Prediksi Inflasi Indonesia Berdasarkan Fuzzy Ann Menggunakan Algoritma Genetika

  • Anwar Rifa'i Program Studi Teknik Informatika, Fakultas Teknologi Informasi, Universitas Budi Luhur, Jakarta, Indonesia
Keywords: Algoritma Genetika, Backpropagation, Inflasi, Neural Network, Prediksi

Abstract

Pemangku kebijakan moneter memiliki ketakutan terhadap inflasi karena dapat memicu naiknya angka kemiskinan dan melonjaknya penggunaan anggaran. Tingkat Inflasi yang tinggi akan mengakibatkan jatuhnya perekonomian suatu negara. Pengambilan kebijakan moneter perlu dikaji secara mendalam un-tuk mencegah hal tersebut. Salah satu upaya yang dapat dilakukan adalah dengan melakukan prediksi inflasi yang akan terjadi. Data tingkat inflasi dari waktu ke waktu merupakan modal untuk melakukan prediksi tingkat inflasi pada waktu mendatang. Suatu prediksi yang baik memiliki nilai error yang kecil. Pada prediksi menggunakan fuzzy artificial neural network (Fuzzy ANN) metode backpropagation, nilai error dapat diperkecil dengan melakukan optimasi pada bobot yang dihasilkan. Pada penelitian ini, op-timasi bobot Fuzzy AAN dilakukan menggunakan algoritma genetika. Model prediksi yang diperoleh se-lanjutnya dievaluasi menggunakan MAPE untuk menentukan keakuratan prediksi. Hasil penelitian menunjukkan bahwa prediksi menggunakan backpropagation neural network dioptimasi menggunakan algoritma genetika (10,33%) lebih baik dibandingkan dengan prediksi menggunakan backpropagation neural network saja (11,67%). Setelah mengetahui bahwa kedua model memiliki hasil prediksi yang cukup baik, keakuratan kedua model dibandingkan menggunakan independent sampe t-test berdasarakan error yang dihasilkan. Hasilnya menjukkan bahwa pada tingkat kepercayaan 95% prediksi menggunakan Fuzzy ANN yang telah dioptimasi menggunakan algoritma genetika (M= 0,69, SD= 0,0421) lebih baik secara signifikan dibandingkan degan fuzzy  ANN saja (M= 0.97, SD= 0,04634 ),  t(22 )= 1.71714, p=0.013.

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Published
2021-03-16
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