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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Mohd. Asyraf Mansor
School of Distance Education, Universiti Sains Malaysia, Malaysia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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LOGIC MINING CLASSIFICATION FOR PHONE PRICES DATASET USING DISCRETE HOPFIELD NEURAL NETWORK AND WEIGHTED RANDOM 2 SATISFIABILITY Nurul Najwa Ahmad Azam; Nur Ezlin Zamri; Mohd Shareduwan Mohd Kasihmuddin; Nurul Atiqah Romli; Mohd. Asyraf Mansor
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2389-2400

Abstract

Smartphones have become essential in today’s technology-driven world, with various models offering unique features like camera quality, screen resolution, and storage. Understanding how these features influence smartphone prices can help consumers make informed purchasing decisions. This study introduces a logic mining technique to classify smartphone features that contribute to pricing using Weighted Random k Satisfiability with Modified Reverse Analysis. The model implements a Discrete Hopfield Neural Network, a Modified Niched Genetic Algorithm for training, and the Jaccard Feature Selection Method. The Phone Prices Dataset from Kaggle was used for experimentation, revealing the model’s ability to extract optimal patterns in the form of induced logic. The results show that the proposed model outperforms existing methods, achieving an accuracy of 0.8083, precision of 0.8925, specificity of 0.9760, Matthew’s correlation coefficient of 0.5334, and an F1-score of 0.5887, demonstrating its effectiveness in analyzing and classifying smartphone pricing factors.
ROBUSTNESS EVALUATION OF THE 3-SATISFIABILITY REVERSE ANALYSIS METHOD WITH DISCRETE HOPFIELD NEURAL NETWORK AND GENETIC ALGORITHM FOR TRAFFIC FLOW DATASET Amierah Abdul Malik; Mohd. Asyraf Mansor; Nur Ezlin Zamri; Nurul Atiqah Romli
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2413-2426

Abstract

Traffic flow congestion is a pervasive global phenomenon. Nonetheless, the systematic analysis and identification of traffic flow patterns remain a challenge as the volume of traffic data increases. Consequently, robust data extraction methods are required to uncover underlying data patterns. This paper proposes a 3-Satisfiability logic mining approach using a Discrete Hopfield Neural Network, develops the 3-Satisfiability Reverse Analysis method by integrating the Discrete Hopfield Neural Network with a Genetic Algorithm, and implements this method on traffic flow datasets, comparing its accuracy with existing approaches. The 3-Satisfiability Reverse Analysis method employs 3-Satisfiability for logical representation and integrates a Discrete Hopfield Neural Network with a Genetic Algorithm as its learning system. A simulation was conducted using the Urban Traffic dataset for São Paulo, Brazil. The robustness of the method in extracting relationships within traffic flow data was evaluated using selected performance metrics. The results indicated that the proposed 3-Satisfiability Reverse Analysis method, which integrates the Discrete Hopfield Neural Network and Genetic Algorithm, achieved promising performance with an accuracy rate of 80%, outperforming existing methods
Co-Authors Amierah Abdul Malik Mohd Shareduwan Mohd Kasihmuddin Nur Ezlin Zamri Nurul Atiqah Romli Nurul Atiqah Romli Nurul Najwa Ahmad Azam