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All Journal Society: Jurnal Pengabdian Masyarakat Envirology
Allan Darma Putra Pramudita
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Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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Pemodelan Prediktif Emisi Karbon Indonesia Menggunakan Machine Learning: Analisis Tren 1975-2017 dan Proyeksi Pencapaian Target NDC 2030 Akil Hi Umar; Allan Darma Putra Pramudita
Envirology Vol. 2 No. 2 (2024): ENVIROLOGY
Publisher : Universitas Persatuan Islam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65483/envirology.v2i2.232

Abstract

Pencapaian target Nationally Determined Contribution (NDC) Indonesia memerlukan pemahaman mendalam terhadap pola historis emisi karbon dioksida (CO₂) dan proyeksi akurat menggunakan pendekatan komputasional. Penelitian ini bertujuan mengembangkan model prediktif emisi CO₂ Indonesia berbasis machine learning untuk menganalisis tren 1975-2017 dan memproyeksikan pencapaian target NDC 2030. Data sekunder emisi CO₂ total dan per kapita periode 1975-2017 dari Carbon Dioxide Emission Estimates diolah menggunakan Python dengan library Pandas, NumPy, Scikitlearn, dan Statsmodels. Tiga model machine learning dikembangkan dan dievaluasi: Exponential Growth Model, Polynomial Regression, dan Linear Regression. Hasil menunjukkan emisi Indonesia meningkat eksponensial dari 37,84 juta ton (1975) menjadi 496,41 juta ton (2017) dengan CAGR 6,30% per tahun. Model Polynomial Regression memberikan performa terbaik (R²=0,995, RMSE=8,94 Mt, MAE=6,72 Mt) dalam memprediksi pola non-linear emisi. Proyeksi business-as-usual menunjukkan emisi 2030 mencapai 873 juta ton—48% di atas target NDC 592 juta ton. Monte Carlo simulation dengan 10.000 iterasi memberikan 95% confidence interval [831-916 Mt] dengan probabilitas mencapai target NDC hanya 0,01%. Model mengidentifikasi periode 1985-1995 sebagai fase akselerasi tertinggi (CAGR 9,24%) yang berkorelasi dengan industrialisasi masif. Cross-validation K-Fold menunjukkan model robust dengan konsistensi tinggi (std error <10%). Penelitian ini mengkonfirmasi pentingnya pendekatan computational environmental science dalam policy planning, dengan rekomendasi implementasi real-time monitoring system berbasis machine learning untuk tracking progress NDC Indonesia.
Planning the Implementation of a 3R (Reduce, Reuse, and Recycle) Waste Management Facility for the Prevention of Waste-Related Diseases in Sukawening Village, Bandung Regency Allan Darma Putra Pramudita; Melbi Tanjung; Sri Ratna Wulan; Muhammad Anang Hadiat; Riska Septiana; Dita Septiani
Society : Jurnal Pengabdian Masyarakat Vol. 5 No. 3 (2026): Mei
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/ntf0an28

Abstract

Waste generation in Sukawening Village has exceeded the capacity of the existing 3R (Reduce, Reuse, and Recycle) temporary waste management facility (TPS 3R), while current waste treatment practices still rely on open burning. Therefore, it is necessary to develop a plan for a TPS 3R facility capable of accommodating all household waste and reducing waste generation without the use of burning in Sukawening Village.The community service methodology employed in this study includes educational seminars on waste management, the promotion of a healthy environmental culture, and practical training on household-scale composting. In addition, a quantitative approach was applied to develop a feasible TPS 3R planning framework for Sukawening Village. The outcomes of this community service activity are expected to promote more environmentally friendly behavior among the community, enable households to reduce waste generation through composting practices, and provide a viable plan for the development of a TPS 3R facility in Sukawening Village.
Co-Authors Akil Hi Umar Dita Septiani Melbi Tanjung Muhammad Anang Hadiat Riska Septiana Wulan, Sri Ratna