PREDIKSI PENGGUNA BUS TRANS SARBAGITA DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

  • SLAMET SAMSUL HIDAYAT Faculty of Mathematics and Natural Sciences, Udayana University
  • I PUTU EKA NILA KENCANA Faculty of Mathematics and Natural Sciences, Udayana University
  • KETUT JAYANEGARA Faculty of Mathematics and Natural Sciences, Udayana University

Abstract

Trans Sarbagita is a public transportation services people at Denpasar, Badung, Gianyar and Tabanan. Trans Sarbagita is aimed to resolve a problems caused by accretion volume of vehicles in Bali. This study conducted to forecast the number of Trans Sarbagita passengers in 2013 using ANFIS. The ANFIS system composed by five layers where each layers has a different function and its divide in two phases, i.e. forward and backward phases. The ANFIS uses a hybrid learning algorithm which is a combination of Least Squares Estimator (LSE) on forwards phases and Error Backpropagation (EBP) on the backward phases. The results show, ANFIS with six inputs with M.F of  Pi  produces smallest error, compared to seven and eight input and M.F gauss and generalizedbell. Forecast of Trans Sarbagita passenger numbers in 2013 have to fluctuated every day and the average of passenger’s Trans Sarbagita for a day is 1627 passengers with MSE equal to 10210 and MAPE is 4.01%.

Author Biographies

SLAMET SAMSUL HIDAYAT, Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
I PUTU EKA NILA KENCANA, Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
KETUT JAYANEGARA, Faculty of Mathematics and Natural Sciences, Udayana University
Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University
Published
2013-08-30
How to Cite
HIDAYAT, SLAMET SAMSUL; KENCANA, I PUTU EKA NILA; JAYANEGARA, KETUT. PREDIKSI PENGGUNA BUS TRANS SARBAGITA DENGAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM. E-Jurnal Matematika, [S.l.], v. 2, n. 3, p. 46 - 52, aug. 2013. ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/7321>. Date accessed: 07 dec. 2019. doi: https://doi.org/10.24843/MTK.2013.v02.i03.p048.
Section
Articles

Keywords

ANFIS; fuzzy inference system; Error backpropagation algorithm