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All Journal Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Data Science: Journal of Computing and Applied Informatics
Kardi Teknomo
Doctoral student Graduate School of Information Science, Tohoku University Japan
Computer Science & IT Industrial & Manufacturing Engineering

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Finding Random Integer Ideal Flow Network Signature Algorithms Teknomo, Kardi; Nababan, Erna Budhiarti; Bisono, Indriati Njoto; Lim, Resmana
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 1 (2025): June 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.1.105-120

Abstract

We propose a Random Integer Ideal Flow Network (IFN) Signature Algorithm that generates integral flow assignments in strongly connected directed graphs under uncertainty. Existing models often fail to incorporate the inherent randomness and integer constraints present in systems like social networks. Unlike traditional approaches that enforce integrality through large scaling factors, our method distributes integer coefficients across multiple canonical cycles, ensuring precise balance where the sum of inflows exactly equals the sum of outflows at each node. We introduce two pseudocode algorithms that uphold flow conservation while maintaining network irreducibility, ensuring autonomy through strong connectivity. Theoretical contributions include the decomposition of IFNs into canonical cycles and the construction of network signatures, string-based representations that allow efficient performance evaluation through direct string manipulation. These signatures enable quick validation of key network properties such as total flow, balanced link flows, and structural irreducibility. To demonstrate practical applications, we apply our algorithm to modeling family power dynamics, illustrating how IFN can create minimal yet resilient networks that balance autonomy with accountability. This framework lays the foundation for future advancements in predictive modeling and network optimization. To ensure reproducibility, we provide an open-source Python implementation on GitHub.
Premagic and Ideal Flow Matrices Teknomo, Kardi
Data Science: Journal of Computing and Applied Informatics Vol. 3 No. 1 (2019): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1107.054 KB) | DOI: 10.32734/jocai.v3.i1-621

Abstract

Several interesting properties of a special type of matrix that has a row sum equal to the column sum are shown with the proofs. Premagic matrix can be applied to strongly connected directed network graph due to its nodes conservation flow. Relationships between Markov Chain, ideal flow and random walk on directed graph are also discussed.
Co-Authors Erna Budhiarti Nababan Indriati Njoto Bisono, Indriati Njoto Resmana Lim