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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Mathematical and Fundamental Sciences Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Procuratio : Jurnal Ilmiah Manajemen Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Ilmiah Sains Indonesian Journal of Applied Mathematics
Utriweni Mukhaiyar
Institut Teknologi Bandung
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Mathematics Physics Other

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Spatial Weighting Selection in GSTAR and S-GSTAR Models for Temperature Prediction Riani Utami; Utriweni Mukhaiyar; Nabila Mardiyah; Yalela Sa’adah; Erni Widyawati
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.34305

Abstract

Recent research in time series analysis indicates that events at a particular location are not only influenced by events at previous times but also by proximity between locations. Events influenced by both space and time can be modeled using a space-time model. GSTAR model is one such space-time model. In its development, time series data exhibiting seasonal patterns are modeled using Seasonal GSTAR (S-GSTAR). The GSTAR and S-GSTAR models are used to model temperature in the Banjar, Cilacap, and Sleman Districts. The purpose of employing both methods is to compare the best model for modeling temperature at these three locations. Spatial weights used include inverse distance weighting using the Euclidean distance formula, uniform weighting, and cross-correlation normalization weighting. Ordinary Least Squares (OLS) is the estimation method used in this study. The best model obtained is S-GSTAR  with inverse distance weighting, as this model has the smallest RMSE value.
Comparison of Stock Prediction Using ARIMA Model with Multiple Interventions of Step and Pulse Functions Muh. Qodri; Utriweni Mukhaiyar; Vira Ananda; Siti Maisaroh
Jurnal Ilmiah Sains Volume 24 Issue 1, April 2024
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/jis.v24i1.51269

Abstract

Stock price predictions based on technical analysis using historical data help investors determine the optimal time to buy or sell shares with the aim of achieving maximum profits. The aim of this research is to compare the results of Kimia Farma's share price predictions using the ARIMA model with intervention analysis of two variables at once, namely the pulse function and the step function. This is the novelty of this research. The data used in this research is daily data on Kimia Farma shares from the period 16 April 2018 to 14 April 2023. The best model produced is ARIMA (0,1,1) with intervention, shown by a MAPE value of 0.3356% and an RMSE of 0.3356%. 4.03. Kimia Farma's share price prediction for the next five days is 906.5548; 905.7875; 905.0206; 904.2542; 903.4882 rupiah. An increase in share prices occurred after the intervention in the period 15 April 2023 to 19 April 2023. Keywords: ARIMA; intervention model; step and pulse function; kimia farma
Co-Authors Alfairus, Muh. Qodri Dradjad Irianto Dwi Rianti Erni Widyawati Fachrur Rozi Ferdy Rontos Karina Ayudhia Sasmito Kurnia Handoko Mardianto, Lutfi Nabila Mardiyah Riani Utami Salim Salim Salma Kardiyanti Siti Maisaroh Sonhaji, Abdulah Sunyanti Sunyanti Syahri Ramadhani Trimono, Trimono Udjianna S. Pasaribu Udjianna Sekteria Pasaribu Vira Ananda Yalela Sa’adah Yundari, Yundari