Nur Inayah

This study generalizes the dengue transmission model by considering the dynamics of the human population and the Aedes aegypti mosquito population. The mosquito population is devided into two phases, i.e., the aquatic phase and the adult phase. From the model, we seek the disease-free equilibrium, endemic equilibrium, and basic reproduction number (R0) points. The model yields a single basic reproduction number which determines the system’s behavior. If R0 1, the disease-free equilibrium is locally asymptotically stable, indicating that the disease will die out. Conversely, if R0 1, an endemic equilibrium exists, and the disease may persist in the population. Next, a numerical simulation is performed to geometrically visualize the resulting analysis and also to simulate the dengue transmission in DKI Jakarta Province, Indonesia. The resulting numerical simulation supports our analysis. Meanwhile, the simulation in DKI Jakarta Province suggests that the dengue fever disappears after 60 days from the first case appearance after controlling the mosquito population through fogging and the use of mosquito larvae repellent. Lastly, the sensitivity analysis of R0 indicates that parameters related to the mosquito’s aquatic phase have a strong influence on dengue transmission, meaning that small changes in these parameters can significantly increase or decrease the value of R0 and thus the potential for an outbreak.

On D-distance (anti)magic labelings of shadow graph of some graphs

Nowadays, sentiment analysis research in social media is rapidly developing. Sentiment analysis typically falls under supervised learning, which requires annotating data. However, the annotation process for sentiment analysis tasks is notoriously time-consuming. Fortunately, an effective strategy to overcome this challenge has emerged, known as active learning. Active learning involves labeling only a small subset of the dataset, leaving the rest for annotation through sampling strategies. This study focuses on comparing two active learning strategies: random sampling and boundary sampling. These strategies are applied to machine learning models such as logistic regression and random forests. In addition, we present an evaluation of the model performance and data savings achieved by implementing these strategies in the context of traditional machine learning for sentiment analysis on Twitter. The dataset considered consists of two labels: positive and negative sentiments. The results of our investigation show that active learning can significantly reduce the amount of training data required, saving up to 65% of the total training data required to achieve peak model accuracy. The most successful model identified uses a random forest with a margin sampling strategy, yielding an accuracy of 81.12% and an F1 score of 88.60%. This research highlights the effectiveness of active learning strategies in sentiment analysis, demonstrating their potential to improve model performance and resource efficiency. The results underscore the viability of employing active learning methods, particularly the combination of random forest models with margin sampling, for more efficient sentiment analysis in social media.

Let G be a graph with n vertices and f : V(G)→{1, 2, 3, …, n} be a bijection. The {0, 1}-vertex weight of a vertex v ∈ V(G) under f is wf[v] = f(v) + Σ_u∈N(v) f(u). If wf[v] = k for each v ∈ V(G) then f is called a {0,1}-distance magic labeling of G with magic constant k. If {wf[v] : v ∈ V(G)} is {a, a + d, a + 2d, …, a + (n – 1)d} then f is called an (a, d)-{0,1}-distance antimagic labeling of G. A graph is called a {0, 1}-distance magic graph (resp. an (a, d)-{0,1}- distance antimagic graph), if it admits a {0, 1}-distance magic labeling (resp. an (a, d)-{0,1}-distance antimagic labeling). In this article, we completely solve the problem of {0, 1}-distance magic 2-regular graphs and show that several classes of these graphs are (a, d)-{0, 1}-distance antimagic.

This research developed a model of COVID-19 based on the SEIR model which was further developed by dividing the infected subpopulation into symptomatic and asymptomatic, adding quarantine of infected individuals and vaccination in two steps. Making this model begins with making a compartment diagram of the disease and then forming a system of differential equations. After the model is formed, the disease-free equilibrium point, endemic equilibrium point, and basic reproduction number (R₀) are obtained. Analysis of the stability of the disease-free equilibrium point was locally asymptotically stable if R₀1. Numerical simulation for the model that has been made is in line with the analysis. Furthermore, the sensitivity analysis obtained that the parameters that have a significant effect on the spread of COVID-19 are the rate of the first dose vaccination, the rate of contact with symptomatic or asymptomatic individuals, and the rate of quarantine of symptomatic infected individuals.

In this paper, we study the total product and total edge product cordial labeling for dragonfly graph Dg_n. We also define generalized dragonfly graph and find product cordial and total product cordial labeling for this family of graphs.

Let be a connected graph and be a proper -coloring of . Let be a partition of vertices of induced by the coloring . We define the color code of a vertex as an ordered -tuple that contains the distance between each partition to the vertex . If distinct vertices have distinct color code, then is called a locating -coloring of . The locating chromatic number of is the smallest such that has a locating -coloring. In this paper, we determine the locating chromatic number of book graph.