Assistance Recipients Research Articles

Klasifikasi Masyarakat Penerima BPNT Program Sembako 2021 di Kelurahan Tiakar dengan Mengunakan Metode KNN Classifier

At the time of the implementation of the food program in the Tiakar Sub-District, there was no information available on the criteria that would make the head of a family eligible to be a recipient of assistance, even though this was very important so that the government's goal of fulfilling the need for nutritious food could be felt by those who really needed it. The purpose of this study was to classify the heads of families in Tiakar Subdistrict as eligible or not eligible to receive staple foods using the K-Nearest Neighbor method. This study uses interview data conducted with the heads of families in the Tiakar Village. The data analysis step is to divide the data into training data and test data by 80%:20%, determine the number of nearest neighbours, calculate the dissimilarity distance and choose a class, calculate the level of accuracy using the confusion matrix and then choose the optimal K. Based on the results of the study, it was found that the K value that was good to use in the classification of family heads in Tiakar Subdistrict was K=3 because it had an accuracy percentage of 95%.

Penerapan Metode TOPSIS Untuk Menentukan Penerima Bantuan Santri Kurang Mampu Pada Lembaga Pendidikan Islam Pondok Pesantren

In The educational unit is one of the tools to achieve educational goals in Indonesia. Most educational institutions in Indonesia are still experiencing some problems in selecting candidates for assistance, because they are still evaluating in a conventional way so that it is feared that the recipients of the assistance will be students or students not fairly and appropriately. target. Therefore, it is necessary to create a Decision Support System for Recipients of Santri or Student Assistance, to determine which students or students fall within the criteria for being eligible to receive such assistance. The criteria used are 6 criteria, namely parents' income, crafts, type of floor of the house, average report card score, parents' occupation, and vehicle ownership. Making this system uses the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, which is a decision-making method with a hierarchical structure, which is indeed included in the Multi Criteria Decision Making (MCDM).

A Systematic Literature Review of Welfare Sanctions and Racial Equity

In the United States, welfare sanctions are designed to enforce compliance with program rules and are the most tangible and, in some cases, detrimental enforcement mechanism for recipients of public cash assistance. Whether or not they are equitably administered is critical to our understanding of the program and of safety net enforcement mechanisms across the sector. Using electronic databases of peer-reviewed articles across disciplines, this systematic literature review resulted in a deeper understanding of the research conducted related to racial inequities and welfare sanctions. The 29 studies included in this analysis highlight different rates and effects of sanctions for recipients depending on race and ethnicity. Findings are synthesized and recommendations for future research are presented.

Hybrid Decision Tree Method and C4.5 Algorithm for a Recommendation System in Determining Recipients of Direct Cash Assistance (BLT)

Development of a recommendation system to determine recipients of Direct Cash Assistance (BLT) using the C4.5 algorithm hybrid method and decision tree. In the current era of digitalization, the BLT program is a solution for the Indonesian government to help people affected by the COVID-19 pandemic. In this study, we propose a recommendation system that combines the C4.5 algorithm and a decision tree to increase accuracy and efficiency in determining BLT beneficiaries. Primary data was obtained through interviews and observations, while secondary data was obtained from the village administration, written reports, journals, theses and previous research. The results showed that the C4.5 algorithm and decision tree hybrid method gave good performance in determining BLT recipients. The C4.5 algorithm is used to calculate the accuracy of the training data and testing data with a ratio of 80% : 20%, while the decision tree is used to create a decision tree that classifies prospective BLT recipients. This research fills in the previous research gap regarding the recommendation system for determining BLT beneficiaries. The results of this study are expected to provide useful information for the government in making decisions regarding the BLT program, especially at the village or sub-district level. With an accurate and efficient recommendation system, financial assistance can be provided to those who really need it, helping to meet the basic daily needs of affected communities.

Sistem Pendukung Keputusan Penerapan Metode OCRA dalam Seleksi Penerima Bantuan Penduduk Miskin dengan Pembobotan ROC

The research conducted was the selection of recipients of assistance to the poor in Perk Village. Old Country in the district. District Downstream Labuhan Batu, namely BLT-DD or Village Fund Direct Cash Assistance. The process of distributing aid to the poor is still done conventionally in the selection of recipients of assistance to the poor, so the process of distributing funds is very long. This study applies a Decision Support System (SPK) which is used as a system that can facilitate the selection process for recipients of assistance to the poor. In the selection process for recipients of assistance, the poor must meet 5 criteria, namely Chronic Disease, Income, Occupation, Number of Dependents, and House Status. Thus, a system that can make decisions is needed, namely SPK by applying the ROC and OCRA methods to get the weight value of each criterion and the preference value of the best alternative, namely alternative A4 on behalf of Suadi with a preference value of 1.3757

Efektivitas Program Bantuan Pangan Non Tunai Dalam Pengentasan Kemiskinan Di Kabupaten Ponorogo

Poverty is the inability of individuals to meet their needs economically. One of the programs for poverty alleviation is Non-Cash Food Assistance (BPNT). The problems in the field are still many recipients of Non-Cash Food Assistance that need to be corrected. The primary purpose of this study is to analyze the realization of the BNPT program in poverty alleviation in Ponorogo district, to analyze the obstacles encountered by the BPNT program in the Ponorogo Regency area in poverty alleviation, and to analyze the effectiveness of the BPNT program in poverty alleviation in Ponorogo district. This research uses qualitative methods. The findings of this study are 1) Realization of the Non-Cash Food Assistance Program (BPNT) in Ponorogo district, namely to reduce the expenditure burden through meeting food needs. BPNT distribution involves Warung Agents taking food ingredients such as vegetables, eggs, etc. 2) Constraints of the BPNT program in poverty alleviation in Ponorogo district: inappropriate personal data resulted in the Prosperous Family Card not being issued. 3) The effectiveness of the BPNT program in poverty alleviation in Ponorogo district includes: Target accuracy needs to be more effective, Program socialization has not been effective, Program objectives have been effective, Program monitoring has not been effective because monitoring from the assistance is still ineffective.

Penerapan Metode Simple Multi Attribute Rating Technique (SMART) Untuk Seleksi Penerimaan Bantuan Usaha Produktif

Productive business assistance is assistance provided to improve business capabilities, depending on the type of business being running. The goal is to develop work productivity and also increase income. As for the distribution of productive business assistance at BAZNAS Pekanbaru City, it still uses an old system and is not yet effective, so the process takes quite a long process. In order for the selection process to be effective, a decision system was created for the alternative of recipients of productive business assistance. The method in this research using the simple multi attribute rating technique (SMART) method. This research uses 6 criteria and 22 sub-criteria. The application is build with using PHP and MySQL programming languages. The results of the application of the SMART method which has been tested on 10 sample recipients obtained the order of the highest value to the smallest. With the highest value is 0.75. This system has been tested using the Blackbox testing method and the user acceptance test (UAT) with an assessment final value is 94.4%.

Integration of the Hybrid Decision Support System and Machine Learning Algorithm to Determine Government Assistance Recipients: A Case Study in the Indonesian Funding Program

The Indonesian government provides incentives to facilitate community development through various funding programs to improve the economy and restore the national economy. However, there were many obstacles in determining the proper target beneficiaries. This study aims to assist decision-makers in determining targeted and accountable beneficiary candidates. In this study, a hybrid Analytical Hierarchy Process (AHP) method with Simple Additive Weighting (SAW) was used and integrated with machine learning modeling using Logistic Regression (LR). The AHP approach is used to determine the weight of each criterion, and the SAW method is used to sort out each available alternative with the help of an expert team's assessment. Instead, the LR method is used for the predictive analysis and classification of the resulting data.

Unemployment of parents — one of the poverty causes in families with children

Families with children in Russia are at high risk of poverty. The economic consequences of the coronavirus pandemic and the sanctions imposed in spring 2022 make social policy a priority. It is necessary to pay a stronger attention to groups of population at higher risk of poverty. In recent years the support for families with children has been strengthened and the circle of potential recipients of assistance expanded (raising the age of children, lifting restrictions on the type of family). Additional support measures provided to families with children to reduce the negative effects of the crisis helped to alleviate the severity of poverty but did not change the position of families with children on the welfare scale, they are still the largest group of the poor (according to Rosstat they make 80% of all poor households). It seems important to identify the main factors of poverty of families with children, which include, not least, the limited employment of parents. The main purpose of the study is to show, using the data of the statistical survey (HBS-2019), the scale of unemployment and its impact on families with children as well as gender characteristics of the phenomenon of unemployment. Financial assistance to families with children should be combined with work to activate the actions of families for self-sufficiency.

PENERAPAN METODE TOPSIS DALAM SISTEM PENGAMBILAN KEPUTUSAN PEMBERIAN BANTUAN REHABILITASI RUMAH

The provision of assistance for the rehabilitation of uninhabitable houses to the community often results in fraud and errors in the process of selecting and providing assistance. The TOPSIS method is a method used to calculate in the decision-making process in determining prospective recipients of uninhabitable housing rehabilitation assistance. The results of the TOPSIS calculation from the system were then tested using the sensitivity test method by giving a weight change of 0.5 and 1 for each criterion weight. The test results in 2019 with a total of 25 families of data obtained the most sensitive criteria for adding a weight of 0.5, namely the criteria for work and life dependents with a percentage result of 92%, while for adding weight to 1 the most sensitive criterion is the job criteria with a percentage result of 96%. Black box testing of the system built is measured using a questionnaire media with 10 statement items given to 15 respondents which is calculated using a Likert Scale where the system built is highly approved which can be seen from the index percentage value greater than 80% (strongly agree).

Satisfaction of Livable House Assistance Recipients with the Implementation of the Self-Help Housing Stimulant Assistance Program

The house is one of the primary needs that must be met by Indonesian people who do not meet livable standards. One of the efforts to be carried out in the infrastructure section, aims so that people get a decent place to live. To realize having a livable house must be planned and prepared properly. This research is reviewed from the problem of many houses that do not yet have decent housing standards. The Government's Self-Help Housing Stimulant Assistance (BSPS) is intended to encourage and increase the self-sufficiency of low-income people in improving the quality of their homes, as seen from the quality of roofs, floors and walls of the house along with infrastructure, public facilities and facilities that meet health, safety, comfort requirements. and bathroom facilities. In the BSPS program as an alternative to poverty alleviation policies by providing direct material assistance for the housing construction process. Based on the intent of the BSPS program objectives according to the BSPS program implementation team in Indonesia, especially in Rembang Regency, the aim of the BSPS program is to improve the quality of life of low-income people to obtain livable housing. that most people respond well to the BSPS program in Japrejo Village and Sumberejo Village Recipients of the Self-Help Housing Stimulant Assistance Program (BSPS), however, it is not uncommon for people to refuse due to limited funds which are considered insufficient. Satisfaction of building recipients after receiving assistance from Self-Help Housing Stimulant Assistance (BSPS) in obtaining assistance. Construction/improvement of houses for low-income people to ensure their quality meets the livable requirements of the BSPS program where assessment is carried out in the Structural and Non-Structural sections according to the house criteria livable

Subsidized health insurance impact among the poor: Evidence on out-of-pocket health expenditures in Indonesia

Universal Health Care (UHC) in Indonesia, named the National Health Insurance (Jaminan Kesehatan Nasional - JKN), has been running since 2014. JKN was predicted to be the most extensive UHC program in the world. Under JKN, the poor get free health services through the cashless method through a sub-program called Contribution Assistance Recipients (Penerima Bantuan Iuran - PBI). Unfortunately, JKN faced several failures to cover the program's expenditures within years. Within the current dynamics, was PBI, as part of JKN still effectively helping the poor? We examined the effectiveness of the PBI program by measuring differences in out-of-pocket health expenditures for the poor with similar socio-economic characteristics who used PBI and those who did not. We incorporated secondary data from National Socio-economic Survey (SUSENAS). The dataset was executed by using Propensity Score Matching (PSM) methodology. We used health expenditures and socio-economic parameters such as income, education, and gender from the 2017 and 2018 SUSENAS data. We found that in 2017, the total health expenditures of the PBI beneficiaries were lower than the non-beneficiaries. Nevertheless, by merging all two years' data, similar to 2018, we found general pattern that PBI participants' total health out-of-pocket payments were bigger than the non-participants. Health expenditures such as medicine, traditional practitioners, and others, were expenditure classifications in which PBI beneficiaries had lower expenses than non-beneficiaries in 2017. Therefore, Therefore, the UHC subsidy program for the poor in Indonesia has not only been ineffective through the years of implementation but also has not been effectively implemented for all variations of health expenditure types.

Covid-19 Social Aid Admission Selection Using Simple Additive Weighting Method as Decision Support

The process of receiving Covid-19 social assistance to residents who are recorded as social aid recipients in the RT.07 RW.10 Kp. Sukapura Jaya area is still uneven. The second problem is that there is no particular mathematical calculation to determine the value of the weight of the criteria, especially for residents who are recorded as receiving Covid-19 social aid in the RT.007 RW.10 Kp. Sukapura Jaya area. The gradual decline in social aid programs so that the number that falls does not match the data of social aid recipients. This caused a polemic for RT administrators in distributing social aid programs. The decline in social aid programs does not match the number of citizens recorded. It overcomes citizens who cause social jealousy—analyzing the problems experienced by the RT management in the distribution of Covid-19 social assistance, especially the RT.07 RW.10 Kp. Sukapura Jaya area to residents who are recorded as recipients. Selecting Covid-19 social assistance recipients, especially in the RT.07 RW.10 Kp. Sukapura Jaya area. So the application of methods as decision support is needed, and it is needed to help determine the weight of particular criteria for citizens who are recorded as more in need. This study proposes a decision support method using the Simple Additive Weighting (SAW) method, which is expected to help decision-making in solving problems for selecting Covid-19 social aid recipients in the RT.07 RW.10 Kp. Sukapura Jaya community. The purpose of the study is to select residents who are recorded to receive social aid who are more in need first will get Covid-19 social aid.

Implementation of Determination of Families Recipients of Direct Cash Assistance Through Village Funds (DCA-VF) in Nagrak Village, Sukabumi Regency in 2022

This study aims to determine the implementation of the Direct Cash Assistance program implementation starting from the socialization of RT/RW and Musdesus to the implementation of the program. This study uses qualitative methods and determines the informants used to obtain information for the correctness of the data and validation of the data. Collecting data using the method of observation, interviews, and documentation. Data analysis techniques use the theory of Miles and Huberman. This study uses the implementation theory approach of the George Edwards III model. The success of this theory is measured by communication, resources, disposition, and bureaucratic structure. The results of the study showed that several obstacles occurred in the program, namely (1) several beneficiary families did not meet the program's criteria. (2) there are some invalid KPM data. In this case, the village government as the executor of the DCA-VF Program is looking for solutions and alternatives to overcome these problems so that the Village Fund Direct Cash Assistance Program can be implemented in Nagrak Village, namely, the Village government is more thorough in recording KPM criteria. It is necessary to re-emphasize socialization to the local RT/RW regarding the direct village fund cash assistance program (DCA-VF) as an effort to minimize social jealousy among the people of Nagrak Village, Cisaat District, Sukabumi Regency.

(In)visible Sanctions: Micro-level Evidence on Compulsory Activation for Young Welfare Recipients

Abstract Since the early years of activation and workfare in the 1990s, the use of welfare conditionality and benefit sanctions has been proposed among the necessary solutions to ensure the efficiency of welfare policy by reinforcing individual economic incentives. Using rich administrative registers from Norway, we produce micro-level quantitative evidence on compulsory activation for young recipients of social assistance. The empirical challenge is that activation through the threat of benefit sanctions is not a feature that unambiguously emerges from observational data, except for when sanctions indeed take place and benefits are reduced. To overcome this barrier, we introduce a novel methodology to identify individual-level effects of activation on young welfare recipients, exploiting municipal variation in the introduction of compulsory activation. More precisely, we study whether individuals who are residents in municipalities that have introduced compulsory activation display a stronger relationship between their labor market status (activation) and their benefit size (because sanctions being in place) compared to individuals residing in municipalities where activation has not been made compulsory. Our results show that there is no different relationship between social assistance benefits and passive labor market status for individuals living in municipalities that practice activation compared with individuals residing in municipalities in which activation is not yet mandatory. In other words, there is no visible effect of sanctions for passive recipients.

Covid-19 Social Aid Admission Selection Using Simple Additive Weighting Method As Decision Support

The process of receiving Covid-19 social assistance to residents who are recorded as social aid recipients in the RT.07 RW.10 Kp. Sukapura Jaya area is still uneven. The second problem is that there is no particular mathematical calculation to determine the value of the weight of the criteria, especially for residents who are recorded as receiving Covid-19 social aid in the RT.007 RW.10 Kp. Sukapura Jaya area. The gradual decline in social aid programs so that the number that falls does not match the data of social aid recipients. This caused a polemic for RT administrators in distributing social aid programs. The decline in social aid programs does not match the number of citizens recorded. It overcomes citizens who cause social jealousy—analyzing the problems experienced by the RT management in the distribution of Covid-19 social assistance, especially the RT.07 RW.10 Kp. Sukapura Jaya area to residents who are recorded as recipients. Selecting Covid-19 social assistance recipients, especially in the RT.07 RW.10 Kp. Sukapura Jaya area. So the application of methods as decision support is needed, and it is needed to help determine the weight of particular criteria for citizens who are recorded as more in need. This study proposes a decision support method using the Simple Additive Weighting (SAW) method, which is expected to help decision-making in solving problems for selecting Covid-19 social aid recipients in the RT.07 RW.10 Kp. Sukapura Jaya community. The purpose of the study is to select residents who are recorded to receive social aid who are more in need first will get Covid-19 social aid.

PERANCANGAN APLIKASI PENILAIAN RUMAH TIDAK LAYAK HUNI MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING PADA KEGIATAN KEMENTERIAN PUPR

Determination of recipients of uninhabitable housing assistance (RTLH) is one of the problems that is of concern to the Directorate of Self-Help Housing of the Ministry of Public Works and Public Housing of the Republic of Indonesia, because the large number of data on incoming aid proposals makes it difficult to determine which beneficiaries deserve due to the limited budget. One way to determine beneficiaries is to use a decision system using the Simple Additive Weighting (SAW) method. This method will provide the value of the selected alternative preference as an indicator to determine the recipient of assistance as it can help the data processing team, stakeholders and leaders in the Directorate of Self-Help as the government which has a policy in determining the prospective recipients of the house renovation assistance objectively not subjectively. The data used was taken from data collection conducted by the field team in the Serang City area of Banten Province where the data obtained were 935 residents

Sosialisasi Pemanfaatan Aplikasi Digital Platform Livin' by Mandiri pada Masyarakat Penerima Bantuan Sosial

This socialization activity was carried out in order to provide knowledge to the public or customers who are also recipients of social assistance regarding the use of the Livin' by Mandiri digital platform application. The goal is for customers to have easy access to banking services anytime and anywhere, as well as to encourage and strengthen the shift in the use of banking services from conventional to digital technology. The methods used in this activity are lecture and educational methods. The results of this activity have had a good impact where customers have enthusiasm and interest in utilizing digital services in the future. That way the knowledge and utilization of banking digitalization in people in remote areas will increase.

Integration of Fuzzy C-Means and SAW Methods on Education Fee Assistance Recipients

Every year, UMTAS gets a quota for KIP tuition fee assistance provided by KEMDIKBUD. This program is intended for high school / vocational/equivalent graduates from poor and vulnerable families. The evaluation results of its implementation have problems, including the number of applicants exceeding the quota given by KEMDIKBUD and some applicants coming from well-off families. This research uses the fuzzy c-means method for data clustering and the SAW method for ranking. The results of data grouping using the fuzzy c-means method obtained the first cluster (C1) of 72 data and the second cluster (C2) of 119 data. Group C1 is closer to the provisions of aid recipients (eligible) compared to data group C2 (ineligible) because Data C1 consists of 100% DTKS recipients, 50% KIP and KKS card owners, 100% parental income <750,000, 40.28% parental dependents >=2 people and 29.17% applicants with achievements. 72 registrant data included in Data C1 are then ranked using the SAW technique to get weights, and 30 data with the highest weight will be used as a decision on recipients of KIP-Kuliah Education fee assistance according to the quota provided. The optimization of Fuzzy C-Means with SAW methods in selecting recipients of education fee assistance is objective and right on target.

Implementation of the Naive Bayes and Simple Additive Weighting Methods in the Feasibility Selection of Village Cash Assistance Recipients

Village Fund Direct Cash Assistance (VF-DCA) is a direct cash assistance policy in which funds come from the village. The VF-DCA policy is expected to ease the burden on the community, especially those with a low economy. Still, it is undeniable that there are opportunities for misuse of village funds by some VF-DCA organizers, intentionally or unintentionally. Problems that arise in implementing VF-DCA are mistakes in determining the beneficiary communities that are not on target so that the impact of VF can be manipulated for the interests of certain groups in several cases of social assistance programs. The solution to these problems is that this research creates a decision support system in selecting the eligibility of assistance recipients. This research used the Naïve Bayes (NB) method in selecting VF-DCA recipients and the Simple Additive Weighting (SAW) way to determine the order of eligibility scores for each candidate for VF-DCA recipients. In this study, the results of the classification process using the NB method obtained an accuracy rate of 99.36%. Hence, this is simplified in determining potential recipients of VF-DCA using the NB and SAW methods