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METODE GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) DENGAN ESTIMASI GENERALIZED LEAST SQUARE (GLS) Rahma Aulia Ar Raniri; Sudarno Sudarno; Puspita Kartikasari
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.131-142

Abstract

A model that combines both time and location factors in a multivariate time series called space time model. Generalized Space Time Autoregressive (GSTAR) is one of the space-time models that can be utilized in data forecasting. The GSTAR model is a development method of Space Time Autoregressive (STAR) that can be used for heterogeneous locations. The GSTAR model is used to forecast data in multiple locations at once. Generalized Least Squares (GLS) is one of the estimation methods that can be used in the GSTAR model. The GLS method is used on data that has residuals that are correlated across equations. This study applies GSTAR model to forecast farmer exchange rates of horticultural subsector in West Java, Yogyakarta, East Java, and Banten using GSTAR-GLS (11)I(1) model with uniform, invers distance, and cross-correlation normalization weights. The analysis result for model GSTAR-GLS (11)I(1) with three weighted methods shows that the best forecast result is using uniform weights with SMAPE for West Java, Yogyakarta, East Java, and Banten are 2.85%; 3.63%; and 1.92% or the forecasting result is highly accurate.
PENGENDALIAN KUALITAS PUPUK NITROGEN, PHOSPAT, KALIUM (NPK) PELANGI FUSION DI PT PUPUK KALIMANTAN TIMUR MENGGUNAKAN PETA KENDALI MEWMA & MEWMV Bungan Tcania Paulina Grace; Puspita Kartikasari; Deby Fakhriyana
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.110-120

Abstract

The NPK Pelangi Fusion fertilizer is one of the main products manufactured by PT Pupuk Kalimantan Timur. NPK fertilizer is a blend of various plant nutrients, primarily Nitrogen, Phospat, and Kalium, aimed at enhancing crop yields. However, deviations from the NPK fertilizer specifications can lead to inconsistent plant growth and low harvest yields. Statistical Process Control (SPC) is a method used to process data and monitor production processes using statistical techniques, with the goal of detecting changes in process performance through the use of control charts. In this study, the Multivariate Exponentially Weighted Moving Variance (MEWMV) control chart is used to monitor the variance of the production process due to its optimal performance in detecting small variance shifts. Additionally, the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is used to monitor the mean of the NPK Pelangi Fusion production process, as it quickly detects subtle shifts in variance. The analysis results indicate that the optimal weighting for the MEWMV control chart is ω=0.2 and λ=0.4, resulting in an Average Run Length (ARL) of 370. Similarly, the optimal weighting for the MEWMA control chart is λ=0.06, with an upper control limit of H=9.80 and an ARL of 200. This study concludes that the mean and variance of the multivariate production process have been effectively controlled.