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

Order Theory I and II As Foundations for Finding Relationship Between Formulas Stephanus Ivan Goenawan
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 2 No. 1 (2020): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v2i1.5804

Abstract

Order theory is generated through the process of induction logic from solving several mathematical functions so that it can be formulated in general the pattern of regularity. If there are function and associates each other is organized and interconnected, then the constants will also be organized and interconnected. Similarly, if there are function and associates that are mutually regulated and the constants that make them organized and interrelated, then the function basis and associates will also have interconnected properties.
Fractional Generating Function from The Square Root of Two with A-B Goen Numbers Stephanus Ivan Goenawan
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 4 No. 1 (2022): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v4i1.8073

Abstract

The square root of two is an irrational number that cannot be written as a fraction of the numerator and denominator. By using the generating function of A-B Goen, from the resulting set of numbers, the sequence of A-B Goen numbers can be obtained by selecting integer numbers. The A Goen numbers are generated from the generator function which have integer numbers, while the B Goen numbers are obtained from the sequence numbers. In this study, through the generating function of A-B Goen, it can be proven that the division between the A Goen number and the B Goen number in an infinite sequence will result the value of the square root of two.
Efficient Computation of Number Fractions from the Square Root of Two Using the A-B Goen Number Function Via the Ivan Newton (in) Series Goenawan, Stephanus Ivan
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.v6i3.11575

Abstract

The square root number of two is an irrational number. If it is an irrational number, the result cannot be written as a fraction of the numerator and denominator. Fractions that approach the square root value of two have a correlation with Goen's A-B numbers. The regularity of the A-B Goen number sequence can be formulated into the A-B Goen function which is built from the Ivan Newton series. In this research, it can be proven that the A-B Goen function from the Ivan Newton (IN) series is computationally more effective and efficient when compared to the A-B Goen generating function in producing A-B Goen numbers which in infinite sequence will approach the square root value of two.
Proof of Data Weigher Analysis (DWA) and Its Application to Dynamic Meta Data Weigher Goenawan, Stephanus Ivan
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.v7i3.13155

Abstract

Data Weigher Analysis (DWA) addresses the persistent problem of objectively quantifying whether the values in a data set lean more heavily toward the left or right side, a challenge that becomes increasingly complex in irregular or large-scale data sets. Motivated by the need for a simple yet rigorous quantitative framework, this study compares two DWA techniques—the data weighting method and the data mean difference method—designed to compute balance points in a sequence. The data weighting method assigns balanced linear weights to left and right subsets, whereas the data mean difference method calculates first- and second-order mean differences to capture asymmetry in data distribution. We provide a theoretical proof of equivalence between these two formulations, showing that the mean difference approach produces the same linear weighting as the original data weighting scheme. Building on this theoretical result, we introduce a sliding-window algorithm to operationalize DWA on large, dynamic data streams, allowing automated detection of local imbalances in real time. Empirically, we validate our approach on real-world metadata and trade datasets, comparing it against baseline descriptive statistics to assess efficiency and precision. Quantitative findings show that the mean difference method reduces computation processes without loss of accuracy compared with manual weighting. Overall, this work contributes to a unified theoretical foundation, a lightweight algorithmic implementation, and evidence of practical benefits for using DWA in decision-making contexts such as questionnaire analysis, market dynamics, and trade flow monitoring.