PENERAPAN METODE EXTENDED KALMAN FILTER PADA KASUS PERTUMBUHAN PENDUDUK KABUPATEN JEMBER - Rory Ronella Agustin; Kosala Dwidja Purnomo; Alfian Futuhul Hadi (Prodi Matematika FMIPA Unirow Tuban, 2019)
This study discusses the estimated number of people using the methods of Jember Regency Extended Kalman Filter (EKF) and determine the appropriate logistic growth model for predicting the next populations in Jember. There are two assumptions logistic growth model will be compared, first is logistic growth model assuming a linear populations function and the second is logistic growth model assuming parabolic populatins function. To determine efficiency of Extended Kalman Filter conducted trial process, using 6, 14, 28 measurements data. Each data taken from Central Statistic Agency of East Java Province during 1990-2017. Finally, this study indicate that the logistic growth model assuming parabolic populations function is an appropiate better than logistic growth model assuming a linear populations for populations in Jember during 1990-2017. The Extended Kalman Filter method is able to increase the confidence level of the estimation results indicated by getting smaller of average norm covariance error. More data used, the estimation results using Extended Kalman Filter method are getting better and closer to the real data.