Klasifikasi Calon Penerima Bantuan Keluarga Miskin Menggunakan Metode Learning Vector Quantization (LVQ) (Studi Kasus: Daerah Kecamatan Mlandingan, Situbondo) - Rio Arifando; Nurul Hidayat; Arief Andy Soebroto (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
In an effort to reduce poverty, the government of Mladingan sub-district, Situbondo provides social funds for society categorized as low class society, the fund is given based on an assessment of several indicators, determined by the government and made to assist the government staff in classifying the families who deserve it, so that the distribution of fund is well-targeted. This study aims to design a system that can classify the society by assessing them as fund beneficiaries or not. Classification method used in this study is Learning Vector Quantization. The data input of the prospective beneficiaries through data transformation process will result as data weight, which is used in the classification process. Weighting data are done by giving such score according to each parameter. The object used in this study is the data collection of the Families in Mlandingan Subdistrict, Situbondo. The family data contain 7 poverty parameters including age, the number of the family members, income, outcome, housing conditions, home ownership status, and educational level. This study uses five test scenarios that resulted a recommendation value of learning rate 0.1, decrement learning rate 0.1, training data as 30%, minimum learning rate 0.01 and maximum number of iterations 2. Accurate results obtained is 98%.