Prediksi Laju Pertumbuhan Penduduk Di Kabupaten Sleman Dengan Metode Adaptive Neuro-Fuzzy Inference System (Anfis) Dan Metode Sugeno

Authors

  • Agus Dianto Universitas Ahmad Dahlan
  • Andri Pranolo Universitas Ahmad Dahlan

DOI:

https://doi.org/10.12928/jstie.v6i3.15248

Keywords:

Prediksi Laju Penduduk, metode anfis dan sugeno, Kabupaten Sleman

Abstract

Pemerintah kabupaten Sleman hanya mendapatkan data penduduk di Kabupaten Sleman dilakukan hanya saat sensus penduduk saja, dimana ketika pemilu dan program pemerintah saja. Dalam sistem prediksi laju pertumbuhan penduduk menggunakan metodologi adaptive neurofuzzy infrence system (anfis) dan metode sugeno. Metode adaptive neuro-fuzzy infrence system (anfis) dimulai dengan tahap menentukan lapisan 1, lapisan 2, lapisan 3, lapisan 4, lapisan 5. tahap perancangan sistem, tahap implementasi/coding, dan tahap pengujian sistem. Sistem diuji dengan 2 metode yaitu Black Box Test dan Alpha Test. Hasil penelitian ini menghasilkan sistem prediksi Laju penduduk. Hasil perhitunan anfis untuk mengetahui perbandingan data sensus dan data hasil hitung anfis dan Hasil prediksi pada priode selanjutnya yang dihitung menggunakan metode sugeno dan metode geometri. Data tersebut menghasilkan perbandingan data sensus dan data hasil hitung anfis sebesar 0,44%, dengan hasil pengujian prediksi metode sugeno naik sebesar 16,10% pada tahun 2020 dapat diketahui sangat meningkat dan hasil pengujian dengan metode geometri sebesar 1,65% dapat diketahui laju pertumbuhan penduduk setiap tahunya. Dan kesimpulan dapat diambil perbandingan hasil sensus dengan hasil hitung anfis meningkat, sedangkan mengunakan metode sugeno lebih baik untuk memprediksi laju pertumbuhan penduduk dan dengan metodemgeometri dapat diketahui prediksi laju pertumbuhan setiap tahunnya.

Author Biographies

Agus Dianto, Universitas Ahmad Dahlan

Teknik Informatika

Andri Pranolo, Universitas Ahmad Dahlan

Teknik Informatika

References

Dewi dan Himawati.2015. Prediksi Tingkat Pengangguran Menggunakan Adaptif Neuro Fuzzy Inference System (ANFIS). Malang: Konferensi Nasional Sistem & Informatika STMIK STIKOM Bali.

Fathansyah. 2004. Sistem Basis Data. Bandung: Informatika.

Pressman, Roger. 2012. Rekayasa Perangkat Lunak. Yogyakarta: Andi

Pujianta, Ardi. 2015. Fuzzy Logic. Yogyakarta: Ardi

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Kelistrikan Indonesia Menggunakan Algoritma Adaptive Neuro-Fuzzy Inference System. Jurnal ELECTRANS, VOL.11, NO.2

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Published

01-11-2018

Issue

Vol. 6 No. 3 (2018): Oktober

Section

Articles

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