Optimizing Data Collection Strategies for Effective Poverty Alleviation: Insights from the Social Welfare System - Next Generation (SIKS-NG)

Malang Regency is the second-largest region in East Java with a diversity of geographical, social, and economic conditions. Data from the Malang Regency Social Service in 2018 shows that the number of impoverished families in Malang Regency reached 230,081 families. The highest number of impoverished families is found in the Dampit District, totaling 12,053 families, while the lowest is in the Kromengan District with 3,061 families. This indicates that the Dampit District has a significant number of impoverished families, surpassing other districts in Malang Regency. This makes the Dampit District a relevant location for research on poverty and its impacts. This research has dual objectives: first, to provide reliable, accurate, and valid information on existing poverty indicators, which will serve as a strong foundation for supporting poverty alleviation programs in Malang Regency. Second, this research aims to formulate strategies in the form of activity guidelines that will enhance the competence of employees in conducting poverty data collection, ultimately supporting poverty alleviation efforts. Data collection methods include interviews, documentation, observation, and Focus Group Discussions to obtain more in-depth information. In-depth interviews were conducted using purposive sampling techniques involving stakeholders related to poverty alleviation in the Dampit District Office, Malang Regency. The analysis results indicate that the government of the Dampit District and the villages in the area have not fully succeeded in implementing the Next Generation Social Welfare System (SIKS-NG) optimally. The poverty data collection has predominantly focused on collecting data on the poor only. Therefore, improvements in the approach and implementation of SIKS-NG are needed to enhance the effectiveness of poverty alleviation programs. The findings of this research highlight the importance of a participatory approach in collecting poverty data, involving village governments, utilizing information infrastructure like BDT, and the data consolidation process to ensure the effectiveness of poverty alleviation programs. Additionally, appropriate training for data collection teams is crucial in improving the accuracy of poverty data. This is an open access article under the CC BY-SA license.


INTRODUCTION
Poverty is a multidimensional and multisectoral issue with diverse characteristics.It is not solely about the lack of income but also involves other aspects such as access to quality education, affordable healthcare, adequate housing, decent employment, a nd participation in policy and development processes.Poverty encompasses issues related to food, clothing, human resource quality, employment opportunities, future prospects, clean water and sanitation, powerlessness, representation and freedom, among oth ers (Charlier & Legendre, 2021;Oster, 2019;Primadianti & Sugiyanto, 2020).
Indonesia is one of the developing countries facing an undeniable poverty issue.The poverty rate among its population seems to remain persistent each year.Poverty is a humanitarian concern that requires highly integrated efforts to overcome (Sari, 2021).According to the World Bank, poverty standards are based on per capita income (Leonita & Sari, 2019).Individuals with a per capita income less than one-third of the national per capita income are categorized as poor.In this context, the poverty threshold, as defined by the World Bank, is USD$2 per person per day.Poverty has numerous negative consequences.Alongside the emergence of social problems, poverty can also affect a country's economic development (Utami, 2020).High levels of poverty lead to increased costs for economic development efforts (Novriansyah, 2018), indirectly hindering economic progress.Below is a table of the 10 provinces in Indonesia with the highest number of impoverished residents.
Based on data from the Central Statistics Agency (BPS) in 2023, the provinces on the Java island consecutively have the highest number of poor population (BPS, 2023).The first position is held by East Java with 4.236.510poor individuals.If the population of East Java in 2022 was 41 .150.000, it means that the percentage of its poor population is approximately 10.49%.The second province with the largest number of poor individuals is West Java with 4.053.620individuals, which accounts for 7,98% of the total population.Following that is Central Java with 3.858.230individuals or 10,98%.Next in line are North Sumatra with 1.262.190individuals or 8,33%, South Sumatra with 1.054.990individuals or 11,95%, and Lampung Province with 995.590 individuals or 11.44%.
The high poverty rate has made poverty a central focus in every development effort (Anggraeni, 2022).Although recent data shows a decrease in the percentage of the population living in poverty in East Java, this does not necessarily mean that the well-being in East Java has improved.Nevertheless, the reduction in the number of poor people should be taken seriously because there is a potential risk of falling back into poverty, especially given the global pressures that require intellectual competitiveness.According to BPS data, Malang R egency ranks first with 252.88 thousand people living in impoverished conditions.Even more so, the number of poor people in Malang Regency is above 250 thousand individuals.The per capita income in Malang Regency in 2022 reached Rp107.036.4 million.This is followed by Jember Regency with 232.73 thousand individuals, Sampang Regency with 217.97 thousand individuals, Sumenep Regency with 206.20 thousand individuals, and Probolinggo Regency with 203.23 thousand individuals facing similar challenges in terms of their population's poverty (BPS Malang, 2022).The larger the poverty gap, the farther the income of the poor population is from the poverty line, indicating that they may face greater difficulties in meeting their basic needs (Amar et al., 2020).Thirdly, Income Distribution Among the Poor, income distribution refers to how income is generated within society.Examining income distribution among the poor is crucial because it reflects the level of economic inequality within the population group below the poverty line.If income is unevenly distributed among the poor population, it could mean that the majority of the poor receive little benefit from the existing economic growth (Khaerunnisa, 2019;World Bank, 2021).
Malang Regency, located in East Java Province, is one of the largest regencies, second only to Banyuwangi Regency, in terms of land area.The area of Malang Regency consists of both land and sea, with respective areas of 2,977.05square km and 557.81 square km.The regency holds diverse potential, particularly in agriculture, fisheries, tourism, plantations, and other sectors.Administratively, Malang Regency is divided into 33 subdistricts, with 12 urban districts (Kelurahan) and 378 rural villages (Desa).Each subdistrict has its unique characteristics and regional challenges (BPS Malang, 2022).One of the subdistricts in Malang Regency facing significant challenges related to rural poverty rates is Dampit Subdistrict.According to data from the Central Statistics Agency (BPS) in 2016, the poverty rate in this subdistrict reached 16.88 percent, surpassing the national rural poverty rate of 14.22 percent.This reflects the economic difficulties encountered by a considerable portion of the rural population in this area.Furthermore, the Human Development Index (HDI) of Dampit Subdistrict also shows a notable difference compared to the national HDI.With an HDI of 72.18, Dampit Subdistrict falls below the national HDI of 72.77.This indicates challenges in achieving social and economic development on par with other regions in Indonesia.The increase in the poverty rate in Dampit Subdistrict is also a concern, particularly with the rise in the expenditure group of Rp150,000 -Rp199,000 from 1.74% to 2.32% in 2016.This suggests that some local residents face difficulties in meeting their basic needs.
In an effort to address these issues, the government has prioritized education, healthcare, and poverty alleviation in the National Medium -Term Development Plan (RPJMN) for the years 2010 -2024.Improving accessibility and the quality of healthcare and education services, especially for the poor, is one of the key agendas in enhancing the quality of life for the population in Dampit Subdistrict.This approach allows for more targeted assessments of poverty and the design of assistance programs that align with the characteristics of the local population (Zaini et al., 2018).In the context of regional development in East Java, efforts to improve accessibility and the quality of healthcare and education services remain a primary focus, particularly for areas like Dampit Subdistrict.This is a necessary step to improve the economic, social, and living conditions of the community in the subdistrict, with the hope that poverty rates will decrease and the Human Development Index (HDI) will rise in the future.
Government officials must be capable of adapting to ongoing changes and even initiating the necessary changes to enhance the effectiveness of public services and achieve development goals (Syahbana & Indriaty, 2021).The launch of poverty assistance programs is one way to address a nd reduce poverty rates, but it consistently presents challenges due to the invalidity of population data.The validity of poverty data is a serious issue in the implementation of poverty assistance programs (Rank et al., 2021).Inaccurate or invalid data can lead to resource allocation injustices and may potentially trigger protests from individuals who feel they are not receiving the benefits they are entitled to.
Some common challenges faced in poverty data collection include first, the difficulty in determining poverty criteria to define and measure poverty, as each community or region may have different criteria.Establishing the right criteria for data collection can be a challenge.Second, technical incapacity in data collection in some areas due to inadequate technical capabilities for accurate data collection.This may include a lack of technological devices and infrastructure, as well as limitations in knowledge and data collection skills.Third, difficulties in accessing remote areas, making it hard to reach households that may require assistance.This can affect the accuracy of data.Additionally, the perceived lack of honesty in providing information about their living conditions by the community can make it challenging for enumerators to collect data .
To address the issue of poverty data invalidity and enhance community participation in the entire data collection process, a very wise approach that can improve the effectiveness of poverty alleviation programs is needed.Some additional steps that can be taken to implement this approach include first, adopting a participatory approach in the planning phase before conducting data collection by holding forums with the local community to ensure that the data collection and resulting programs are more aligned with the community's needs and realities.Second, providing training to the community on the importance of data collection, the criteria used, and their role in this process.Third, ensuring that the data collection process and the use of data are transp arent.The community should know how the data will be used and have access to the results of data collection.Also, ensure that there are mechanisms to address complaints or concerns from the community regarding the data collection process.Fourth, collaborating with independent institutions or NGOs can help ensure data accuracy and provide an objective view of the data collection process.Continuous monitoring and evaluation are also essential to ensure that the data and programs achieve the intended goals and benefits for the communities in need.
With the priority development strategy of reducing poverty and improving the quality of data collection services by the government apparatus in Malang Regency, it is essential in the effort to alleviate poverty in Malang Regency and achieve the priority development strategy.A holistic approach, encompassing training, technology, communication, and robust oversight, can enhance the quality of data collection services provided by the government apparatus in Malang Regency, making them more effective in poverty alleviation efforts in line with established development strategies.Given these conditions, a study on improving the quality of data collection services by government personnel in poverty alleviation is necessary.Thus, the policy for preparing human resources by the government apparatus with performance will be related to the type and nature of development to be carried out to meet the needs of qualified personnel (Gusak, 2020).
The research aims to achieve two main goals: first, to identify reliable poverty indicators that can effectively support poverty alleviation programs in Malang Regency, and second, to develop strategies to enhance the skills of government officials in poverty data collection to facilitate these programs.The expected outcomes include action plans to improve officials' data collection skills and policy recommendations for the consistent implementation of poverty alleviation initiatives.This study specifically focuses on the Dampit District in Malang Regency.

METHOD
The research design for the Study of Improving the Quality of Data Collection Services by Government Apparatus in Poverty Alleviation combines both qualitative and quantitative approaches in descriptive research.The chosen approach is qualitative, emphasizing the meaning and actions derived from the experiences of a group of individuals and social behaviors, particularly the decision -makers' actions (Mohajan, 2020).The research is conducted in the Dampit Subdistrict of Malang Regency.Data collection methods encompass interviews, documentation, observation, and potentially Focus Group Discussions (FGD) to gather in-depth information.
The selection of respondents was a deliberate process aimed at gathering diverse and relevant perspectives.In-depth interviews were conducted to collect valuable insights.The respondents were carefully chosen based on their direct involvement and expertise in matters related to poverty alleviation within the Dampit Optimizing Data Collection Strategies for Effective Poverty Alleviation: Insights from the Social Welfare System -Next Generation (SIKS-NG) (Nur Chamidah) 131 District of Malang Regency.The rationale for selecting these specific individuals and groups as respondents is as follows: 1) Village Chiefs: Village Chiefs were included in the study due to their pivotal roles in local governance and community leadership.Their insights provided a valuable perspective on the grassroots level and the challenges faced by impoverished families within their respective villages.2) Village Poverty Alleviation Coordination Team: This team comprises individuals tasked with coordinating and implementing poverty alleviation programs at the village level.Their inclusion allowed for an in -depth exploration of the strategies and initiatives aimed at reducing poverty within the district.3) District Head (Camat): The District Head, or Camat, holds a central position in local government and plays a key role in shaping policies and programs related to poverty reduction.I nterviews with the District Head provided insights into the broader governmental perspective on poverty alleviation efforts.4) Non-Governmental Organizations (NGOs): Non-Governmental Organizations actively involved in poverty alleviation initiatives were also among the chosen respondents.Their participation was crucial to understanding the role of civil society and NGOs in complementing government efforts and addressing the multifaceted challenges of poverty.
By interviewing a diverse set of stakeholders representing both government and non-government entities, this qualitative approach aimed to capture a comprehensive range of perspectives and experiences related to poverty alleviation efforts in the Dampit District of Malang Regency.
Data analysis employs both qualitative and quantitative techniques, presented through tables and graphs, and described concerning the data's conditions and fundamental concepts related to the study.
In qualitative research, data analysis is an ongoing two-stage process, starting with data reduction, where raw data is selected, sorted, grouped, and simplified (Rohman et al., 2021).Data presentation involves systematically arranging the information according to predetermined focal points and sub-focus areas, facilitating analysis and interpretation.Conclusion drawing and verification are part of configuring the complete dataset, including interpreting events/phenomena, establishing interrelationships between events, comparing, connecting, and contrasting various events to obtain a comprehensive, complete, and detailed understanding.

The Poverty Indicators in Supporting Poverty Alleviation Programs in Dampit Subdistrict
Dampit is a subdistrict located in the Malang Regency, East Java Province, Indonesia.It is one of the 33 subdistricts in Malang Regency.Dampit boasts an intriguing geographical location, situated approximately 36 km to the southeast of the city of Malang.The subdistrict shares borders with several neighboring subdistricts and villages, such as Sumbermanjing Wetan, Tirtoyudo, Sumbermanjing Wetan, Turen, and Wajak.
The total area of Dampit subdistrict is approximately 135,300 square km, with diverse land use.There are paddy fields covering 1,476.33 hectares, dry fields and farmland spanning 7,110.33 hectares.Additionally, there are plantations covering 2,560.78hectares and forests extending over 337.23 hectares.The area also includes residential and yard land covering 1,627.18hectares, as well as some industrial buildings covering 5.50 hectares.There is also some miscellaneous land covering 450.15 hectares.Overall, the soil structure in Dampit subdistrict tends to be of the podzolic type.The region features diverse topography, with both plains and mountains, ranging in elevation from 300 to 460 meters above sea level.The soil slope is generally less than 40%.The average annual rainfall in this area is approximately 1,419 mm.In terms of administration, Dampit subdistrict is divided into 1 urban village, 11 villages, 46 hamlets or neighborhoods, 114 RW (Rukun Warga or Community Unit), and 71 RT (Rukun Tetangga or Neighborhood Unit).This is a brief overview of Dampit, a subdistrict with diverse geographical and administrative characteristics in Malang Regency, East Java, Indonesia.
Based on data from the Ministry of Home Affairs, Directorate General of BPD 2017, in Sukodono Village, there is a total population of 10.579 people, with 2.168 people or 20.49% of the total population living in poverty.In Srimulyo Village, out of a total population of 14 .083people, 2.350 people or 16.69% are considered poor.Meanwhile, in Baturetno Village, 27.39% of the 3 .518inhabitants are living in poverty.Bumirejo Village has 2.358 poor residents out of a total of 11.165 people, which is equivalent to 21.12%.In Sumbersuko Village, out of a population of 7.277 people, 1.931 people or 26.53% live in poverty.Amandanom Village has 6 .589inhabitants, and 28.08% of them are in a state of poverty.Next, Dampit Village has a population of 26,970 people, with 6.494 people or 24.08% of them being poor.In Pamotan Village, out of a total of 20 .254residents, 4.116 people or 20.32% live in poverty.Majangtengah Village has 13.690 residents, and 20.04% of them are considered poor.Rembun Village has 6.139 residents, and 22.94% of them are in a state of poverty.In Pojok Village, out of a total population of 3.180 people, 599 people or 18.85% are considered poor.Finally, in Jambangan Village, out of a total population of 11.633 people, 1.986 people or 17.07%live in poverty.The rural poverty rate in East Java Province is 13.90% and the national rural poverty rate is 12.29%, indicating that Dampit subdistrict has a poverty rate above the rural poverty rate in East Java Province and nationally.In general, the Dampit subdistrict possesses a total of 14 Islamic Elementary Schools (MI), 47 Elementary Schools (SD), 12 Junior High Schools (SMP), 7 Islamic Junior High Schools (MTS), 2 Senior High Schools (SMA), 2 Islamic Senior High Schools (MA), and 6 Vocational High Schools (SMK).The count of educators in this subdistrict amounts to 167 teachers, while the student population includes 2,304 in MI, 8,851 in SD, 2,817 in SMP, 1,987 in MTS, 1,134 in SMA, 640 in MA, and 2,559 in SMK.This data offers a valuable insight into the educational infrastructure within the region and its unequal distribution.

Village
Within the Dampit subdistrict, instances of students dropping out of school are frequently associated with the prevailing poverty conditions experienced by the local community.The challenging economic circumstances frequently compel them to make difficult decisions.In numerous cases, education becomes a casualty of these situations, as children are compelled to prematurely end their educational pursuits due to financial limitations, with some even entering the workforce at an age when they should ideally still be pursuing their studies.In instances where local employment opportunities are scarce, seeking employment outside the region often becomes the preferred choice.
As it is commonly recognized, Dampit stands as one of the subdistricts with the second -largest population, trailing only Singosari Subdistrict in Malang Regency (Ranggana & Nasrudin, 2021).Additionally, the Dampit subdistrict plays a significant role in sending both male and female laborers overseas for work.All of these circumstances are closely linked to the high incidence of poverty and the lower educational attainment within the community.The reduced level of education among parents also influences their limited understanding of the significance of education for their offspring.Consequently, within the statistics of Dampit, we observe a not ably elevated dropout rate, particularly at the primary education level.Furthermore, another pertinent issue pertaining to education is the heightened occurrence of early marriages and informal unions (secret marriages) among youngsters who are no longer engaged in formal schooling.
Table 3 presents a diverse range of healthcare facilities in the area, including one Maternity Hospital, one clinic, two inpatient Health Centers (Puskesmas rawat inap), and five pharmacies distributed throughout villages and urban zones.Despite the existing healthcare infrastructure, additional focus is required, particularly in villages lacking direct access to healthcare services.Furthermore, the significance of healt hcare workforce availability is evident in the figures for doctors, nurses, midwives, and nutritionists as indicated in the table.Dampit village stands out with a higher number of healthcare professionals compared to other villages, possibly indicating a superior level of healthcare provision there.By comparing villages based on their healthcare facilities, it becomes a foundation for expanding or enhancing healthcare resources to align with the specific needs of the local population.
Primary healthcare services, especially Health Centers (Puskesmas), play an essential role in deliverin g healthcare to the community, thus warranting special attention.Additionally, the presence of pharmacies and nutritionists is vital for ensuring the accessibility of medications and nutritional support required by the population, all while maintaining appropriate oversight and regulation.This data can serve as a starting point for planning improved healthcare infrastructure development within the Dampit Subdistrict.In general, the information presented in Table 4 depicts the state of housing and environmental conditions in the Dampir ara as follows: The data regarding drinking water sources provides insights into the diverse water supply situations among households.Among the surveyed households, it's noteworthy that only one household relies on refillable water as their primary source, while five households have the convenience of tap water with meters.Additionally, six households depend on spring water as their primary source of drinking water.It's important to contextualize this information by considering the findings of a previous study conducted by Hardati et al. (2020), which revealed that in some industrial centers, households opt for bottled water due to its practicality and accessibility, although a small percentage of households still rely on untreated spring water for drinking purposes.
Moving on to electricity usage, a substantial majority of households, specifically 42.437 households, are connected to and utilize the services provided by PLN, the State-Owned Electricity Company.However, it's worth noting that there is no available information regarding households using non -PLN electricity sources.In the realm of energy consumption within households, it's important to recognize the influence of various determinants.Kotsila & Polychronidou (2021) have identified key factors that significantly impact household electricity consumption.These factors include the number of occupants, the size of the dwelling, the type of heating system in use, the duration of heating and cooling, as well as weather conditions.
Regarding main road lighting, a subset of households, specifically five of them, is connected to the government's electricity supply for their lighting requirements.On the other hand, seven households opt for nongovernment electricity sources for their main road lighting.Interestingly, no households within the dataset rely on non-electric lighting methods for their main road illumination.Access to main road lighting can improve safety and security, especially for those living in poverty who may not have access to other forms of lighting.This can also improve access to transportation and economic opportunities (Megawati et al., 2021).
Shifting our attention to toilet facilities, it is noteworthy that 12 households have access to latrines, a critical component for ensuring proper sanitation.Access to proper toilet facilities can improve health and hygiene, reducing the spread of diseases and improving overall well-being.Lack of access to proper toilet facilities can also lead to social exclusion and discrimination (Megawati et al., 2021).However, the dataset does not provide information about households that lack latrine facilities, which would be valuable for a comprehensive assessment of sanitation infrastructure.
Finally, the dataset provides insights into the fuel types used for cooking purposes.Specifically, it indicates that 12 households utilize LPG cylinders with a 3 kg capacity for their cooking needs.However, there is a lack of information concerning the utilization of other fuel types, such as city gas, larger LPG cylinders, firewood, kerosene, or any other alternatives for cooking within the surveyed households.Access to clean and efficient fuel sources can improve health and reduce environmental degradation.In many developing countries, the use of traditional biomass fuels such as wood and charcoal can lead to indoor air pollution and respiratory illnesses.Providing access to clean and efficient fuel sources such as liquefied petroleum gas (LPG) can help alleviate poverty and improve health outcomes (Megawati et al., 2021).
Optimizing Data Collection Strategies for Effective Poverty Alleviation: Insights from the Social Welfare System -Next Generation (SIKS-NG) (Nur Chamidah) 135 Based on the previously mentioned data, it becomes evident that the Dampit Subdistrict plays a pivotal role in facilitating poverty alleviation initiatives within the region.The elevated poverty rates, surpassing rural poverty rates at both the provincial and national levels, empha size the substantial challenges that must be addressed to mitigate poverty in Dampit.This underscores the importance of concentrating on socio -economic programs that can provide aid to the impoverished population, such as social welfare, skills training, or small and medium-sized enterprise initiatives.
The data on education infrastructure reveals that the educational landscape in Dampit still faces hurdles.The distribution of schools and educators across villages remains uneven, and the substantial rates of students discontinuing their education, particularly at the primary level, highlight the pressing need for more extensive educational interventions and investments in educational facilities.Enhancing access to education and the quality of educational services can help disrupt the cycle of poverty, which is often linked to lower educational attainment.
The information on healthcare facilities demonstrates that the healthcare infrastructure is already in place but requires further attention, particularly in villages lacking direct access to healthcare services.It is equally important to ensure an adequate healthcare workforce, including doctors, nurses, midwives, and nutritionists.This aspect plays a pivotal role in delivering quality healthcare services to the local population.Special emphasis should be placed on the development of primary healthcare services, such as Health Centers (Puskesmas).
Overall, this dataset serves as a robust foundation for government bodies and relevant institutions to formulate and execute poverty alleviation programs that center on education, healthcare, and fundamental infrastructure in the Dampit Subdistrict.This endeavor can significantly enhance the living standards of the populace and diminish poverty levels in the area.The implications of this data also underscore the necessity for enhancements in the drinking water infrastructure, the diversification of power sources, the upkeep of primary road lighting, and the improvement of sanitation facilities to elevate the quality of life for the local residents.
In the context of public services, addressing poverty presents challenges due to the complex handling by various Regional Work Unit (SKPD) offices, resulting in increasingly lengthy bureaucratic processes (AZ, 2022;Bayliss et al., 2021).Efforts to alleviate poverty cannot proceed without effectively addressing issues in education, healthcare, the economy, housing, and social security, necessitating government involvement through public service delivery to the community.Consequently, it can be concluded that poverty alleviation falls within the purview of public services.
Adaptation plays a pivotal role in implementing E-government through the National Poverty Information System (SIKS-NG).Adaptation here refers to individuals or organizations adjusting to environmental changes, encompassing developments in information and communication technology and changes in poverty data processing.The effectiveness of this application program can be gauged through several criteria: a. Capacity Development: Individuals and organizations need to possess the skills to understand and utilize the information technology underpinning SIKS-NG.b.Technological Flexibility: Given the evolving nature of information technology, SIKS-NG will undergo changes and updates.c.Data Processing Proficiency: A strong grasp of how SIKS-NG operates and how poverty data informs decision-making is vital for its effective use.d.Responsiveness to Community Needs: SIKS-NG should be employed to enhance services to the community concerning poverty, necessitating the ability to respond to community needs using available data.
The SIKS-NG program holds the potential to positively impact the empowerment of impoverished and less privileged communities (Asrandi T et al., 2022).To realize this potential, it is imperative to ensure the system functions smoothly, produces accurate data, and offers suitable services to those in need.The focus on adaptation underscores the importance of addressing the specific requirements of less privileged populations, aligning with government responsibilities for community well-being.
In summary, the appointment of SIKS-NG operators by the Welfare Section Chief, confirmed by village heads, represents a crucial step in system management.Selecting operators with computer expertise is a wise choice, as they will oversee SIKS-NG operation and maintenance.Additionally, involving selected operators in Technical Guidance (BimTek) is a commendable practice, enabling them to gain a deeper understanding of the system, update their skills, and leverage SIKS-NG effectively for poverty data management.Improved system comprehension ensures smoother SIKS-NG operation and maximizes its benefits.
The identification of poverty indicators, derived from the 2020 Integrated Database Update and subsequently integrated into the Social Welfare Information System (SIKS NG) application, requires ha rmonizing and consolidating data to yield reliable, accurate, and valid information regarding the number of impoverished individuals.The second discovery pertains to the mechanism for collecting poverty data, encompassing both the Poor Data (DAMIS) progra m and SIKS NG.Data unification informs the determination of impoverished communities.Should SIKS NG be deemed more comprehensive and supported by various government programs, it could serve as a guiding framework for data collection procedures.

Strategies and Directions for Enhancing the Competency of Government Officials in Poverty Data Collection
The Malang regency Government should establish a Coordination Team for Accelerating Village Poverty Alleviation under the auspices of the Malang regency Social Office, which would involve village government officials.The formation of this Coordination Team for Accelerating Village Poverty Alleviation involving village government officials is a positive step towards improving data collection at th e village level.This is beneficial because involving village governments in the poverty data collection process is a commendable participatory approach.By engaging village governments, there is potential to enhance data accuracy.Village governments can conduct more intensive and comprehensive data collection, which can help identify the impoverished population more effectively.With better data collection and participation from village governments, poverty alleviation programs can be more precisely targeted.This means that aid and resources will be allocated to those who truly need them.
Utilizing information infrastructure such as the Integrated Database Update (BDT) is a commendable step to ensure the accuracy of data concerning the less privileged pop ulation.This aids in the verification and validation process, which is crucial for poverty alleviation programs.The existence of BDT is expected to yield accurate and precisely targeted data results.The presence of BDT significantly eases the responsibilities of officers in charge, who are tasked with conducting monthly checks and preparing periodic reports delivered to subdistricts and the Social Office.It is essential to maintain and manage the BDT effectively, including through training authorized personnel responsible for verification and validation.This way, the system can continue to be an effective tool in poverty alleviation efforts at the subdistrict level.
In line with the information obtained from BDT regarding Purwantoro Subdistrict, Blimbing District , Malang City, through the community institution Puskesos, priority is given to ensuring compliance with service standards related to information on assistance receipt and distribution through 14 indicators that determine the impoverished population every month.These 14 indicators include: 1) Legal basis, 2) Requirements, 3) Systems, mechanisms, and procedures, 4) Completion time frame, 5) Costs/tariffs, 6) Service products, 7) Facilities, infrastructure, and amenities, 8) Competence of implement ation, 9) Internal supervision, 10) Handling of complaints, suggestions, and input, 11) Quantity of implementation, 12) Service guarantee, 13) Security and safety guarantees, and 14) Performance evaluation.
Data consolidation in determining the impoverished population is a highly important step to ensure that the data used in poverty alleviation programs is accurate and integrated (Tonggil et al., 2021).It is crucial to reach a consensus among all stakeholders, including local governments, in deciding to use SIKS-NG as a guideline for data collection mechanisms.Furthermore, there must be a commitment to ensuring effective implementation and sustainable maintenance of this system.Thus, poverty data can be more accurate, and poverty alleviation programs can be more targeted and efficient.By using SIKS-NG as a guideline for data collection mechanisms, operators at the village level are not burdened with two concurrent systems, DAMIS and SIKS-NG.Allocating a budget for the socialization support program of the Social Welfare Information System (SIKS) NG, initiated by the Ministry of Social Affairs through the Malang regency Social Office, ensures that support for village-level operators can be maximized in a sustainable manner.
The Village Management Acceleration Coordination Team is tasked wit h collecting data in accordance with the following mechanism: The process of socializing and establishing data collection teams plays a vital role in poverty data collection (Arifah et al., 2020).These teams consist of individuals responsible for gathering information concerning communities at risk of poverty.Team members need to undergo training and possess a solid understanding of the data collection process while also remaining attuned to social concerns.

b. Debriefing of Data Collection Team
Thoroughly preparing and planning are essential elements in ensuring the continuity of public services.This can be elucidated by considering several critical factors, such as defining the objectives to be achieved, delineating the activities necessary to attain these goals, and specifying the timing of their implementation.Consequently, preparation can be viewed as a proactive response to achieving future objectives.In addition to preparation, planning represents another fundamental aspect used to accomplish these aims.The role of community participation in local development planning, emphasizing the importance of involving the community in determining the direction of development programs (Wijaya & Subekti, 2021).Handayani et al. (2020) emphasizes that urban and regional planning involves prescribing spatial components of the human living environment The preparatory services carried out by the Social Welfare Center (Puskesos) in the Purwantoro Subdistrict, involving the absorption of community aspirations, constitute highly affirmative measures aimed at empowering and elevating the welfare of disadvantaged communities.In this context, the active engagement of the community in the deliberative process and the submission of their proposals through the Integrated Data (BDT) mechanism provide opportunities to enhance the pertinence and effectiveness of social programs administered by Puskesos.However, it is essential to acknowledge that community participation entails more than just assimilating their aspirations; it also involves empowering them in the decision -making and oversight processes.Local authorities, Puskesos, and relevant organizations must continually facilitate these procedures, ensuring that community aspirations genuinely shape the programs that are implemented.
The training is designed to enhance the competencies of the Data Collection Team in collecting data on impoverished populations and inputting this data into computer application systems.Through these discussions, the local community can collaboratively determine who genuinely meets the poverty criteria and is eligible for assistance.This step aims to prevent errors and misuse in determinin g aid recipients and enhance transparency and community participation in poverty alleviation programs.After the discussions, the results of the Village discussions, presented as the List of Prospective TPHH, are made public for a minimum of 7 days to allow the community sufficient time to provide feedback or raise objections.The List of Prospective Temporary Poor Households can be disclosed through Notice Boards, Leaflets/Pamphlets/Posters, formal and informal discussion forums.The dissemination of the L ist of Prospective Temporary Poor Households is overseen by the Coordination Team for Accelerating Villa ge Poverty Alleviation while respecting individuals' rights protected by the law.
Once the publication period concludes, the received feedback or object ions must be considered during the subsequent verification and validation process.Verification and validation results will be used to determine the final list of TPHH and those who genuinely meet the poverty criteria.The objection period for the List of Prospective Temporary Poor Households lasts for 7 (seven) days from the village discussions.If objections are raised, verification is conducted by the Coordination Team for Accelerating Village Poverty Alleviation within a maximum of 3 (three) days from the receipt of objections.In the absence of objections during the objection period, the List of Prospective Temporary Poor Households is finalized and used as a reference for data collection.

d. Determining the Population Living in Poverty
The process of gathering information about people living in poverty, with the aim of creating a Temporary List of Poor Households (RTMS), involves various steps that engage data collection officers and those living in temporary poverty.Data collection is carried out by dat a collection officers either visitin g each temporary poor household individually or by gathering data from approximately 10 temporary poor households and completing a questionnaire about the impoverished population.
The crucial step in managing poverty-related data is the data entry process, which utilizes a computer application system (Liu et al., 2021).The Data Collection Team is responsible for entering the data gathered, and its members must possess the necessary skills to operate the computer application system used for data management.This computer application system is instrumental in organizing the collected data by categorizing households according to poverty criteria, such as Extremely Impoverished, Impoverished, Nearly Impoverished, Vulnerably Impoverished, and Non -Impoverished.Following this, the data collected undergoes processing through the computer application system to produce lists of Poor Households, Non -Poor Households, and Aggregate Data.
The objection period for the lists of Poor Households, Non -Poor Households, and Aggregate Data spans at least 7 (seven) days from the date of publication.Should there be any objections raised by the Figure 1.Flow Chart & Mechanism of Data Collection on Poor People Socialization and team formation involve a variety of participants, including Village Government officials, Village Representative Bodies, Village Community Organizations, Religious Leaders, Community Leaders, Women's Representatives, Impoverished Citizens, members o f the Business Community, and other stakeholders.Resource persons include the Village Head, the Coordination Team for Accelerating Villa ge Poverty Alleviation, Subdistrict Head, representatives from Civil Society Organizations, Committee members, and Facilitators.The organizing committee comprises the Village Government, with the Facilitators being part of the Coordination Team for Accelerating Village Poverty Alleviation.
The participants in this training are the pre-established Data Collection Teams.The training is organized by the village p-ISSN: 2528-2077, e-ISSN: 2528-1208 138 government, under the supervision of the subdistrict authority.The training sessions a re facilitated by the Coordination Team for Accelerating Village Poverty Alleviation, and the resource persons for the training include the Village Head, the Coordination Team for Accelerating Village Poverty Alleviation, the Subdistrict Head, and Civil Society Organizations.The training materials pertain to the Regent's Regulation on the Criteria and Procedures for Poverty Population Data Collection.These materials cover a range of topics, including the Mechanism and Stages of Poverty Population Data Collection, Interview Techniques, Poverty Population Questionnaire Forms, and Data Entry procedures.c.Determination of Temporary Poor Population Determining the population eligible for temporary poverty assistance involves creating a list o f potential temporary poor households (Prospective TPHH) by incorporating data from the Central Statistics Agency (BPS) and new proposals from the community in a participatory manner.The list of potential temporary poor households is discussed with the community and carried out step by step, starting from the Neighborhood Unit (RT) level, through Hamlet, and up to the Village level.Neighborhood Unit (RT), Hamlet, and Village discussions are conducted to establish the data of Prospective TPHH at their respective levels.These discussions are led and organized by leaders at each level.For example, Neighborhood Unit discussions are led by the Neighborhood Unit Head, Hamlet discussions are organized by the Hamlet Head, and Village discussions are conducted by the Village Consultative Body.The Neighborhood Unit discussions are coordinated by the Coordination Team for Accelerating Village Poverty Alleviation.Participants in these discussions consist of various stakeholders, including Community Leaders, Religious Figures, Women's Representatives, Impoverished Residents, Business Community members, and other stakeholders.The result of these discussions is the List of Prospective Temporary Poor Households at the RT, Hamlet, and Village levels.The outcomes of the discussions are documented in a Meeting Report.

Table 4 .
Health Facilities in Dampit Subdistrict