Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Media Statistika
Subanar Subanar
Departemen Matematika, FMIPA, Universitas Gadjah Mada
Decision Sciences, Operations Research & Management

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
PERAMALAN BEBAN LISTRIK DAERAH ISTIMEWA YOGYAKARTA DENGAN METODE SINGULAR SPECTRUM ANALYSIS (SSA) Herni Utami; Yunita Wulan Sari; 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%.
PERAMALAN DATA PENUMPANG KERETA API DENGAN MENGGUNAKAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM- RECURRENT NEURAL NETWORK (MODWT-RNN) Mira Andriyani; Subanar Subanar
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 (498.176 KB) | DOI: 10.14710/medstat.12.2.164-174

Abstract

The train is one of the public transportation that is very popular because it is affordable and free of congestion. There is often a buildup of passengers at the station so that it sometimes causes a accumulation of passengers at the station and makes the situation at the station to be not conducive. In order to avoid a buildup of passengers, forecasting the number of passengers can be done. Forecasting is determined based on data in previous times. Data of train passengers in Java (excluding Jabodetabek) forms a non-stationary and contains nonlinear relationships between the lags. One of the nonlinear models that can be used is Recurrent Neural Network (RNN). Before RNN modeling, Maximal Overlap Wavelet Transform (MODWT) was used to make data more stationary. Forecasting model of train passengers in Java excluding Jabodetabek, Indonesia using MODWT-RNN results forecasting with RMSE is 252.85, while RMSE of SARIMA and RNN are 434.97 and 320.48. These results indicate that the MODWT-RNN model gives a more accurate result thanS ARIMA and RNN.
PERSAMAAN DIFERENSIAL ORNSTEIN-UHLENBECK DALAM PERAMALAN HARGA SAHAM Amam Taufiq Hidayat; Subanar Subanar
MEDIA STATISTIKA Vol 13, No 1 (2020): 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 (383.443 KB) | DOI: 10.14710/medstat.13.1.60-67

Abstract

Geometric Brownian motion is one of the most widely used stock price model. One of the assumptions that is filled with stock return volatility is constant. Gamma Ornstein-Uhlenbeck process a model to describe volatility in finance. Additionally, Gamma Ornstein-Uhlenbeck process driven by Background Driving Levy Process (BDLP) compound Poisson process and the marginal law of volatility follows a Gamma distribution. Barndorff-Nielsen and Shepard (BNS) Gamma Ornstein-Uhlenbeck model can to sample the process for the stock price with volatility follows Gamma Ornstein-Uhlenbeck process. Based on these, the simulation result are compared BNS Gamma Ornstein-Uhlenbeck model with geometric Brown motion for Standard and Poor (SP) 500 stock data. Simulation result give BNS Gamma Ornstein-Uhlenbeck model and Geometric Brownian motion a Root Mean Square Error (RMSE) are 0,13 and 0,24 respectively. These result indicate that the BNS Gamma  Ornstein-Uhlenbeck model gives a more accurate  than Geometric Brownian motion
Co-Authors Abdurakhman Abdurakhman Gunardi . Herni Utami Hidayat, Amam Taufiq Mira Andriyani Yunita Wulan Sari