Atiek Iriany
brawijaya university

Published : 3 Documents

Found 3 Documents

CROSS-COVARIANCE WEIGHT OF GSTAR-SUR MODEL FOR RAINFALL FORECASTING IN AGRICULTURAL AREAS Sulistyono, Agus Dwi; Hartawati, Hartawati; Suryawardhani, Ni Wayan; Iriany, Atiek; Iriany, Aniek
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 6, No 2 (2020): CAUCHY: Jurnal Matematika Murni dan Aplikasi
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v6i2.7544


The use of location weights on the formation of the spatio-temporal  model contributes to the accuracy of the model formed. The location weights that are often used include uniform location weight, inverse distance, and cross-correlation normalization. The weight of the location considers the proximity between locations. For data that has a high level of variability, the use of the location weights mentioned above is less relevant. This research was conducted with the aim of obtaining a weighting method that is more suitable for data with high variability. This research was conducted using secondary data derived from 10 daily rainfall data obtained from BMKG Karangploso. The data period used was January 2008 to December 2018. The points of the rain posts studied included the rain post of the Blimbing, Karangploso, Singosari, Dau, and Wagir regions. Based on the results of the research forecasting model obtained is the GSTAR ((1), 1,2,3,12,36) -SUR model. The cross-covariance model produces a better level of accuracy in terms of lower RMSE values and higher R2 values, especially for Karangploso, Dau, and Wagir areas.
Penerapan Bagan Kendali Multivariat Robust Pada Data Produksi Pupuk ZK PT Petrokimia Gresik Darmanto, Darmanto; Kusdarwati, Heni; Iriany, Atiek; Setiawan, Iwan; Ashari, Ayu Aisyah
Performa: Media Ilmiah Teknik Industri Vol 17, No 1 (2018): PERFORMA Vol. 17, No 1 Maret 2018
Publisher : Program Studi Teknik Industri, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/performa.17.1.18514


PT Petrokimia Gresik is the most complete fertilizer producer in Indonesia and one of its production is ZK fertilizer. There are five measurable chemicals that correlate to form ZK fertilizer ie H2O, H2SO4, K2O, SO3 and Cl-. ZK fertilizer monitoring process has not been statistically done by PT Petrokimia Gresik, either univariat or multivariate. Since ZK fertilizer is composed of five chemicals that correlate each other, a multivariate control chart is used. RMCD is one of the robust parameter estimation methods for outlier data. The average vector and variance-covariance matrix derived from the RMCD method is used to calculate the statistics on the multivariate control chart. Therefore, the robust control chart is more sensitive to detecting a shift in production processes compared to the classical ones. The data used in Phase I is daily data per January 1 - April 30, 2017, while Phase II data used is daily data as of May 1 - July 15, 2017. The results of the control chart analysis in Phase I shows that the production process has not been controlled statistically analysis of cause-effect diagrams. Furthermore, the control chart limits in Phase I that have been stable after the repair are used for Phase II production data. The result of the control chart analysis in Phase II shows that the production process has shifted. This can be known by the number of points that out of control.
The Journal of Experimental Life Science Vol 8, No 2 (2018)
Publisher : Graduate School, University of Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jels.2018.008.02.01


Geographically and Temporally weighted regression (GTWR) modeling has been developed to evaluate spatial heterogeneity and temporal heterogeneity in factors influencing the spread of dengue fever in Malang city. By using the monthly data in 2012-2015 as the temporal unit of each urban village in Malang and village is considered as a spatial unit. GTWR model is compared with the GWR model using several statistical criteria. GTWR model shows that the relationship between dengue incidence with population density and monthly average temperature significantly affects each Village in Malang.Keywords : DHF, GTWR, Spatiotemporal Pattern