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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Mahdiyasa, Adilan Widyawan
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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MODELING CUSTOMER LIFETIME VALUE WITH MARKOV CHAIN IN THE INSURANCE INDUSTRY Mahdiyasa, Adilan Widyawan; Pasaribu, Udjianna Sekteria; Sari, Kurnia Novita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp687-696

Abstract

In the competitive insurance industry, accurately predicting Customer Lifetime Value (CLV) is vital for sustaining long-term profitability and optimizing resource allocation. Traditional static models often fail to capture the dynamic and uncertain nature of customer behavior, which is influenced by factors such as life changes, economic conditions, and evolving product offerings. To address these limitations, this paper proposes an advanced modeling approach that integrates Markov Chains with survival analysis. Markov Chains are well-suited for modeling stochastic processes, where future states depend on current conditions, while survival analysis provides insights into event timing and likelihood for estimating the insurance premium. The proposed model combines these approaches to make a more complete and accurate prediction of CLV. This helps insurers make better decisions and improves the overall performance of their business. We employ the data of customer behavior from the insurance company in Bandung, Indonesia from 1994 to 2020. We found that CLV in the insurance industry is significantly affected by customer behavior.
DYNAMIC MODELING OF CARBON DIOXIDE EMISSIONS USING HIGH-ORDER DIFFERENTIAL EQUATIONS AND NONLINEAR ESTIMATION Pasaribu, Udjianna Sekteria; Mahdiyasa, Adilan Widyawan; Irfanullah, Asrul
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/barekengvol20iss2pp1215-1228

Abstract

Carbon dioxide (CO₂) is one of the main factors contributing to global warming. As the second largest CO₂ emitter globally, the United States (US) faces increasing political and economic pressure to reduce its emissions. Historical emission data exhibits complex structural patterns characterized by linear growth, quadratic trends, and periodic oscillations. Most existing models fail to capture this multifaceted behavior. In this study, we propose a high-order differential equation to represent the dynamic behavior of CO₂ emissions in the US. The model integrates linear, quadratic, and oscillatory components to reflect both long-term and short-term fluctuations. Nonlinear parameter estimation techniques are employed to fit the model to historical emission data with high accuracy. The proposed model effectively captures historical emission behavior, demonstrating strong goodness of fit and identifying both trend and cyclical components. Model-based projections indicate a likely resurgence in emission growth over the next decade, raising concerns regarding compliance with climate commitments and potential exposure to international carbon pricing instruments. The findings highlight the value of combining differential equation modeling with nonlinear estimation in analyzing environmental systems. The main limitation of this study is that it focuses only on historical emission dynamics, without direct integration of socio-economic drivers. This gap, however, highlights opportunities for future research.
Co-Authors Irfanullah, Asrul KURNIA NOVITA SARI Pasaribu, Udjianna Sekteria