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All Journal FORUM STATISTIKA DAN KOMPUTASI Statistika Jurnal Matematika & Sains
Brodjol Sutijo
Department of Statistics, Faculty of Mathematics and Natural Sciences Institut Teknologi Sepuluh Nopember
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics

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Forecasting Tourism Data Using Neural Networks - Multiscale Autoregressive Model Brodjol Sutijo; Suhartono Suhartono; Alfonsus J. Endharta
Jurnal Matematika & Sains Vol 16, No 1 (2011)
Publisher : Institut Teknologi Bandung

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Abstract

Neural Networks - Multiscale Autoregressive (NN-MAR) model is a development of neural network. The network is built by using wavelet theories for time series forecasting. There are few research explaining how NN-MAR model can be used for forecasting seasonal time series data. The main aspect for forecasting seasonal time series data is the lag inputs, which should include seasonal lags. The procedure starts from Maximal Overlap Discrete Wavelet Transform (MODWT) decomposition. For non-stationary data, the differencing process is used to get a stationary data. From the decomposition process we get the scale and wavelet coefficients. The lags of these coefficients are used as the inputs in the network. In the hidden layer, the number of hidden neurons is chosen by using the criterion of R2incremental and F-test, so that we get the best NN-MAR model. The aim of this research is to build NN-MAR model for seasonal time series data, such as tourism data. The number of international tourist coming to Soekarno-Hatta airport in Jakarta and to Ngurah Rai airport in Bali are used as the case study.
PEMODELAN RESIKO PENYAKIT KAKI GAJAH (FILARIASIS) DI PROVINSI PAPUA DENGAN REGRESI ZERO-INFLATED POISSON Sri Pingit Wulandari; Brodjol Sutijo; Ika Rahmawati
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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The goverment has established elimination of filariasis tropical disease as one of the priority programs. One of the districts that has become a target is Papua. The total amount of  filariasis victim on every regency/city in Papua district can be assumed to follow a Poisson distribution. So Poisson regression method is a suitable method to know the influence factor of filariasis disease. Poisson regression model assumes equidispersion, that is equality of mean and variance of the response variable. Overdispersion test shows that the variance of the response variable exceeds its mean value. So the model is modified into zeroinflated Poisson (ZIP) regression model (logit and log). ZIP logit regression model shows that the quantity of filariasis victim in every regency/city in Papua district with zero count is influenced by the percentage of household members who sleep inside mosquito net, the percentage of household members who sleep inside insecticide musquito net, and the percentage of house-holds who keep pet (dog/cat/rabbit). While ZIP regression on log model shows that the increasing number of percentage household who keeps their pet will enhance the quantity of filariasis victim  in Papua district as many as two people. Regencies/cities which need to get special attention through an elimination program of filariasis are Asmat, Tolikara, Supiori, Yapen Waropen, and Jayapura city.
UJI NONLINEARITAS YANG DIABAIKAN DALAM TIME SERIES Brodjol Sutijo; Subanar Subanar
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.874

Abstract

Dalam makalah ini akan dibahas tentang pengujian nonlinearitas didasarkan pada pendekatan Neural Network (NN) yang dikemukakan oleh Lee dan White untuk kondisi nonlinear yang terabaikan pada model time series. Pada uji neural network ini, dikembangkan dari model Feedforward neural network dengan menambahkan hubungan langsung dari input ke output. Uji ini akan dibandingkan dengan uji Tsay dan didasarkan pada studi simulasi, baik untuk model linear maupun model nonlinear. Pendekatan uji dengan neural network adalah pendekatan lagrange multiplier, sedangkan uji Tsay didasarkan pada pendekatan regresi dengan menambahkan perkalian komponen dari variabel prediktor. Hasil simulasi secara umum menunjukkan jika model yang dibentuk adalah model linear, kekuatan uji nonlinearitasnya rendah, sedangkan jika yang dibentuk adalah model nonlinear, maka kekutan uji nonlinearnya tinggi. Hasil ini berlaku untuk metode White maupun Tsay.
Co-Authors Alfonsus J. Endharta Ika Rahmawati Sri Pingit Wulandari subanar subanar Suhartono Suhartono