Jurnal Diferensial

Implementasi Fuzzy Mamdani pada Sistem Pendukung Keputusan Pemilihan Mobil Listrik

Ikha Puspita Parwitasari — 2025-12-31

The rapid development of electric vehicles (EVs) has led to a wide variety of models with different specifications and prices, requiring a method that can evaluate multiple criteria simultaneously. This study applies the Mamdani fuzzy inference system to assess the suitability of EVs based on six key variables: price, production year, driving range, passenger capacity, power, and battery capacity. Triangular membership functions are used to represent the linguistic variables, and the rule base reflects realistic decision preferences. Through fuzzification, Mamdani inference, aggregation, and defuzzification, crisp suitability scores are produced for each vehicle. Results show that EVs with high specifications and proportional prices achieve the highest scores, while those with high prices but low specifications rank lowest. The fuzzy Mamdani approach effectively integrates linguistic and subjective criteria to support structured decision-making in EV selection.

A Solutions of the Linearized Two-Dimensional Generalized Dispersive Wave Equation with Mixed Derivative via the Residual Power Series Method

Nawzad Hasan Ali — 2026-02-10

This article applies the Residual Power Series Method (RPSM) to solve the Linearized Two-Dimensional Generalized Dispersive Wave Equation (L-2DGDWE) featuring the mixed derivative term $u_{xt}$. The RPSM is based on the general Taylor series formula combined with a residual error function minimization. A new analytical solution is investigated in this work. The analytical solution is designed to find approximate solutions via RPSM, and these obtained results are compared with exact solutions to demonstrate the precision, reliability, and rapid convergence of the proposed method. Graphical representations at different time instances are provided to visualize the solution behavior.

An Analysis of the COVID-19 Agenda Using Big Data from Social Media: A Comparative Study across Countries with R Programming

Şakir İşleyen — 2026-03-04

Social media platforms are becoming increasingly important as sources of public discourse and real-time data analysis, as the COVID-19 epidemic has highlighted. Using the hashtag #COVID19, this study examines COVID-19-related tweets from seven nations (the US, Germany, South Korea, Iraq, Spain, Italy, and Turkey) in order to find trends in engagement and correlations. Similarities between public attitude and government communications are examined by statistical techniques such as content analysis, frequency analysis, and cross-delay correlation, as well as R programming. The findings show that tweet patterns from different countries are highly correlated, and that the Iraqi government's tweets with a typical theme were more popular than those with a COVID-19 theme. This study provides information on cross-border communication tactics in times of crisis and illustrates the potential of big data analytics for comprehending global phenomena.

Analysing Cholera-Measles Epidemics of a Fractional-Order Model with Preventive Strategies Using Laplace Adomian Decomposition Method.

Mutiu Lawal Olaosebikan — 2026-03-11

This study provides an in-depth examination of cholera-measles epidemics through a fractional-order mathematical model that integrates essential preventive measures. By employing fractional calculus, the model captures the memory and hereditary properties of disease transmission dynamics, offering a more realistic representation than classical integer-order models. This consists of multiple compartments representing the progression of each disease, with control measures such as treatment, vaccination, water sanitation and public health awareness integrated into the system. Considering numerical iteration on model to see how these changes affect the spread of disease. The results reveal that fractional-order models not only enhance the accuracy of epidemic forecasting but also demonstrate the effectiveness of timely and combined preventive strategies in reducing infection rates. Sensitivity analysis further identifies crucial parameters influencing disease dynamics, guiding resource allocation for optimal control. The findings indicate the relevance of fractional modeling and provides valuable insights for informing strategic planning efforts to curb cholera-measles transmission.