Pengembangan Sistem Prediksi Perubahan Iklim Berbasis Kecerdasan Buatan untuk Manajemen Sumber Daya Alam yang Berkelanjutan di Papua

Papua, as a region with significant natural resource wealth in Indonesia, faces complex challenges in managing its resources sustainably amid the growing threat of global climate change. This research aims to develop a climate change prediction system that is strengthened with artificial intelligence technology, especially through machine learning techniques, to support the management of natural resources in Papua. By utilizing climate data from the Meteorology, Climatology, and Geophysics Agency and resource extraction data from the Ministry of Energy and Mineral Resources, the proposed system seeks to integrate and analyze this information to predict climate change and its impacts in real-time. AI provides opportunities to process and analyze big data with high efficiency, resulting in more accurate and timely predictions about the impact of climate change on biodiversity, ecosystem sustainability, and natural resource accessibility. This increase in accuracy is expected to facilitate policymakers in designing efficient adaptation and mitigation strategies to respond to dynamic environmental changes. In addition, the study also explores how AI technology can contribute to natural resource management in a more innovative and sustainable way, opening up new avenues in natural resource conservation and management in Papua. The main focus is to integrate this predictive analysis into regional development planning, ensuring that economic growth takes place in harmony with environmental conservation, which is crucial for Papua's long-term sustainability.

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Publication Details

Journal
Jurnal Syntax Admiration
Published
2024-09-21
DOI
https://doi.org/10.46799/jsa.v5i9.1488
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Pengembangan Sistem Prediksi Perubahan Iklim Berbasis Kecerdasan Buatan untuk Manajemen Sumber Daya Alam yang Berkelanjutan di Papua

Muhamad Hisyam Az-Zahran, Melisa Indah Sari Silaban, Euodia Hodesy Rasuli
Jurnal Syntax Admiration
Data Mining and Machine Learning Applications
article

Pengembangan Sistem Prediksi Perubahan Iklim Berbasis Kecerdasan Buatan untuk Manajemen Sumber Daya Alam yang Berkelanjutan di Papua

Muhamad Hisyam Az-Zahran, Melisa Indah Sari Silaban, Euodia Hodesy Rasuli
article en

Abstract

Papua, as a region with significant natural resource wealth in Indonesia, faces complex challenges in managing its resources sustainably amid the growing threat of global climate change. This research aims to develop a climate change prediction system that is strengthened with artificial intelligence technology, especially through machine learning techniques, to support the management of natural resources in Papua. By utilizing climate data from the Meteorology, Climatology, and Geophysics Agency and resource extraction data from the Ministry of Energy and Mineral Resources, the proposed system seeks to integrate and analyze this information to predict climate change and its impacts in real-time. AI provides opportunities to process and analyze big data with high efficiency, resulting in more accurate and timely predictions about the impact of climate change on biodiversity, ecosystem sustainability, and natural resource accessibility. This increase in accuracy is expected to facilitate policymakers in designing efficient adaptation and mitigation strategies to respond to dynamic environmental changes. In addition, the study also explores how AI technology can contribute to natural resource management in a more innovative and sustainable way, opening up new avenues in natural resource conservation and management in Papua. The main focus is to integrate this predictive analysis into regional development planning, ensuring that economic growth takes place in harmony with environmental conservation, which is crucial for Papua's long-term sustainability.

Jurnal Syntax AdmirationVol. 5(9)
Padjadjaran University (ID)
Openalex Percentile: Top 75%
Data Mining and Machine Learning Applications
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