Atiek Iriany
brawijaya university

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Geographically and Temporally Weighted Regression Modeling in Analyzing Factors Affecting the Spread of Dengue Fever in Malang Indrayani, Fahmi; Pramoedyo, Henny; Iriany, Atiek
The Journal of Experimental Life Science Vol 8, No 2 (2018)
Publisher : Graduate School, University of Brawijaya

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Abstract

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
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

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Abstract

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.