Evaluating the Effectiveness of Artificial Intelligence Models in Predicting Economic Indicators: an in-Depth Review
Abstract
Abstrak: Di era digital saat ini, kecerdasan buatan (artificial intelligence/AI) memainkan peran yang semakin penting dalam analisis ekonomi; namun, efektivitas berbagai model AI dalam memprediksi indikator ekonomi masih memerlukan evaluasi menyeluruh. Penelitian ini bertujuan untuk mengatasi kesenjangan ini dengan menilai efektivitas model AI dalam konteks yang lebih luas melalui pendekatan tinjauan literatur yang sistematis. Penelitian ini mengidentifikasi metode yang efektif dan mengeksplorasi tantangan dan keberhasilan yang terkait dengan implementasinya. Dengan menggunakan pendekatan penelitian kualitatif dan tinjauan literatur sistematis, literatur yang digunakan bersumber dari database pengindeksan seperti Scopus, DOAJ, dan Google Scholar, dengan tanggal publikasi mulai dari tahun 2014 hingga 2024. Hasil evaluasi menunjukkan bahwa model AI, khususnya deep learning dan model hybrid, menawarkan keuntungan yang substansial dibandingkan metode konvensional dalam memprediksi indikator ekonomi. Jaringan syaraf, seperti LSTM dan CNN, unggul dalam menangkap pola temporal dan spasial yang kompleks, sementara model hibrida meningkatkan akurasi prediksi dengan mengintegrasikan berbagai teknik AI. Penggabungan sumber data alternatif, seperti media sosial dan tren penelusuran, memberikan wawasan tambahan di luar data ekonomi tradisional, sehingga memperkaya prediksi. Explainable AI (XAI) semakin mendukung efektivitas model-model ini dengan meningkatkan transparansi dan kepercayaan di antara para pemangku kepentingan. Selain itu, Natural Language Processing (NLP) meningkatkan akurasi prediksi dengan menganalisis sentimen pasar dan berita ekonomi, sehingga menambah konteks yang berharga.
Abstract: In the current digital era, artificial intelligence (AI) plays an increasingly pivotal role in economic analysis; however, the effectiveness of various AI models in predicting economic indicators still requires thorough evaluation. This research aims to address this gap by assessing the effectiveness of AI models within a broader context through a systematic literature review approach. The study identifies effective methods and explores the challenges and successes associated with their implementation. Employing a qualitative research approach and systematic literature review, the literature used is sourced from indexing databases such as Scopus, DOAJ, and Google Scholar, with publication dates ranging from 2014 to 2024. The evaluation results reveal that AI models, particularly deep learning and hybrid models, offer substantial advantages over conventional methods in predicting economic indicators. Neural networks, such as LSTM and CNN, excel at capturing complex temporal and spatial patterns, while hybrid models enhance predictive accuracy by integrating various AI techniques. The incorporation of alternative data sources, such as social media and search trends, provides additional insights beyond traditional economic data, enriching predictions. Explainable AI (XAI) further supports the effectiveness of these models by increasing transparency and trust among stakeholders. Additionally, Natural Language Processing (NLP) enhances predictive accuracy by analyzing market sentiment and economic news, thereby adding valuable context.
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DOI: https://doi.org/10.31764/jua.v29i1.30341
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