KOMBINASI METODE ENSEMBLE, CFS DAN POHON KEPUTUSAN UNTUK PREDIKSI KINERJA PETUGAS STUDI KASUS: SURVEY PODES BADAN PUSAT STATISTIK
DOI:
https://doi.org/10.30736/jti.v5i1.390Keywords:
prediksi, kinerja petugas, survey bps, podes, badan pusat statistikAbstract
Kegiatan rilis data pada pendataan Potensi Desa (Podes) Badan Pusat Statistik pada kurun waktu sembilan tahun terakhir, selalu mengalami keterlambatan. Untuk meminimalisir agar keterlambatan tidak terjadi secara berulang, penelitian bertujuan memprediksi kinerja petugas berdasarkan faktor internal dan eksternal sebagai informasi kegiatan di Badan Pusat Statistik (BPS). Pemilihan atribut pada penelitian ini menggunaan nilai gain informasi dan Correlation feature selection (CFS), selanjutnya dilakukan pemodelan dengan algoritma Pohon Keputusan. Hasil penelitian ini menunjukkan akurasi prediksi pada petugas organik BPS meningkat sebesar 10.57 % dari 63,19 % menjadi 69,87 % dengan faktor penentu keterlambatan adalah beban kerja, track record dan kemudahan lokasi, sedangkan pada petugas mitra dengan menggunakan metode CFS akurasi prediksi meningkat sebesar 24,11 % dari 65,78 % menjadi 81,643 % dengan faktor penentu keterlambatan adalah nilai pendalaman,kemampuan bekerja dalam tim, dan profesionalitas.Downloads
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