KLASIFIKASI LEVEL KUALITAS UDARA MENGGUNAKAN METODE DECISION TREE UNTUK MENDUKUNG SDGS
DOI:
https://doi.org/10.54314/jssr.v9i3.6675Keywords:
Air Quality, Decision Tree, pollution, SDG, ClassificationAbstract
Abstract: Air quality is a critical environmental issue that directly affects public health and urban sustainability. This study aims to develop an air quality classification model using the Decision Tree algorithm based on key pollutant parameters such as PM₁₀, PM₂.₅, SO₂, CO, NO₂, and O₃, and to evaluate its contribution to the achievement of the Sustainable Development Goals (SDGs). The dataset was obtained from air quality monitoring in Jakarta during 2021 and underwent preprocessing stages including data cleaning and feature selection. The model was tested across various training-testing ratios, with the highest accuracy of 97.50% achieved at a 60:40 ratio. Performance evaluation showed precision, recall, and F1-score values above 97%, with PM₂.₅ and PM₁₀ identified as the most influential attributes in classification. The Decision Tree model was chosen for its ability to produce accurate results while remaining interpretable. The classification outcomes can be utilized in early warning systems, public education, and data-driven policymaking. These findings support the achievement of SDG 3 (Good Health and Well-Being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).
Keywords: Air Quality; Decision Tree; pollution; SDG; Classification.
Abstrak: Kualitas udara merupakan isu lingkungan krusial yang berdampak langsung pada kesehatan masyarakat dan keberlanjutan kota. Penelitian ini bertujuan mengembangkan model klasifikasi kualitas udara menggunakan algoritma Decision Tree berdasarkan parameter polutan utama seperti PM₁₀, PM₂.₅, SO₂, CO, NO₂, dan O₃, serta menganalisis kontribusinya terhadap pencapaian Tujuan Pembangunan Berkelanjutan (SDGs). Data yang digunakan berasal dari pemantauan kualitas udara di Jakarta tahun 2021 dan telah melalui tahap pra-pemrosesan, termasuk pembersihan data dan seleksi fitur. Model diuji pada berbagai rasio data latih dan uji, dengan akurasi tertinggi sebesar 97,50% pada rasio 60:40. Evaluasi performa menunjukkan nilai presisi, recall, dan F1-score di atas 97%, dengan PM₂.₅ dan PM₁₀ sebagai atribut paling berpengaruh dalam klasifikasi. Model Decision Tree dipilih karena kemampuannya dalam menghasilkan output yang akurat sekaligus mudah diinterpretasikan. Hasil klasifikasi ini berpotensi digunakan dalam sistem peringatan dini, edukasi masyarakat, dan pengambilan kebijakan berbasis data. Temuan ini mendukung pencapaian SDG 3 (kehidupan sehat dan sejahtera), SDG 11 (kota dan permukiman yang berkelanjutan), serta SDG 13 (penanganan perubahan iklim).
Kata kunci: Kualitas Udara; Decision Tree; Polutan; SDGs; Klasifikasi.
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