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All Journal Elkawnie Al-Kimia Chimica et Natura Acta KOMMAS: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) Tabasa: Jurnal Bahasa, Sastra Indonesia, dan Pengajarannya
Kurnia Ramadani
Department Of Chemistry, Faculty Of Science And Technology, UIN Alauddin Makassar, Makassar
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics

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Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

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Performance Analysis of Neighborhood Component Analysis on Support Vector Machine in Greenhouse Gas Emission Classification Gustriza Erda; Kurnia Ramadani
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.4305

Abstract

The heatwave phenomenon has hit several countries in various parts of the world, caused by climate change. Climate change leads to greenhouse gas emissions increasing beyond the limits set by the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report Global Warming Potentials. This final project uses a combination of Neighborhood Component Analysis (NCA) and Support Vector Machine (SVM) methods with linear, polynomial, Radial Basis Function (RBF), and sigmoid kernel functions. The purposes of this final project are to evaluate the performance of NCA on SVM and to determine the best kernel function in this combination. Based on the analysis, it was found that classification using a combination of NCA and SVM methods can reduce variables, with the best kernel function being the Polynomial kernel function. This is because the analysis using the Polynomial kernel function achieved the highest accuracy values for training data, testing accuracy, and F1-Score, which are 98,96%, 99,15%, and 98,98% respectively. Additionally, the training analysis time and testing analysis time were the shortest at 0,15 seconds and 0,04 seconds.
Co-Authors Agnes Gabriela Putri Winata Amalyah Febryanti Andi Nurhikmah Arfiani Nur David Chandra Dwi Nur Assyifah Rajmah Gustriza Erda Hermawan Hermawan Indra Aulia Irmawati, Ekha Kurniawati L Ledya Novamizanti Muhammad Azwar Zulmi Muhammad Raia Pratama Putra Wibowo Musafira Nensilianti Nur Amalia P Nurul Nurul Ramayana Ramayana Ridwan Ridwan Rismang Rismang Sjamsiah, Sjamsiah St. Chadijah Suryo Adhi Wibowo Susi Diriyanti Novalina Syamsidar HS Syamsidar HS Syamsidar HS Syamsidar HS Titik Andriani, Titik