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All Journal Jurnal Informatika Engineering, Mathematics and Computer Science Journal (EMACS)
Aan AlBone
Jurusan Teknik Informatika, Universitas Pasundan, Bandung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

 1 
Building Customer and Product Networks with Cosine Similarity in Graph Analytics for Deep Customer Insight Albone, Aan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11693

Abstract

Creating connections that allow users to share information, experiences, and product recommendations is the main goal of social networks. These networks are essential for assisting companies in comprehending user preferences, behavior, and buying trends. Graph theory is a crucial tool for analyzing and interpreting the intricate relationships found in such systems. It enables a structured depiction of users and their interactions through nodes and edges, offering important insights into the information and influence flow within the network. This idea is used in our customer network model to enhance recommendation and product engagement tactics. We can find users with similar interests and recommend pertinent products by examining the relationships between customers. Two customers are said to have closely aligned preferences and behaviors when their cosine similarity is greater than 70%. This makes it possible for the system to suggest goods that a customer has bought or given a high rating to another customer in the same similarity cluster. Additionally, we can track price sensitivity and market trends by mapping products within a product network. The network analysis enables us to see how a product's price impact on demand in comparison to similar items is affected if it is more expensive than comparable alternatives. All things considered, social network analysis and graph theory together provide a potent method for comprehending customer behavior, improving personalization, and refining marketing tactics for improved business results.
Optimization of Fraud Detection Model with Hybrid Machine Learning and Graph Database Albone, Aan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 1 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i1.10744

Abstract

Machine learning and the graph database work well together. By concentrating on the relationships between fraudsters or fraud cases, graph databases can provide an additional layer of security, while machine learning uses statistics and data analytical tools to categorize information and identify patterns within data. In doing so, it can transcend rigid rules and scale human insights into algorithms. When combined with a graph, machine learning alone can increase the accuracy of fraud signals to 90% or higher. On its own, it can reach 70–80%. Graphs also improve machine learning's explainability.
Optimizing Enterprise Risk Management for Decision Making Using Knowledge Graph Albone, Aan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 3 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i3.14325

Abstract

The challenge in current enterprise risk management is that hundreds of risks are eventually recorded without knowing how hazards relate to one another or cascade. The distinction between peripheral and critical hazards is unknown to decision-makers. Organizations can depict the interconnectedness of risk in a structured, adaptable, and understandable way by showing these components as nodes and their interactions as edges. This knowledge graph makes it possible to store and query risk data in ways that are not entirely supported by conventional relational models. This method's ability to execute graph queries that uncover links and patterns that would otherwise be obscured in siloed datasets is one of its main advantages. Such inquiries can reveal how a single threat can lead to many vulnerabilities across multiple assets, or how flaws in shared systems can directly and indirectly raise exposure to interconnected hazards. These revelations draw attention to structural flaws that linear or isolated investigations frequently ignore. Organizations can improve situational awareness and long-term risk governance by using such a knowledge graph to find hidden trends, pinpoint important risk spots, and more efficiently prioritize mitigation efforts. The knowledge graph also helps to optimize enterprise risk management goals like resource allocation, control prioritization, and prompt reaction planning. Enterprise risk management can effectively represent the intricate relationships between risks, vulnerabilities, threats, and assets by incorporating a knowledge graph. Businesses can concentrate mitigation efforts where they will have the biggest impact by determining which nodes and edges are the most important and highest impact. This focused strategy increases overall resilience and decreases inefficiencies.
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