Implementasi Hierarchical Clustering Untuk Analisis Fully Diluted Market Cap Narrative Crypto Berbasis Web

Adha, M. Fathir (2026) Implementasi Hierarchical Clustering Untuk Analisis Fully Diluted Market Cap Narrative Crypto Berbasis Web. Undergraduate thesis, Universitas Katolik Musi Charitas.

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The crypto asset market lacks a standardized narrative classification, making it difficult for users to objectively understand valuation differences among crypto narratives. This research aims to develop a web-based analytical application capable of performing valuation segmentation of crypto narratives based on the Fully Diluted Market Cap (FDMC) parameter. The system was developed using the Waterfall method, covering the stages of communication, planning, modeling, construction, and system testing. The research data were collected from 1,011 top crypto assets, which were subsequently filtered and consolidated into 26 major narratives. The data analysis process consisted of data selection, preprocessing, logarithmic transformation, Z-Score normalization, Agglomerative Hierarchical Clustering modeling and cluster evaluation. The results show that the system successfully formed five clusters based on log-transformed FDMC values, which were subsequently interpreted into three valuation categories Bluechip, Growth and Speculative with good cluster separation quality. The application presents the analysis results through interactive visualizations, enabling users to understand valuation differences among crypto narratives more clearly and in a data-driven manner. The interpretation of these categories is exploratory in nature and should not be considered as a direct investment recommendation.

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