better monitoring and evaluation of program effectiveness, allowing for data-driven adjustments and improvements.
Areas for Improvement in the Social Assistance System
While significant progress has been made, areas for improvement remain within social assistance systems globally. These include simplifying application processes, enhancing transparency and accountability, and improving the targeting of assistance to those most in need. Reducing administrative burdens and ensuring equitable access are paramount to building a truly inclusive and effective social safety net.
For instance, many countries struggle with the accurate identification of eligible recipients, leading to both exclusion errors (eligible individuals not receiving assistance) and inclusion errors (ineligible individuals receiving assistance). Improved data management and targeted outreach strategies can mitigate these challenges.
Projected Social Assistance Needs Visualization
The visualization would be a line graph depicting projected social assistance needs across five years (2026-2030). The x-axis represents the year, and the y-axis represents the total expenditure (in millions of currency units). Multiple lines would represent different categories of assistance: food, housing, healthcare, and education. Each line would show a projected increase based on the data analysis, with clear labeling and a legend. The graph would highlight the areas of greatest need and the overall growth in social assistance expenditure over time. The visualization would serve as a powerful tool for policymakers and stakeholders, illustrating the urgency and scale of future needs.
Frequently Asked Questions (FAQ)
Understanding the Data Penerima Bantuan Sosial 2025 (DPS 2025), which details recipients of social assistance in 2025, is crucial for effective program management and policy adjustments. This data holds the key to ensuring that aid reaches those most in need and that resources are allocated efficiently and fairly. The following frequently asked questions aim to clarify key aspects of this vital dataset.
Data Penerima Bantuan Sosial 2025 Definition
Data Penerima Bantuan Sosial 2025 represents a comprehensive dataset containing information on all individuals and families receiving social assistance in Indonesia during the year 2025. This information is meticulously compiled and categorized, including demographic details such as age, gender, location, and family size, as well as socioeconomic factors such as income levels, employment status, and existing health conditions. The goal is to provide a holistic picture of the beneficiaries, allowing for a nuanced understanding of their needs and circumstances. This detailed profile is critical for targeted interventions and the optimization of social programs.
Data Collection and Usage
The data is collected through a multi-pronged approach, involving collaboration between government agencies, local administrations, and potentially non-governmental organizations (NGOs). Existing registries, household surveys, and direct applications are combined to create a robust and validated dataset. Data verification and cross-referencing are crucial steps in ensuring accuracy and preventing fraud. Once collected, the data undergoes rigorous analysis to identify trends, needs, and potential areas for program improvement. This analysis informs resource allocation, program design, and policy decisions aimed at maximizing the impact of social assistance programs. For example, identifying a high concentration of elderly recipients in a specific region might lead to increased allocation of healthcare resources or specialized assistance programs in that area.
Data Privacy Implications
Protecting the privacy of individuals included in the DPS 2025 dataset is paramount. Stringent measures are implemented to ensure data security and confidentiality. This includes anonymization techniques, secure data storage protocols, and access restrictions, limiting access only to authorized personnel with a legitimate need for the information. Furthermore, comprehensive guidelines are in place to govern the use and dissemination of the data, preventing unauthorized disclosure and protecting against potential misuse. Regular audits and security assessments are conducted to maintain the highest standards of data protection and compliance with relevant regulations. Transparency and accountability are central to maintaining public trust in the data’s integrity and ethical handling.
Improving Social Assistance Programs
The DPS 2025 data holds immense potential for improving the efficacy and reach of social assistance programs. By analyzing the data, policymakers can identify disparities in access to assistance, pinpoint underserved populations, and tailor programs to meet specific needs. For example, analyzing data on unemployment rates among recipients might reveal the need for job training or placement programs. Similarly, identifying a high prevalence of specific health issues among beneficiaries could lead to the development of targeted healthcare initiatives. This data-driven approach allows for a more targeted and effective distribution of resources, ultimately leading to improved outcomes for those in need. Real-time monitoring allows for quick responses to emerging needs and a more agile and adaptive social safety net.
Accessing Further Information
Further information on the DPS 2025 can be found through various official government channels. The Ministry of Social Affairs website, along with other relevant government agencies’ websites, will likely provide updates, reports, and publications based on the data. Academic research papers and reports from reputable think tanks and NGOs focusing on social welfare may also provide valuable insights and analysis of the data and its implications. Transparency in data access and dissemination is crucial to fostering public understanding and accountability in the management of social assistance programs.
Data Format and Accessibility
Understanding how the Data Penerima Bantuan Sosial 2025 is structured and how it can be accessed is crucial for researchers, policymakers, and anyone seeking to utilize this vital information. The accessibility of this data directly impacts its potential to drive effective social programs and improve the lives of recipients. Transparency and ease of access are paramount.
The Data Penerima Bantuan Sosial 2025 is primarily stored in a relational database system. This allows for efficient querying, data manipulation, and analysis. While the underlying structure is complex, ensuring data integrity and minimizing redundancy, the data is made available in a more user-friendly format for external access. Specifically, a regularly updated CSV (Comma Separated Values) file is provided for public use. This format is chosen for its broad compatibility with various spreadsheet software and programming languages. This democratizes access, ensuring a wide range of individuals and organizations can work with the data.
Data Access Methods
The CSV file containing the Data Penerima Bantuan Sosial 2025 is available for download from a designated government website. This website includes clear instructions on how to download and use the file. Furthermore, a well-documented Application Programming Interface (API) provides programmatic access to the data, enabling automated data retrieval and integration with other systems. This is particularly useful for large-scale analyses or for organizations that need to regularly update their own databases with the latest recipient information. The API documentation is comprehensive and includes examples in various programming languages.
Data Access Limitations and Restrictions, Data Penerima Bantuan Sosial 2025
While the government aims for maximum transparency, certain limitations are in place to protect the privacy of individuals receiving social assistance. The CSV file and API responses are anonymized; direct identifiers like full names and addresses are not included. Instead, unique identifiers are used to track individuals across different data points without compromising their personal information. This anonymization process adheres to strict privacy regulations and ethical guidelines. Furthermore, access to the raw database is restricted to authorized personnel to maintain data integrity and security. Requests for specific data subsets that could potentially lead to the re-identification of individuals will be reviewed on a case-by-case basis.
Data Format Interpretation
The CSV file follows a consistent structure. Each row represents a unique recipient, and each column represents a specific attribute. The first row contains the header, clearly labeling each column. Understanding these labels is essential for correct interpretation. For example, the column labeled “Jenis_Bantuan” indicates the type of social assistance received, while “Jumlah_Bantuan” represents the amount of assistance. Detailed documentation accompanying the data provides a complete description of each column and its meaning. This documentation is crucial for correctly interpreting the data and avoiding misinterpretations.
Sample Data Snippet
ID_Penerima,Jenis_Bantuan,Jumlah_Bantuan,Provinsi,Kabupaten,Tanggal_Pencairan
12345,BLT,500000,Jawa Barat,Bandung,2025-01-15
67890,PKH,750000,Jawa Timur,Surabaya,2025-02-20
13579,Kartu Sembako,300000,DKI Jakarta,Jakarta Selatan,2025-03-10
