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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Putri, Mega Ramatika
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Computer Science & IT Mathematics

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Peramalan Harga Emas Berjangka Menggunakan Metode ARIMA-GARCH Hasanah, Mauizatun; Putri, Mega Ramatika; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.32723

Abstract

Gold futures price forecasting plays an important role in investment decision-making and risk management, especially in the midst of volatile commodity market dynamics. This research aims to build an accurate gold futures price forecasting model by combining Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The ARIMA model is used to capture linear patterns and historical trends in time series data, while the GARCH model is able to handle the high volatility characteristic of gold price movements, something that conventional forecasting models often fail to capture. The data used in this study is daily gold futures price data collected over the period January 3, 2023 to March 31, 2025, which covers both normal market conditions and periods of turmoil, making it relevant to describe the overall market dynamics. The forecasting results show that the ARIMA-GARCH model with components (3,1,3) (1,1) with a MAPE of 4.52% indicates a good level of accuracy in the context of forecasting gold futures prices that have high volatility. Thus, this model provides precise forecasting results with actual data so that it can be used by market participants and policy makers in managing risks and designing strategies.
Household Clustering in West Java Based on Stunting Risk Factors Using K-Modes and K-Prototypes Algorithms Yusran, Muhammad; Nuradilla, Siti; Putri, Mega Ramatika; Fitrianto, Anwar; Yudhianto, Rachmat Bintang
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11508

Abstract

Stunting remains one of Indonesia’s most persistent public health challenges, with West Java contributing the highest number of cases due to its large population and regional disparities in household welfare. Identifying household groups vulnerable to stunting is essential for designing targeted interventions that integrate nutrition, sanitation, and socio-economic development. This study introduces a data-driven clustering framework using the K-Modes and K-Prototypes algorithms to classify 22,161 households in West Java based on 26 indicators from the March 2024 National Socioeconomic Survey (SUSENAS), encompassing food security, sanitation, drinking water access, economic conditions, social assistance, and demographics. The K-Modes algorithm was applied to categorical data, while K-Prototypes integrated numerical and categorical variables, with parameter optimization performed using a grid search and the Elbow method. Clustering performance was evaluated through the Silhouette Score, Calinski–Harabasz Index, and Davies–Bouldin Index, followed by a bootstrapped stability analysis employing the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Results show that K-Prototypes outperformed K-Modes, yielding a higher Silhouette Score (0.6681 compared to 0.2922), higher CH Index (13,890.6 compared to 3,976.1), and lower DBI (0.4607 compared to 1.5274), indicating superior compactness and separation. Stability testing confirmed strong robustness, with mean ARI = 0.959 and mean NMI = 0.932 across 50 bootstrap replications. The optimal five-cluster structure identified distinct socioeconomic groups, with the highest stunting risk found among households with low income, limited housing space, inadequate sanitation, and more children under five. The findings highlight the effectiveness of K-Prototypes in modeling mixed-type data and support the design of evidence-based, regionally adaptive stunting reduction strategies aligned with Presidential Regulation No. 72/2021 on the Acceleration of Stunting Reduction.
Co-Authors Fitrianto, Anwar Hasanah, Mauizatun Mualifah, Laily Nissa Atul Muhammad Yusran NURADILLA, SITI Yenni Angraini Yudhianto, Rachmat Bintang