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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Matematika UNAND
Samat, Nor Azah
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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EXAMINING RISK FACTORS OF ANEMIA IN PREGNANCY USING HYBRID LOGISTIC REGRESSION MODEL AND ROUGH SET THEORY Rahmi, Izzati; Efendi, Riswan; Samat, Nor Azah; Yozza, Hazmira; Syafwan, Mahdhivan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0537-0552

Abstract

Anemia in pregnancy is a potential danger to the mother and child. Therefore, the risk of anemia in pregnant women requires serious attention from all relevant parties. Considering the numerous negative effects caused by anemia in pregnant women, efforts must be made to prevent and treat anemia in pregnant women by understanding the factors that influence it. This study assesses the risk factors for anemia in pregnant women at Tegal Rejo Community Health Center, Yogyakarta Province. In this paper, a new integrated classification approach with binary logistic regression (LR) analysis and Rough Set Theory (RST) is proposed, in order to examine factors on the incidence of anemia in pregnancy. The proposed model is called the Logistic Regression and Reduction Rough Set (LR3S). In LR3S model, the RST technique is used to detect inconsistent sample and removing inconsistent sample that have probability less than 0.5 before doing LR modelling. To evaluate the development of the resulting model, a comparison was made of the performance of Original Logistic Regression (OLR), LR model after removed outlier namely as Remove Outlier Original Logistic Regression (RO2LR) and LR3S. Using a number of model performance metrics, it is found that LR3S has the best performance for the three models used. Using LR3S model, it is found that CED status, educational level, parity and gestational are significant variable impact on the incidence of anemia.
DEVELOPMENT OF A MATHEMATICAL MODEL FOR SMARTPHONE ADDICTION USING A DETERMINISTIC COMPARTMENTAL APPROACH Amalina, Amalina; Samat, Nor Azah; Sabri, Ahmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1359-1372

Abstract

Smartphone usage is increasing with technological advancements. However, excessive use can lead to addiction, negatively impacting an individual’s physical health, mental well-being, communication skills, and cognitive development. While previous studies have discussed the psychological and social aspects of smartphone addiction, there is still a lack of mathematical models that capture its spread at the population level. To address this gap, this study develops a deterministic compartmental model to describe the dynamics of smartphone addiction, representing real-world addiction scenarios through mathematical analysis. The novelty of this study lies in formulating a theoretical framework that identifies equilibrium points, conducts sensitivity analysis for each parameter, and employs numerical simulations to demonstrate the role of transmission rate and initial conditions in shaping addiction dynamics. The findings highlight that both transmission rate and initial conditions significantly influence the persistence of addiction, underscoring the importance of early interventions to reduce its long-term impact on the population.
Locally Weighted KNN-Based Fuzzy Regression for Property Valuation under Market Uncertainty Yozza, Hazmira; Efendi, Riswan; Samat, Nor Azah; Rahmi, Izzati; Saputra, Ridho
Jurnal Matematika UNAND Vol. 15 No. 2 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.2.163-178.2026

Abstract

Accurate evaluation of house valuation is crucial. Misestimation of house prices creates serious consequences for a variety of stakeholders. House prices can be modeled as a function of their constituent attributes. House prices are fuzzy due to negotiation and unpredictable market conditions. This study aims to develop rules to predict triangular fuzzy numbers of price for a given new house by implementing locally weighted KNN-based fuzzy regression, and to compare its performance with possibilistic fuzzy regression. The dataset used is the house valuation dataset. Data is examined using the modified Cheng and Lee k-nearest neighbor fuzzy regression and Tanaka’s possibilistic fuzzy regression. It is found that the modified Cheng and Lee k-nearest neighbor fuzzy regression outperforms possibilistic fuzzy regression in predicting the triangular fuzzy number of the house prices. The best performance is achieved when data is trained using the modified Cheng and Lee k-nearest neighbor fuzzy regression using: = 29 nearest neighbors, Minkowski distance with exponent parameter = 1.6 and an unequal weighting scheme with r = 1.
Co-Authors Ahmad Sabri Amalina Amalina, Amalina Efendi, Riswan Hazmira Yozza Izzati Rahmi HG Ridho Saputra, Ridho Syafwan, Mahdhivan