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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic
Dwi Marisa Efendi
Institut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia
Computer Science & IT Decision Sciences, Operations Research & Management

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Classification of Social Assistance Recipients Using Machine Learning Cyndi Oktora Putri; Dwi Marisa Efendi; Rustam Rustam
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9550

Abstract

Social assistance is assistance funds provided by local governments. In the Minister of Home Affairs Regulation No. 32 of 2011, it is explained that social assistance is the provision of assistance in the form of money/goods from local governments to individuals, families, groups, and communities which is not continuous and selective in nature. One of the villages in North Lampung still often experiences problems, including high poverty rates and low education levels. The Naive Bayes algorithm method was chosen to classify aid recipients based on employment, age, and income. The spreadsheet calculations show that the Family Hope Program Assistance (PKH) class is 135 people and the Direct Cash Assistance (BLT) class is 39 people with a total of 176 people in the social assistance recipient data. From the results of Rapid Miner calculations, the accuracy value for the PKH and BLT classes is 100.00%.
Eligibility Recipients Determination of El-Nino Direct Cash Aid Using C4.5 Algorithm Dini Elldrica Putri; Dwi Marisa Efendi; Siti Aminah
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9551

Abstract

The El Nino phenomenon, which causes increased temperatures and prolonged drought, is predicted to continue to hit Indonesia until the end of 2023. The significant impacts of El Nino include reducing the productivity of agricultural products, including food crops such as rice. This condition will certainly greatly affect the stability of domestic food prices. A total of 420 people from Bernah Village, Kota Alam Subdistrict received it. In the selection process for BLT El Nino recipients in Bernah Village, problems often occur such as problems in identifying aid recipients. This creates a society where aid recipients are still relatively well-off. By applying the C4.5 Algorithm, the C4.5 Calculation can determine feasibility thereby increasing efficiency in determining El Nino Direct Cash Assistance receipts. Calculations using Microsoft Excel 2010 revealed that there were 252 eligible recipients while 168 were not eligible. And from the calculation results using Rapid Miner 10.1. It is known that the accuracy rate is 100.00% with a total of 420 people receiving aid data.
Co-Authors Cyndi Oktora Putri Dini Elldrica Putri Rustam Rustam Siti Aminah