Learning Vector Quantization 3 (LVQ3) Usage To Determine Recipients of the Family Hope Program (Case Study: Tanjung Lubuk District)

  • Muhammad Deni Alfani Universitas Bina Darma, Indonesia
  • Ilman Zuhri Yadi Universitas Bina Darma, Indonesia
Keywords: Keywords: PKH, Artificial Network, LVQ3.

Abstract

The problem of poverty is a dilemma that the Government must solve. One of the Government's programs is the welfare program for the Family Hope Program (PKH). Tanjung Lubuk district, implementing the Family Hope Program experienced several obstacles in identifying PKH recipients, one of which was selection, limited, and close to officers so that it could lead to the provision of PKH assistance on target. Another problem is that the recipients of the data used are still using old data that has not been updated regularly, so many people who deserve assistance do not receive assistance. The research variables used were 35 variables. The output categories were entitled to receive and not entitled to receive PKH. The research method uses Learning Vector Quantization (LVQ) 3. The data are from 654 low-income families in Tanjung Lubuk District. The data used are 90:10 for practice data and 80:10 for test data. The learning rate values are 0.1, 0.3, 0.5, 0.7, and 0.9, while the learning rate reduction is 0.1, the minimum learning rate is 0.01, the window is 0.1, 0.5, and the m value is 0.1, 0.5. The accuracy obtained is 94.4%.

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Published
2022-12-03
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How to Cite
Alfani, M., & Yadi, I. (2022). Learning Vector Quantization 3 (LVQ3) Usage To Determine Recipients of the Family Hope Program (Case Study: Tanjung Lubuk District) . Journal of Information Systems and Informatics, 4(4), 1030-1041. https://doi.org/10.51519/journalisi.v4i4.374