Klasifikasi Rumah Tangga Penerima Subsidi Listrik di Provinsi Gorontalo Tahun 2019 dengan Metode K-Nearest Neighbor dan Support Vector Machine
DOI:
https://doi.org/10.26418/justin.v10i1.51210Keywords:
Klasifikasi, K-Nearest Neighbor, Subsidi Listrik, Support Vector MachineAbstract
Program subsidi listrik merupakan salah satu program pemerintah untuk penanganan kemiskinan, dimana keluarga tidak mampu mendapatkan bantuan subsidi listrik yang dibayarkan pemerintah ke PT Perusahaan Listrik Negara (PLN). Permasalahannya adalah masih terdapat rumah tangga yang mampu secara ekonomi namun tetap mendapatkan subsidi listrik. Penelitian ini bertujuan untuk melakukan klasifikasi rumah tangga penerima subsidi listrik menggunakan data mining serta melakukan perbandingan hasil klasifikasi dengan metode K-Nearest Neighbor (KNN) dan Support Vector Machine (SVM). Alasan pemilihan metode ini dibandingkan metode lainnya dalam data mining, KNN merupakan metode yang dapat mewakili lazy learning dan SVM merupakan metode klasifikasi yang dapat memberikan generalisasi. Data yang digunakan adalah data Susenas Provinsi Gorontalo tahun 2019. Variabel yang digunakan adalah status penerimaan subsidi listrik sebagai kelas dan variabel penjelas (atribut) mencakup jumlah anggota rumah tangga, status kepemilikan bangunan, luas lantai rumah, bahan atap rumah terluas, bahan dinding terluas, bahan lantai rumah terluas, sumber air minum utama, bahan bakar utama untuk memasak, dan tempat pembuangan akhir tinja. Program yang digunakan dalam pengolahan data adalah R. Hasil penelitian menunjukkan bahwa metode KNN memiliki akurasi yang lebih baik dalam melakukan klasifikasi yaitu sebesar 98,07%. Secara keseluruhan, terdapat perbedaan yang signifikan dari klasifikasi KNN dan SVM, dimana kinerja KNN jauh lebih baik dari SVM dalam melakukan klasifikasi.
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