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Hasih Pratiwi
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hpratiwi@mipa.uns.ac.id
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ijas@mipa.uns.ac.id
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Study Program of Statistics, Universitas Sebelas Maret, Surakarta 57126, Indonesia
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Kota surakarta,
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INDONESIA
Indonesian Journal of Applied Statistics
Published by Universitas Sebelas Maret
ISSN : -     EISSN : 2621086X     DOI : https://doi.org/10.13057/ijas
Core Subject : Economy, Science, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science
Indonesian Journal of Applied Statistics (IJAS) is a journal published by Study Program of Statistics, Universitas Sebelas Maret, Surakarta, Indonesia. This journal is published twice every year, in May and November. The editors receive scientific papers on the results of research, scientific studies, and problem solving research using statistical method. Received papers will be reviewed to assess the substance of the material feasibility and technical writing.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 62 Documents
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Forecasting Clove Price in South, Central, and North Sulawesi Using Generalized Space Time Autoregressive and Vector Autoregressive Pusporani, Elly; Mardianto, M. Fariz Fadillah; Rahmawati, Nike Meliana; Nariswari, Anggita; Hizbullah, Firqa Aqila
Indonesian Journal of Applied Statistics Vol 9, No 1 (2026)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v9i1.112470

Abstract

Cloves are a strategic plantation commodity in Indonesia with important economic and cultural value, and their price volatility directly affects farmers’ welfare, supply chain stability, and regional economic planning. Although previous studies have shown that the generalized space time autoregressive (GSTAR) model is more flexible than the space time autoregressive (STAR) model for heterogeneous locations, empirical studies comparing GSTAR and vector autoregressive (VAR) models for clove price forecasting across geographically interconnected provinces remain limited. This study addresses that gap by comparing the forecasting performance of GSTAR and VAR for monthly clove prices in North Sulawesi, Central Sulawesi, and South Sulawesi. The novelty of this study lies in the application of GSTAR with three spatial weighting schemes uniform, inverse distance, and cross-correlation normalization and its comparison with VAR in the context of clove price forecasting. Monthly data from January 2015 to December 2024 obtained from the Central Statistics Agency were analyzed using an 80:20 training-testing split. Stationarity testing showed that all series became stationary after first differencing, and lag selection based on the Akaike information criterion identified lag 1 as optimal for both models. The results indicate that the GSTAR(1)I(1) model with cross-correlation normalization weights provides the best forecasting performance, with an average MAPE of 3.18% and RMSE of 5,729.84, outperforming the VARI(1,1) model, which produced an average MAPE of 10.57% and RMSE of 15,214.11. These findings confirm that incorporating spatial dependence significantly improves forecasting accuracy and demonstrates that GSTAR is a more effective model for geographically interconnected commodity markets.Keywords: Love price, forecast, GSTAR, SDGs 8, decent work and economic growth, VAR.
Gold Price Forecasting with Long Short Term Memory (LSTM) and ARIMAX Method Adila, Raisa Naura; Abdurakhman, Abdurakhman
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.97739

Abstract

Gold is very popular investment instrument due to its annual prices increases. In the long term, gold prices follow a nonlinear pattern, but in the short term, there are fluctuations influenced by various factors, including global market dynamics, monetary policy, and overall economic conditions. Therefore, predicting gold prices is an important step in minimizing risk and maximizing profits for investors. In this study, we analyze the performance of two methods for forecasting global gold prices, namely long short term memory (LSTM) and autoregressive integrated moving average with exogenous variables (ARIMAX). Data used is weekly global gold price data from August 1, 2000, to June 1, 2024. The variables used are Close as the dependent variable and Open as the exogenous variable. The data used is stationary data through the differencing process and algorithmic transformation to overcome non-stationarity issues. The best LSTM model uses the Tanh activation function with 30 LSTM units, 10 timesteps, and a dropout of 0.01, resulting in a MAPE value of 5.323%. The best ARIMAX model obtained was the ARIMAX (0,1,1) model, with a MAPE value of 0.55% for the test data and 0.61% for the training data. The research results, indicate that the higher accuracy of ARIMAX reflects its suitability for linear data such as gold prices, but the accuracy of LSTM which is below 10% still performs well for more complex data patterns.Keywords: gold price; forecasting; LSTM; arimax.

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