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All Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Parameter: Jurnal Matematika, Statistika dan Terapannya
Hendri, Eko Primadi
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Decision Sciences, Operations Research & Management Education Mathematics

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MODEL PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLLAR SINGAPURA MENGGUNAKAN METODE HYBRID ARIMA-ANN Fadhlia, Sarah; Hendri, Eko Primadi; Cahyaningtyas A, Deasy Dwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.720

Abstract

This research aims to predict the Rupiah exchange rate against the Singapore Dollar using the hybrid ARIMA-ANN method. The hybrid model is used to increase prediction accuracy by utilizing the ARIMA model to capture linear patterns and the ANN model to capture non-linear patterns. The data used in this research is data on the Rupiah exchange rate against the Singapore Dollar. The ARIMA model used for hybrid modeling is ARIMA (1,1,1) because it has an AIC value of 2144.93 which is smaller than other ARIMA models. The residuals from the ARIMA model (1,1,1) are used for ANN modeling. ANN modeling uses 3 inputs, 1-10 hidden layers, and 1 output layer. Based on the analysis results, the ARIMA (1,1,1) - ANN (3,10,1) hybrid model has an RMSE value of 52.092 which is smaller than other ARIMA-ANN hybrid models. Therefore, the hybrid ARIMA (1,1,1) - ANN (3,10,1) model is more effective in predicting the Rupiah exchange rate against the Singapore Dollar.
Time Series Clustering of Rice Productivity Using Trimming Gaussian Mixture Models Fadhlia, Sarah; Hendri, Eko Primadi
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp381-394

Abstract

This study investigates the application of the Trimming Gaussian Mixture Model (TGMM) for clustering monthly rice productivity time series data in West Java from 2018 to 2023. TGMM is a robust clustering approach that reduces the influence of outliers by trimming a specified portion of the data prior to parameter estimation. The dataset, sourced from Open Data Jabar, was analyzed to identify the most representative number of clusters using the Silhouette Score. The optimal clustering solution was achieved with two main clusters (k = 2) and a trimming proportion of 15%. The results revealed three distinct regional groups: two dominant clusters characterized by moderate-stable and high-consistent productivity patterns, and a separate group of outliers marked by low and highly fluctuating productivity. Cluster stability was assessed using the Adjusted Rand Index (ARI), yielding values of 0.41 (bootstrap) and 0.545 (subsampling), which indicate a reasonably consistent clustering structure. These findings demonstrate the effectiveness of TGMM in capturing underlying productivity patterns while accounting for noise and outliers, suggesting its potential as a robust decision-support tool for data-driven agricultural planning and policy formulation.
Co-Authors Cahyaningtyas A, Deasy Dwi Fadhlia, Sarah