Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Nur Ezlin Zamri
Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia, Malaysia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
LOGIC MINING FOR TELECOMMUNICATION CHURN CLASSIFICATION: PERMUTATION WEIGHTED RANDOM 2 SATISFIABILITY REVERSE ANALYSIS APPROACH Nur Ezlin Zamri; Nurul Ain Najwa Mohamad Jamil; Nurul Atiqah Romli; Mohd Shareduwan Mohd Kasihmuddin
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/barekengvol20iss3pp2375-2388

Abstract

The telecommunications industry is experiencing rapid transformation, resulting in tense competition and increased customer volatility. Telecom churn, which refers to the discontinuation of services by customers, poses a serious challenge due to its direct impact on revenue and long-term profitability. Addressing this issue requires effective methods for understanding and predicting customer behavior. Hence, a logic mining approach is introduced in this study, namely the Permutation Weighted Random 2 Satisfiability Reverse Analysis Method, to classify customer churn in the telecommunications sector. The proposed method is based on a logical rule known as Weighted Random 2 Satisfiability, which is implemented in the Discrete Hopfield Neural Network. The logical rule facilitates the dynamic allocation of negative literals, contributing to improved logical representation. Furthermore, the Election algorithm is incorporated during the training phase to enhance the accuracy of logical structure interpretation. The proposed method is capable of extracting optimal data patterns and generating induced logic that accurately describes customer churn behaviour. This induced logic not only predicts whether a customer will churn but also provides interpretable insights into the underlying causes. Experimental results demonstrate a strong average accuracy of 85.6%, indicating the effectiveness and scalability of the proposed approach for knowledge discovery. Although the proposed approach achieves strong accuracy, the lower F1-Score and Matthews Correlation Coefficient reveal limitations in churn customer classification, highlighting the need for further improvement in handling class imbalance. This study contributes to the field of data mining by offering a logic-based framework for churn classification and emphasizing its practical relevance in supporting strategic customer retention efforts in a competitive telecommunications sector.
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 Mohd. Asyraf Mansor Nurul Ain Najwa Mohamad Jamil Nurul Atiqah Romli Nurul Atiqah Romli Nurul Najwa Ahmad Azam