Herni Utami
Department of Mathematics Gadjah Mada University, Indonesia

Published : 8 Documents
Articles

Found 8 Documents
Search

PERAMALAN BEBAN LISTRIK DAERAH ISTIMEWA YOGYAKARTA DENGAN METODE SINGULAR SPECTRUM ANALYSIS (SSA) Utami, Herni; Sari, Yunita Wulan; Subanar, Subanar; Abdurakhman, Abdurakhman; Gunardi, Gunardi
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (740.583 KB) | DOI: 10.14710/medstat.12.2.214-225

Abstract

This paper will study forecasting model for electricity demand in Yogyakarta and forecast it for 2019 until 2024. Usually, electricity demand data contain seasonal. We propose Singular Spectral Analysis-Linear Recurrent Formula (SSA-LRF) method. The SSA process consists of decomposing a time series for signal extraction and then reconstructing a less noisy series which is used for forecasting. The SSA-LRF method will be used to forecast h-step ahead. In this study, we use monthly electricity demand in Yogyakarta for 11 year (2008 to 2018). The forecasting results indicates that the forecast using window length of L=26 have good performance with MAPE of 1.9%.
Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model Sulandari, Winita; Subanar, Subanar; Suhartono, Suhartono; Utami, Herni
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.69

Abstract

Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast
PENGARUH SUATU DATA OBSERVASI DALAM MENGESTIMASI PARAMETER MODEL REGRESI Utami, Herni; I, Ruri; abdurrakhman, abdurrakhman
MATEMATIKA Vol 5, No 3 (2002): Jurnal Matematika
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (54.254 KB)

Abstract

Observasi yang mempengaruhi model regresi sedemikian hingga elipsoid konfidensi untuk estimasi parameter regresinya menjadi kecil apabila observasi tersebut ?dihilangkan? adalah observasi penting.  Sehingga observasi penting tersebut bisa merupakan observasi berpengaruh sesungguhnya atau bisa juga sebagai outlier. Salah satu cara menentukan observasi ke-i penting atau tidak, melihat  elipsoid konfidensi parameter model regresi linear dengan ?menghilangkan?  observasi tersebut.
PERAMALAN NILAI TUKAR DOLAR AMERIKA TERHADAP INDONESIA DENGAN MODEL MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM-AUTOREGRESSIVE MOVING AVERAGE Farima, Vega Zayu; Utami, Herni
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 1 (2018): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Beberapa permasalahan dalam kehidupan sehari-hari perlu untuk diramalkan sebelum diambil keputusan. Nilai tukar mata uang asing yang mempengaruhi kurs Indonesia seperti nilai tukar dolar Amerika sangat perlu diramalkan untuk jangka waktu tertentu. Data kurs memiliki volatilitas yang sangat tinggi dan cenderung tidak stasioner. Transformasi wavelet mampu merepresentasikan informasi waktu dan frekuensi secara bersamaan sehingga dapat digunakan untuk menganalisis data-data nonstasioner. MODWT-ARMA yaitu model hibrid dari Maximal Overlap Discrete Wavelet Transform (MODWT) dan Autoregressive Moving Average (ARMA) yang berhubungan dengan data runtun waktu nonstasioner. Secara teori, nilai detail yang diperoleh dari dekomposisi MODWT adalah stasioner. Hal ini menyebabkan hasil dekomposisi dapat diramalan dengan ARMA. Pada peramalan nilai tukar dolar Amerika terhadap rupiah, diperoleh pemodelan yang fitted dengan data training dan diperoleh nilai MAPE yang kecil yaitu 0.82%. Hal ini mengindikasikan bahwa model gabungan ini efektif untuk menambah keakuratan peramalan.  Kata kunci : Peramalan, Data Runtun Waktu, Dekomposisi, MODWT-ARMA, MAPE.
SECOND ORDER LEAST SQUARE ESTIMATION ON ARCH(1) MODEL WITH BOX-COX TRANSFORMED DEPENDENT VARIABLE Utami, Herni; -, Subanar
Journal of the Indonesian Mathematical Society Volume 19 Number 2 (October 2013)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.19.2.166.99-110

Abstract

Box-Cox transformation is often used to reduce heterogeneity and to achieve a symmetric distribution of response variable. In this paper, we estimate the parameters of Box-Cox transformed ARCH(1) model using second-order leastsquare method and then we study the consistency and asymptotic normality for second-order least square (SLS) estimators. The SLS estimation was introduced byWang (2003, 2004) to estimate the parameters of nonlinear regression models with independent and identically distributed errors.DOI : http://dx.doi.org/10.22342/jims.19.2.166.99-110
Estimating the function of oscillatory components in SSA-based forecasting model Sulandari, Winita; Subanar, Subanar; Suhartono, Suhartono; Utami, Herni; Lee, Muhammad Hisyam
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.312

Abstract

The study of SSA-based forecasting model is always interesting due to its capability in modeling trend and multiple seasonal time series. The aim of this study is to propose an iterative ordinary least square (OLS) for estimating the oscillatory with time-varying amplitude model that usually found in SSA decomposition. We compare the results with those obtained by nonlinear least square based on Levenberg Marquardt (NLM) method. A simulation study based on the time series data which has a linear amplitude modulated sinusoid component is conducted to investigate the error of estimated parameters of the model obtained by the proposed method. A real data series was also considered for the application example. The results show that in terms of forecasting accuracy, the SSA-based model where the oscillatory components are obtained by iterative OLS is nearly the same with that is obtained by the NLM method.
PERAMALAN NILAI TUKAR DOLAR AMERIKA TERHADAP INDONESIA DENGAN MODEL MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM-AUTOREGRESSIVE MOVING AVERAGE Farima, Vega Zayu; Utami, Herni
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 6, No 1 (2018): Jurnal Statistika
Publisher : Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Muham

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Beberapa permasalahan dalam kehidupan sehari-hari perlu untuk diramalkan sebelum diambil keputusan. Nilai tukar mata uang asing yang mempengaruhi kurs Indonesia seperti nilai tukar dolar Amerika sangat perlu diramalkan untuk jangka waktu tertentu. Data kurs memiliki volatilitas yang sangat tinggi dan cenderung tidak stasioner. Transformasi wavelet mampu merepresentasikan informasi waktu dan frekuensi secara bersamaan sehingga dapat digunakan untuk menganalisis data-data nonstasioner. MODWT-ARMA yaitu model hibrid dari Maximal Overlap Discrete Wavelet Transform (MODWT) dan Autoregressive Moving Average (ARMA) yang berhubungan dengan data runtun waktu nonstasioner. Secara teori, nilai detail yang diperoleh dari dekomposisi MODWT adalah stasioner. Hal ini menyebabkan hasil dekomposisi dapat diramalan dengan ARMA. Pada peramalan nilai tukar dolar Amerika terhadap rupiah, diperoleh pemodelan yang fitted dengan data training dan diperoleh nilai MAPE yang kecil yaitu 0.82%. Hal ini mengindikasikan bahwa model gabungan ini efektif untuk menambah keakuratan peramalan.  Kata kunci : Peramalan, Data Runtun Waktu, Dekomposisi, MODWT-ARMA, MAPE.
ANALISIS REGRESI TERPOTONG Utami, Herni
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.v4i2.885

Abstract

Analisis regresi terpotong merupakan pengembangan dari analisis regresi klasik dengan menambah konstanta pemotongtertentu. Misalkan variabel random y menyatakan variabel dependen/respon dan x1, x2 , ... xp merupakan variabelindependen, maka model regresi klasik adalah y = x¢b + e dengan asumsi e ~ N(0,s2). Akibatnya y juga berdistribusiNormal dengan mean E(y | x) = x¢b dan variansi s2. Selanjutnya jika harga y > a maka diperoleh regresi terpotong denganmean E(y | y > a) = x¢b + sl dan variansi var(y) = s2[1 - s] dengan l = f (a) / F (a), a = ( x¢b - a)/s, dan d = l(l-a)(Greene, 1997) [?].