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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Prosiding SNATIKA Vol 01 (2011) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JMM (Jurnal Masyarakat Mandiri) International Journal of Natural Science and Engineering Indonesian Physical Review Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Masyarakat Indonesia The Indonesian Journal of Computer Science
Poltak Sihombing
FMIPA USU, Medan
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Physics Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Development of the fuzzy grid partition methods in generating fuzzy rules for the classification of data set Marbun, Murni; Sitompul, Opim Salim; Nababan, Erna Budhiarti; Sihombing, Poltak
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5378

Abstract

The main weakness of complex and sizeable fuzzy rule systems is the complexity of data interpretation in terms of classification. Classification interpretation can be affected by reducing rules and removing important rules for several reasons. Based on the results of experiments using the fuzzy grid partition (FGP) approach for high-dimensional data, the difficulty in generating many fuzzy rules still increases exponentially as the number of characteristics increases. The solution to this problem is a hybrid method that combines the advantages of the rough set method and the FGP method, which is called the fuzzy grid partition rough set (FGPRS) method. In the Irish data, the rough set approach reduces the number of characteristics and objects so that data with excessive values can be minimized, and the fuzzy rules produced using the FGP method are more concise. The number of fuzzy rules produced using the FGPRS method at K=2 is 50%; at K=K+1, it is reduced by 66.7% and at K=2 K, it is reduced by 75%. Based on the findings of the data collection classification test, the FGPRS method has a classification accuracy rate of 83.33%, and all data can be classified.
Deep learning approaches for analyzing and controlling rumor spread in social networks using graph neural networks Manurung, Jonson; Sihombing, Poltak; Andri Budiman, Mohammad; Sawaluddin, Sawaluddin
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8143

Abstract

The pervasive influence of social networks on information dissemination necessitates robust strategies for understanding and mitigating the spread of rumors within these interconnected ecosystems. This research endeavors to address this imperative through the application of a graph neural network (GNN) model, designed to capture intricate relationships among users and content in social networks. The study integrates user-level attributes, content characteristics, and network structures to develop a comprehensive model capable of predicting the likelihood of rumor propagation. The proposed model is situated within a broader conceptual framework that incorporates sociological theories on information diffusion, user behavior, and network dynamics. The results of this research offer insights into the interpretability of the GNN model’s predictions and lay the groundwork for future investigations. The iterative refinement of the model, consideration of ethical implications, and comparison against traditional machine learning baselines emerge as crucial steps in advancing the understanding and application of deep learning methodologies for rumor control in social networks. By embracing the complexities of real-world scenarios and adhering to ethical standards, this research strives to contribute to the development of proactive tools for rumor management, fostering resilient and trustworthy online information ecosystems.
Development of distance formulation for high-dimensional data visualization in multidimensional scaling Marto Hasugian, Paska; Mawengkang, Herman; Sihombing, Poltak; Efendi, Syahril
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8738

Abstract

This research aims to produce a new method called pasca-multidimensional scaling (pasca-MDS) by modifying the multidimensional scaling (MDS) method, the developed model comes as a solution to overcome the problem of data complexity by reducing its description dimension without losing important information. This model, offers an innovative approach in dealing with these problems. Pasca-MDS not only focuses on reducing the dimensionality of data, but also retains the essence of relevant information from each data point. As such, it allows for easier and more efficient analysis without compromising the accuracy of the information conveyed. The main advantage of pasca-MDS lies in its ability to produce simpler visual representations while maintaining the original structure of complex data. This provides clarity and ease in understanding the patterns or relationships hidden within. By using adjustment techniques after the MDS process, this model can provide more optimized results. This process allows the adjustment of data points to achieve a better representation in a lower dimensional space, resulting in a more intuitive and easy-to-understand interpretation. The developed distance formula has the ability to minimize stress compared to other distance formulas in MDS space, with the aim of improving the accuracy of high-dimensional data visualization.
Co-Authors Abner H.Bajari Andri Andri Andri Budiman, Mohammad Ari Juliana Arjon Turnip Asrianda Asrianda Baehaqi Budhiarti Nababan, Erna Butar Butar, Kartika Dewi Darwin Darwin Efendi, Syahril Erna Budhiarti Nababan Fahmi Fahmi Fauzan Nurahmadi Ghazali, Alfin Gunawan Gunawan Handrizal Handrizal Hasugian , Paska Marto Herman Mawengkang Irpan Pardosi Jayawarsa, A.A. Ketut K. M. Nasution , Mahyuddin Manurung, Jonson Maya Silvi Lydia Mesran, Mesran Muhammad Zarlis, Muhammad Naemah Mubarakah Ng Poi Wong Opim Salim Sitompul Roni Yunis Sarif, Muhammad Irfan Sawaluddin Sawaluddin, Sawaluddin Siti Sarah Harahap Sutarman Sutarman Tulus Tulus Weber, Gerhard Wilhelm Yudhistira Adhitya Pratama