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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika JTAM (Jurnal Teori dan Aplikasi Matematika) Zeta - Math Journal
Pratama, Fachriza Yosa
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Education Mathematics

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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.
Air Temperature Prediction in Sleman Yogyakarta using Fourier Series and Markov Switching Syahzaqi, Idrus; Riefky, Muhammad; Cahyoko, Fajar Dwi; Nahar, Muhammad Hafidzuddin; Pratama, Fachriza Yosa; Mardianto, Muhammad Fariz Fadillah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i2.35371

Abstract

Global warming increases the urgency of accurate local temperature forecasting, particularly in Sleman, Yogyakarta, a region characterized by diverse topography and high exposure to climate-related risks such as volcanic activity, agricultural vulnerability, and rapid urbanization. Such conditions increase the urgency for localized predictive models that can support agricultural planning, energy management, and disaster preparedness. This research used quantitative approach with a comparative predictive modelling design to predict the weekly average air temperature in Sleman by comparing two models: the Fourier Series regression and the Markov Switching Autoregressive (MSAR) model. The Fourier Series was selected for its ability to capture smooth seasonal and periodic behavior typical of climatological data, whereas the MSAR model was employed to accommodate regime shifts and nonlinear structural variations. The dataset comprises 127 weekly observations from January 2023 to June 2025 (BMKG), the data were split into 70% training and 30% testing. Model performance was assessed using GCV, MSE, MAE, MAPE, and residual diagnostics. Results show that the Fourier Series model performs substantially better, achieving lower GCV (0.3520), MSE (0.00415 training; 0.00114 testing), and MAE (0.34015 training; 0.12940 testing), as well as lower MAPE (1.26% training; 0.47% testing). In contrast, the MSAR model yields higher errors with GCV (0.5747), MSE (0.9113 training; 0.4686 testing), MAE (0.8005 training; 0.5512 testing), and MAPE (1.96% training; 1.34% testing). These results indicate that Sleman’s temperature dynamics characterized by stable oscillatory patterns with minimal regime shifts are more effectively captured through harmonic decomposition. The study reinforces the importance of periodic modeling for mixed-topography regions like Sleman and recommends future research integrating additional climatic variables, hybrid statistical–machine-learning frameworks, and longer time spans to improve responsiveness to extreme events and nonlinear atmospheric behavior.
Co-Authors Ana, Elly Cahyoko, Fajar Dwi Dita Amelia Dwiyanto, Adelia Sukma Idrus Syahzaqi M. Fariz Fadillah Mardianto Mahadesyawardani, Arinda Marbun, Barnabas Anthony Philbert Maulidya, Utsna Rosalin Mochammad Baihaqi Nahar, Muhammad Hafidzuddin Nur Chamidah Rahmada, Indrastanto Oktodian Riefky, Muhammad