Application Of Data Mining For Classification Of BLT-DD Recipients Using The Support Vector Machine Method
Keywords:
Support Vector Machine, BLT-DD, classification, socio-economic data, data miningAbstract
This study focuses on the application of the Support Vector Machine (SVM) algorithm for classifying recipients who are eligible to receive Direct Cash Assistance from the Village Fund (BLT-DD) in Nurussalam District, East Aceh Regency. The background of this research is the difficulty in identifying households eligible for BLT-DD due to increasing poverty and economic inequality exacerbated by the COVID-19 pandemic. This study aims to address this issue by utilizing the SVM algorithm, which can separate household data into two categories: "Eligible" and "Not Eligible." A total of 550 data points from Nurussalam District were used in this study, with 400 data points for training and 150 data points for testing. In the training data, 322 households (80.5%) were classified as "Eligible," while 78 households (19.5%) were categorized as "Not Eligible." The data collected includes variables such as household income, type of employment, education level, history of chronic disease, and home ownership status. After preprocessing the data, such as normalization and encoding, the SVM model was trained to classify BLT-DD recipients. In the testing data, 128 data points (85.33%) were classified as "Eligible," while 22 data points (14.67%) were classified as "Not Eligible." Further analysis of village distribution in Nurussalam District shows that some villages have a high percentage of eligible recipients, such as Blang Rambong and Alue Jagat, with 100% of recipients classified as "Eligible." Other villages, such as Arul Pinang and Alue Dua Muka O, show more varied eligibility rates, with 71.43% and 72.73% classified as "Eligible," respectively. In conclusion, the SVM algorithm provides an effective approach in determining the eligibility of BLT-DD recipients, helping the government to distribute assistance more accurately and efficiently in Nurussalam District.
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