A MACHINE LEARNING-BASED CONCEPTUAL FRAMEWORK FOR AUTOMATED DIGITAL WASTE CLASSIFICATION WITH PROJECTED CARBON FOOTPRINT REDUCTION
DOI:
https://doi.org/10.55123/storage.v5i3.8955Keywords:
Carbon footprint, Digital waste, Data governance machine learning, Green computingAbstract
Pertumbuhan volume data digital yang pesat turut meningkatkan proporsi sampah digital serta berkontribusi terhadap emisi karbon pusat data, sementara penelitian klasifikasi berkas yang ada umumnya belum menghubungkan hasil klasifikasi dengan estimasi dampak lingkungan secara terukur. Penelitian ini bertujuan mengusulkan Automated Digital Waste Classification (ADWC), sebuah kerangka kerja konseptual berbasis pembelajaran mesin. Penelitian ini menggunakan pendekatan Design Science Research (DSR) dengan evaluasi kelayakan artefak melalui sintesis literatur dan proyeksi berbasis benchmark, mencakup arsitektur lima lapisan yang mengintegrasikan fitur metadata. Hasil proyeksi menunjukkan bahwa konfigurasi penuh ADWC mencapai macro-F1 tertinggi melampaui konfigurasi fitur tunggal maupun kombinasi parsial, serta kompetitif terhadap model benchmark termutakhir seperti CNN-BERT Hybrid. Pada korpus asumsi ±30 TB, ADWC-Full diproyeksikan mengidentifikasi 38,7% data sebagai limbah digital yang dapat ditindaklanjuti, dengan potensi reduksi emisi karbon yang meningkat proporsional terhadap skala implementasi. Analisis sensitivitas mengungkap bahwa intensitas karbon jaringan listrik merupakan parameter paling berpengaruh terhadap besarnya manfaat lingkungan jauh melampaui pengaruh PUE, intensitas energi penyimpanan, maupun fraksi limbah teridentifikasi. Temuan ini menegaskan kelayakan konseptual ADWC sebagai kerangka kerja yang menghubungkan tata kelola data pada tingkat berkas dengan indikator keberlanjutan yang terukur meskipun seluruh hasil masih bersifat proyeksi berbasis benchmark dan memerlukan validasi empiris pada lingkungan operasional nyata.
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