ANALISIS PERKEMBANGAN IMPLEMENTASI LOGIKA FUZZY DAN MACHINE LEARNING DALAM PENGAMBILAN KEPUTUSAN: SEBUAH STUDI LITERATUR SISTEMATIS

Abstract

The development of artificial intelligence technology, particularly Fuzzy Logic and Machine Learning, has brought a major transformation to the decision-making process across various sectors of life. This study aims to analyze the development, trends, and effectiveness of implementing Fuzzy Logic and Machine Learning in decision-making based on a systematic literature review. The method used in this research is a Systematic Literature Review (SLR) by adopting the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol. The primary data sources consist of 12 selected scientific articles published between 2008 and 2024, which discuss the implementation of both methods in the fields of industry, education, healthcare, and information technology. The results of the analysis indicate that Fuzzy Logic (such as the Mamdani method) is highly superior in handling the uncertainty and ambiguity of qualitative data in managerial decisions, whereas Machine Learning provides high accuracy in recognizing complex and dynamic data patterns. The web-based integration of these two approaches (using frameworks such as Streamlit) is proven to enhance the accessibility and efficiency of decision-making systems for end-users. The conclusion of this study emphasizes that method selection must be adjusted to the data characteristics and specific system requirements. Suggestions for future research include the development of hybrid models that combine the interpretive intelligence of Fuzzy Logic with the predictive power of Machine Learning