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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Zeta - Math Journal
Pratama, Fachriza Yosa
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Analisis Korelasi Kanonik Terhadap Hubungan Faktor Meteorologi dengan Produksi Tanaman Perkebunan Baihaqi, Mochammad; Dwiyanto, Adelia Sukma; Rahmada, Indrastanto Oktodian; Pratama, Fachriza Yosa; Mardianto, M. Fariz Fadillah; Amelia, Dita; Ana, Elly
Zeta - Math Journal Vol 9 No 2 (2024): November
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2024.9.2.73-82

Abstract

Tanaman perkebunan merupakan tanaman yang hasil panennya bisa berbeda-beda tergantung dengan keadaan udara yang termasuk indikator meteorologi. Untuk mengetahui hal tersebut dapat menggunakan analisis korelasi kanonik. Penelitian yang dilakukan menggunakan data sekunder, yakni data yang berasal dari sumber yang telah ada.Hasil pencatatan dari faktor meteorologi dan produksi tanaman perkebunan menurut Provinsi pada tahun 2015 digunakan sebagai data sekunder yang akan dianalisis. Data yang diambil yaitu data faktor meteorologi yang terdiri dari suhu (Y1), kelembaban (Y2), curah hujan (Y3), penyinaran matahari (Y4), tekanan udara (Y5) dan data produksi tanaman perkebunan yang terdiri dari Kelapa Sawit (X1­), kelapa (X2), karet (X3), kopi (X4), kakao (X5). Tujuan melakukan penelitian ini untuk memberikan informasi adakah atau tidak adakah pengaruh faktor meteorologi dengan produksi tanaman perkebunan dan juga mengetahui faktor meteorologi dengan pengaruh terbesar atau terbaik terhadap produksi tanaman perkebunan. Dari hasil analisis korelasi kanonik diperoleh hasil bahwa hubungan faktor meteorologi dan produksi tanaman perkebunan, variabel indikator pada variabel faktor meteorologi yang paling dominan adalah tekanan udara dengan pengaruhnya terhadap produksi tanaman perkebunan sebesar 96.2%. Sedangkan variabel produksi tanaman perkebunan yang dominan adalah variabel kelapa sawit dengan nilai korelasi yang paling tinggi yaitu sebesar 1.129.
Comparative Analysis of Local Polynomial Regression and ARIMA in Predicting Indonesian Benchmark Coal Price Mahadesyawardani, Arinda; Maulidya, Utsna Rosalin; Marbun, Barnabas Anthony Philbert; Pratama, Fachriza Yosa; Chamidah, Nur
PYTHAGORAS Jurnal Matematika dan Pendidikan Matematika Vol. 19 No. 1: June 2024
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v19i1.74889

Abstract

As one of the world's biggest coal producers, it is essential for Indonesia to follow the trend of benchmark coal price fluctuations for any future possibilities. This study compared two methods of forecasting benchmark coal prices to evaluate the accuracy of the predictions used a nonparametric regression based on the local polynomial estimator and a parametric ARIMA method. Local polynomial analysis obtained a MAPE of 2.929278% using a CV method based on optimal bandwidth of 5.06 at order 2 with a cosine kernel, which means highly accurate forecasting accuracy. As for the ARIMA analysis, the data does not meet the assumption of normality, but forecasting is still continued with the best model ARIMA (1,2,1) model so that the MAPE is 12.6327%, which means good forecasting accuracy. Therefore in this study, the use of nonparametric regression methods using local polynomial estimators on data with non-normal distribution are more suitable to obtain accurate prediction results.
FORECASTING THE INFLATION RATE IN INDONESIA USING ARIMA-GARCH MODEL Saifudin, Toha; Suliyanto, Suliyanto; Afifa, Fitriana Nur; Arrofah, Aini Divayanti; Fauzi, Doni Muhammad; Pratama, Fachriza Yosa; Adyatma, Isryad Yoga
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp0955-0970

Abstract

Inflation is a key economic indicator that affects purchasing power, economic growth, and financial stability. Accurate forecasting is essential for policymakers to implement effective monetary and fiscal policies. However, traditional models like ARIMA (Autoregressive Integrated Moving Average) mainly capture general trends and often fail to address inflation volatility. This study enhances inflation forecasting accuracy by applying the ARIMA-GARCH hybrid model, which combines trend estimation with volatility modelling. Focusing on Indonesia’s inflation patterns using recent data, it addresses a gap in existing research. This type of research uses quantitative methods, and the data were obtained from the official website of Bank Indonesia. The dataset consists of 240 monthly Indonesian inflation data points spanning from September 2004 to August 2024. The ARIMA (0,1,1)-GARCH (2,0) model is used to analyze inflation trends and volatility dynamics. The model evaluation shows strong predictive performance, with a Mean Absolute Percentage Error (MAPE) of 2.73% and Root Mean Squared Error (RMSE) of 0.74 for training data. Testing data results in a MAPE of 18.95% and RMSE of 0.702, which remains within an acceptable range. These findings highlight the importance of incorporating volatility modelling in inflation forecasting to enhance economic decision-making. A reliable forecast mitigates economic uncertainty, thereby providing a stronger foundation for achieving long-term economic growth. This study contributes by demonstrating the practical application of ARIMA-GARCH in Indonesia’s inflation modelling, providing valuable insights for policymakers in managing inflation-related risks.
Co-Authors Adyatma, Isryad Yoga Afifa, Fitriana Nur Arrofah, Aini Divayanti Dita Amelia Dwiyanto, Adelia Sukma Elly Ana Fauzi, Doni Muhammad M. Fariz Fadillah Mardianto Mahadesyawardani, Arinda Marbun, Barnabas Anthony Philbert Maulidya, Utsna Rosalin Mochammad Baihaqi Nur Chamidah Rahmada, Indrastanto Oktodian Suliyanto Suliyanto Toha Saifudin