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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Eksponensial
Mahadesyawardani, Arinda
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Other

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Pengelompokan Kabupaten dan Kota di Jawa Timur berdasarkan Percepatan Pemulihan Ekonomi Menggunakan Pendekatan Hirearki Mahadesyawardani, Arinda; Zhafirab, Azizah Atsariyyah; Ariyawan, Jovansha; Humaira, Edla Putri; Mardianto, M. Fariz Fadillah; Amelia, Dita; Ana, Elly
EKSPONENSIAL Vol. 15 No. 1 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i1.1273

Abstract

The Covid-19 pandemic's diverse impact on Indonesia's economy, particularly in East Java, spurred the government to formulate a comprehensive work plan targeting three key objectives, one of which is to expedite economic recovery. This plan focuses on three key indicators: economic growth, open unemployment rate (TPT), and the gini ratio. It is known that during the pandemic, East Java initially experienced economic growth that contracted until eventually showing positive growth in the second quarter of 2021, which has been supported by national policies. This study explores district and city classification in East Java based on economic recovery indicators through hierarchical clustering. The analysis identifies Ward's linkage as the most effective model, with a cophenetic correlation coefficient of 0.9311. Internal clustering validation tests reveal two optimal clusters. Cluster 1 is characterized by a notably high average acceleration of economic recovery across all three indicators. The findings suggest that the government should optimize the economic stimulus program for cluster 2 and focus on enhancing income redistribution and job opportunities for cluster 1.
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.
Co-Authors Ana, Elly Ariyawan, Jovansha Dita Amelia Humaira, Edla Putri M. Fariz Fadillah Mardianto Marbun, Barnabas Anthony Philbert Maulidya, Utsna Rosalin Nur Chamidah Pratama, Fachriza Yosa Zhafirab, Azizah Atsariyyah