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All Journal Al-Aqlu : Jurnal Matematika, Teknik dan Sains IJASDS: Indonesian Journal of Applied Statistics and Data Science
Sabina, Sabna Zulfaa
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Engineering Environmental Science Mathematics Public Health Social Sciences

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PERAMALAN EKSPOR MIGAS BERBASIS EKSTRAPOLASI POLINOMIAL CHEBYSHEV: Forecasting of Oil and Gas Exports Based on Chebyshev Polynomial Extrapolation Sabina, Sabna Zulfaa; Rahmi, Dhea Wasila; Awwaliyah, Razma Rizqiyyah; Robbaniyyah, Nuzla Af’idatur; Rusadi, Tri Maryono
Al-Aqlu: Jurnal Matematika, Teknik dan Sains Vol. 3 No. 1 (2025): Januari 2025
Publisher : Yayasan Al-Amin Qalbu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59896/aqlu.v3i1.141

Abstract

Exports play a strategic role in supporting the country's revenue. The oil and gas sector, as one of Indonesia's primary natural resources, contributes significantly to national economic growth. Indonesia's oil and gas production currently exhibits an unstable trend, influenced by the dynamics of global prices and demand. This research aims to forecast Indonesia's oil and gas export volume in 2025 using the Chebyshev extrapolation method. The Chebyshev extrapolation method is chosen due to its ability to produce more accurate predictions for data outside the available time range. The data used includes monthly export volumes of the oil and gas sector from 2019 to 2024. In the analysis, a fourth-degree Chebyshev polynomial is applied to project future export trends. The Mean Absolute Percentage Error (MAPE) for the Chebyshev extrapolation model is 12,10%. The forecasting results indicate an increase in monthly oil and gas exports in 2025, with the lowest value recorded in January at 1.373,70 and the peak export in December at 1.805,33. Overall, this prediction indicates the stability of oil and gas export volumes with a tendency towards an increasing trend compared to the previous year.
Pengklasifikasian 10 Kabupaten/Kota di Provinsi Nusa Tenggara Barat untuk Kasus Kemiskinan Tahun 2022 Menggunakan Analisis Cluster Metode K-Means Sabina, Sabna Zulfaa; Alfarez, Dzaki Ade; Graha, Syifa Salsabila Satya; Auladi, Muhammad Yuzaul; Lisa , Harsyiah; Lisa Harsyiah
Indonesian Journal of Applied Statistics and Data Science Vol. 1 No. 1 (2024): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v1i1.5415

Abstract

Poverty is a very serious problem for countries in the world, especially for developing countries like Indonesia. Poverty will have a big impact if it occurs in the long term with different factors. One province that is still in the spotlight for high levels of poverty is West Nusa Tenggara Province. Even though the number of poor people in West Nusa Tenggara Province has decreased, conditions of the ground show that there are still many people whose lives are far from decent. Therefore, the government must immediately find a solution to overcome the problem of poverty. To overcome cases of poverty in a region, we can group the characteristics of these regions based on poverty indicators into several clusters. Grouping in this case is carried out with data that will be analyzed using the K-Means cluster analysis method. So the results obtained by analysis using the K-Means cluster method for grouping 10 regencies/cities in West Nusa Tenggara Province based on poverty in 2022 formed 3 clusters, namely cluster 1 consisting of West Sumbawa Regency, Bima City and Mataram City, cluster 2 consisting of Bima Regency, Dompu Regency, West Lombok Regency, Central Lombok Regency, East Lombok Regency, and Sumbawa Regency, and cluster 3 consists of North Lombok Regency. Apart from that, the characteristics of each cluster were also obtained, namely cluster 1 containing the districts/cities with the highest PPM values. While RLS, AHH, and TPT have very high numbers in 2022, cluster 2 contains districts/cities that have quite low PPM, RLS, AHH, and TPT numbers in 2022, and cluster 3 contains a group of districts/cities with RLS, AHH, and TPT has quite low numbers compared to the high PPM in 2022.
Co-Authors Alfarez, Dzaki Ade Auladi, Muhammad Yuzaul Awwaliyah, Razma Rizqiyyah Graha, Syifa Salsabila Satya Harsyiah, Lisa Lisa , Harsyiah Rahmi, Dhea Wasila Robbaniyyah, Nuzla Af’idatur Tri Maryono Rusadi