Dari Statis ke Adaptif: Literatur Sistematis Riview tentang Evolusi Sistem Pendukung Keputusan Berbasis Machine Learning
DOI:
https://doi.org/10.52060/juptik.v4i1.4188Abstract
Penelitian ini bertujuan untuk mengkaji evolusi Decision Support System (DSS) dari sistem statis menuju sistem adaptif berbasis machine learning melalui pendekatan Systematic Literature Review (SLR). Studi ini menganalisis artikel ilmiah dalam rentang waktu 2005-2025 dengan menggunakan kerangka PRISMA untuk memastikan proses seleksi yang sistematis dan transparan. Hasil kajian menunjukkan adanya pergeseran signifikan dalam pengembangan DSS, dari pendekatan berbasis aturan (rule-based) menuju sistem berbasis data dan kecerdasan buatan. Pada periode awal, DSS didominasi oleh model deterministik, kemudian bertransisi pada pendekatan data-driven (2005-2015), hingga berkembang menjadi Adaptive Decision Support Systems (ADSS) pada periode 2015-2025, yang ditunjukkan oleh dominasi sekitar 70% studi berbasis machine learning. Integrasi machine learning terbukti meningkatkan akurasi keputusan (65-75%) dan efisiensi sistem (±60%), serta memungkinkan pembelajaran adaptif dan personalisasi rekomendasi. Namun, penelitian ini juga mengidentifikasi sejumlah tantangan utama, seperti kompleksitas model, rendahnya explainability, keterbatasan pendekatan berbasis pengguna, serta belum adanya kerangka konseptual yang terpadu. Dengan demikian, pengembangan DSS ke depan perlu menekankan keseimbangan antara kecerdasan sistem, transparansi, dan kebutuhan pengguna agar menghasilkan sistem yang adaptif, dapat dipercaya, dan implementatif.
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Keywords:
Decision Support System, DDS, Mechine learning, GDSS
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2026-06-01
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