Data Automation For Poor Groups in The City of Surabaya

Purpose Research. is to determine the function of community data collection on poor group criteria in alleviating poverty in the city of Surabaya Research Method. The method used in this research is a qualitative research method, with the aim of finding, understanding, explaining and obtaining an overview of poverty alleviation in the city of Surabaya. Data Analysis . In this research, qualitative analysis was used with the steps of the Crosswell interactive analysis modelwhich consists of three components, namely reduction, data display and conclusions. Results. The main key to the poverty reduction strategy is population data collection that is truly appropriate to conditions in the field, so that it is no longer based on data deposits by the Head of the Neighborhood Association (RT) and the Head of the Community Association (RW). The Surabaya City Government (Pemkot) with various instructions from the Mayor directly dispatched the Regional Apparatus Organization (OPD) to carry out data collection to the location, all OPDs were involved without exception, so there is great hope of speeding up and maximizing community data collection. The results of accurate data collection make social assistance and poverty alleviation programs right on target. Conclusions. The Surabaya City Government has carried out maximum and realistic data collection on the poor, from accurate data collection. The Surabaya City Government has socialized the program and facilitated business premises in every existing sub-district such as SWK and RPK. The steps taken are to form and register MSMEs directly with the Cooperatives and Trade Office, registering poor people for the need to distribute social assistance.

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Publication Details

Journal
Tibanndaru Jurnal Ilmu Perpustakaan dan Informasi
Published
2024-10-16
DOI
https://doi.org/10.30742/tb.v8i2.4296
Primary Topic
Information Retrieval and Data Mining
Type
article
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article

Data Automation For Poor Groups in The City of Surabaya

Putu Ari Saruhum Hasibuan
Tibanndaru Jurnal Ilmu Perpustakaan dan Informasi
Information Retrieval and Data Mining
article

Data Automation For Poor Groups in The City of Surabaya

Putu Ari Saruhum Hasibuan
article en

Abstract

Purpose Research. is to determine the function of community data collection on poor group criteria in alleviating poverty in the city of Surabaya Research Method. The method used in this research is a qualitative research method, with the aim of finding, understanding, explaining and obtaining an overview of poverty alleviation in the city of Surabaya. Data Analysis . In this research, qualitative analysis was used with the steps of the Crosswell interactive analysis modelwhich consists of three components, namely reduction, data display and conclusions. Results. The main key to the poverty reduction strategy is population data collection that is truly appropriate to conditions in the field, so that it is no longer based on data deposits by the Head of the Neighborhood Association (RT) and the Head of the Community Association (RW). The Surabaya City Government (Pemkot) with various instructions from the Mayor directly dispatched the Regional Apparatus Organization (OPD) to carry out data collection to the location, all OPDs were involved without exception, so there is great hope of speeding up and maximizing community data collection. The results of accurate data collection make social assistance and poverty alleviation programs right on target. Conclusions. The Surabaya City Government has carried out maximum and realistic data collection on the poor, from accurate data collection. The Surabaya City Government has socialized the program and facilitated business premises in every existing sub-district such as SWK and RPK. The steps taken are to form and register MSMEs directly with the Cooperatives and Trade Office, registering poor people for the need to distribute social assistance.

Tibanndaru Jurnal Ilmu Perpustakaan dan InformasiVol. 8(2)
Universitas Wijaya Kusuma Surabaya (ID)
Openalex Percentile: Top 50%
Information Retrieval and Data Mining
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