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All Journal Contemporary Mathematics and Applications (ConMathA) MATHunesa: Jurnal Ilmiah Matematika
Nurdini, Aisyah Tur Rif’atin
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Materials Science & Nanotechnology Mathematics

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Pattern-Based Identification of Priority Sectors for Greenhouse Gas Emission Control in Indonesia Using Self-Organizing Map Nurdini, Aisyah Tur Rif’atin; Amiroch, Siti
Contemporary Mathematics and Applications (ConMathA) Vol. 7 No. 2 (2025)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v7i2.74176

Abstract

Indonesia is one of the countries that ratified the Paris Agreement, a legally binding international treaty under the United Nations Framework Convention on Climate Change (UNFCCC) regarding greenhouse gas emissions. In line with this commitment, Indonesia is expected to prioritize emission control in sectors that contribute significantly to national emission levels. This study applies the Self-Organizing Map (SOM), a type of neural network, to cluster emission data by sector based on similarity patterns, aiming to identify priority sectors for emission control in Indonesia. The results indicate that the highest-emitting sectors are: Processes for Carbon Dioxide (CO₂), Transport for Methane (CH₄), Processes for F-Gases, and Agriculture for Nitrous Oxide (N₂O). These findings can inform government efforts to prioritize emission control policies in the Processes, Transport, and Agriculture sectors, tailored to each dominant gas type. Such recommendations are essential to support data-driven decision-making, improve national emission control strategies, and strengthen Indonesia’s position in meeting its Nationally Determined Contributions (NDCs) under the Paris Agreement. Model validation using Quantization Error (QE) produced values of 0.0218 for CO₂, 0.0207 for CH₄, 0.0040 for F-Gases, and 0.0171 for N₂O. These low values indicate high mapping accuracy and confirm that SOM is effective in capturing the distribution patterns of emission data, thus providing a scientific basis for designing more targeted mitigation strategies.  
PREDIKSI HASIL PANEN PADI DI KABUPATEN LAMONGAN MENGGUNAKAN METODE ADAMS-BASHFORTH-MOULTON DENGAN MODEL VERHULST Nurdini, Aisyah Tur Rif’atin; Amiroch, Siti; Pradana, Mohammad Syaiful
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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Abstract

Kabupaten Lamongan merupakan salah satu daerah penghasil padi terbesar di Jawa Timur, yang berperan penting dalam menjaga ketersediaan pangan nasional. Untuk mendukung strategi ketahanan pangan daerah, penelitian ini bertujuan memprediksi hasil panen padi tahun 2024–2033 menggunakan model pertumbuhan Verhulst dengan metode numerik Adams–Bashforth–Moulton (ABM). Data historis panen tahun 2014–2023 digunakan sebagai input, dengan estimasi laju pertumbuhan rata-rata sebesar 0,0078. Model numerik diselesaikan menggunakan metode Runge-Kutta untuk nilai awal, kemudian dilanjutkan dengan ABM. Hasil prediksi menunjukkan tren peningkatan panen sebesar ±2.000 ton per tahun, dengan total panen tahun 2033 mencapai 1.055.760 ton. Nilai galat relatif sebesar 0,0000004238 menunjukkan tingkat akurasi model sangat tinggi. Temuan ini dapat dijadikan dasar penyusunan kebijakan distribusi dan cadangan pangan berbasis tren matematis.
Co-Authors Mohammad Syaiful Pradana, Mohammad Syaiful Siti Amiroch, Siti