Jurnal Diferensial

IMPLEMENTATION OF PARTITIONING AROUND METHOD (PAM) IN IDENTIFYING THE CHARACTERISTICS OF NTT PROVINCE'S GNP

2024-01-11T10:44:08+00:00

For the aspect of economic growth in NTT Province, it is necessary to understand regional characteristics in order to know the potential of each region. The aims of this study were to found the potential of regions in NTT that can increase the rate of economic growth by applying \textit{cluster} analysis, namely the Partitioning Around Method (PAM). The data used is based on data from BPS with variables of GDP, Labor Force, Unemployment, Average Length of Schooling and Investment in 2022. The results showed that optimal \textit{clusters} were obtained as many as 2 groups. \textit{Cluster} 2 shows a larger average value than \textit{cluster} 1. This means that districts/cities that tend to have higher GDP potential as well as labor force, unemployment, average length of schooling and investment are contained in \textit{cluster} 2. Meanwhile, districts/cities in \textit{cluster} 1 show a lower average.

STUDENT GRADUATION PREDICTION USING DECISION TREE METHOD WITH C4.5 ALGORITHM

2024-01-09T03:56:52+00:00

One of the determinants of the quality of higher education is the percentage of student’s ability to complete their studies on time. However, in practice, few students can complete their studies in higher education on time. Graduation prediction is one of the things that can be done to increase student graduation so that early prevention or handling of students who have the opportunity to not graduate on time can be done. The aim of this research is to find out and analyze the use of the Decision Tree Method with the application of the C4.5 Algorithm to effectively predict student graduation on time. The data used is that of Mathematics students at UIN SUSKA RIAU's Faculty of Science and Technology. The decision tree was constructed using 150 training data and processed using the C4.5 algorithm to generate 40 rules, which were then tested using 150 data sets with an accuracy of 78.6667 percent and an AUC of 0.8363.

ON THE NUMERICAL SIMULATION OF THE EFFECT OF VACCINE ON MEASLES USING VARIATIONAL ITERATION METHOD

2024-01-09T03:56:52+00:00

The prevalence of measles as an infectious disease has been of major concern to the government health practitioners over the world. This paper intends to investigate the effect of vaccination as a control measure to control the incidence of it means. The basic reproduction number, local and global stabilities of the disease at equilibrium, sensitivity was obtained. The numerical simulation via variational iteration method was carried out. The result clearly shows that proper procurement of vaccine and its implementation is a good control strategy to reduce the rapid spread of the disease.

COLORING r-DYNAMIC POINT ON CRICKET GRAPH

2024-01-11T10:44:42+00:00

A graph is defined as an ordered set (V,E) where V is a non-empty set of elements called vertices and E is a set of edges which are finite and may be empty and each edge connects two different points of V(G).The r-dynamic coloring is defined as c:V(G)→{1,2,3,…,k} such that it satisfies the following conditions if uv∈V(G), then c(u)≠ c(v), and ∀v∈V(G), |c(N(v))|≥min⁡{r,d(v)}, for positive integers r and degree of vertex v. The purpose of r-dynamic coloring is to find the minimum chromatic number of graph coloring with unlimited parameter r. Dynamic coloring is performed on cricket graphs because no research has been done before. The method used in this research is the axiomatic deductive research method and the pattern detection method.

ANALYSIS EFFECTIVENESS OF GEOGRAPHICALLY WEIGHTED QUANTILE REGRESSION (GWQR) MODEL IN HANDLING OUTLIERS: SIMULATION DATA IDENTIFIED SPATIAL HETEROGENEITY

2024-02-09T10:23:04+00:00

Classical quantile regression is global generalized parameter estimation results, spatial heterogeneity conditions cannot be captured by this model. The use of local models with spatial attribute can accommodate the characteristics between observation locations. The local quantile regression model is called the Geographically Weighted Quantile Regression (GWQR) model. Further testing of the effectiveness of this model is required by utilizing simulation data. This study uses simulated data generated with sample sizes uniformly distributed with intervals (0,1) contaminated with 5%, 10%, 15% outliers, with predictor variables (x=4) (β₁,β₂,β₃,β₄), and quantile sizes of 0.05, 0.25, 0.50, 0.75 and 0.95. Model effectiveness is measured based on Root Mean Square Error (RMSE). From the test, GWQR model can overcome the problem of outliers in simulated data up to the amount of outlier contamination of 15%, and spatial heterogeneity. The RMSE value is getting closer to 0 as the sample size and outliers increase. The test results explain that the 0.95 quantile produces the best parameter estimates compared to other quantiles.

MIXED METRIC DIMENSION OF DOUBLE FAN GRAPH

2024-02-09T10:54:47+00:00

Let G=(V(G),E(G)) be a simple connected graph. A vertex x∈ V(G) resolves the elements u,v∈ V(G)∪ E(G if d(x,u)≠ d(x,v). The mixed metric dimension, has been recently introduced. The mixed metric dimension (mdim(G)) of a graph G is the cardinality of a smallest set of vertices that resolves each pair of elements from V(G)∪ E(G). In this paper, we will identify the mixed metric dimension on double fan graph.

GRACEFUL CHROMATIC NUMBER OF THE FAMILY OF CENTRIPETAL GRAPH

2024-02-09T11:50:24+00:00

One of the topics studied in graphs is graph coloring. The definition of a graceful coloring, namely $k$-elegant coloring of a graph G is the exact vertex coloring c:V(G)→{ 1,2,...,k} where k≥2 induces the exact vertex coloring c^': V(G)→ {1,2,...,k-1} which is defined by c(uv)=|c(u)-c(v)|. The exact vertex coloring c of a graph G is a graceful coloring if c is a k-elegant coloring for k∈N. The graceful chromatic number is the minimum k value where graph G has k-elegant coloring, the elegant chromatic number of graph G is denoted by X_g (G). This article will discuss graceful chromatic numbers in the centripetal graph family which includes octopus graph (O_n), sandat graph (St_n),dutch windmill graph (D_3^m) , and a volcano graph (V_n).

ON THE NUMERICAL SIMULATION OF THE EFFECT OF DISEASE TRANSMISSION COEFFICIENT ON SEIR EPIDEMIC MODEL USING HYBRID BLOCK METHOD

2024-02-09T14:05:05+00:00

This work contains the study, analysis, and solution of a SEIR epidemic model. An effective hybrid block method was used in the solution of the system of linear differential equations to investigate the effect of transmission coefficient on the SEIR model with a permanent immunity. Numerical simulation of the included control parameters are carried out. The obtained results and outcomes are presented graphically.

DETERMINING THE BEST DEFUZZIFICATION METHOD FOR THE MAMDANI FUZZY INFERENCE SYSTEM IN THE DIAGNOSIS OF PRE-ECLAMPSIA IN PREGNANT WOMEN

2024-04-14T18:29:56+00:00

Pre-eclampsia is the second of the three major causes of death in pregnant women after bleeding followed by infection. Pre-eclampsia is a disorder of unknown etiology specifically in pregnant women. To prevent pre-eclampsia from becoming increasingly severely diagnosed systems that can be used for pre-eclampsia premises syconome. One method that can be used to determine the pre-eclampsia diagnosis is the Fuzzy Inference System (FIS) Mamdani this method is based on the concept of fuzzy logic. The process of determining the final decision by this method has several stages, the application of implications, rules, and defuzzification composition. For defuzzification stages, there are four methods that can be used the method Centroid, Bisector, Mean of Maximum (MOM), Smallest of Maximum (SOM), and Largest of Maximum (LOM). This study aims to determine the diagnosis of pre-eclampsia (pregnancy poisoning) in pregnant women based on FIS Mamdani by previously determining the best FIS Mamdani defuzzification method. In determining the best defuzzification method, the measures Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Sum Square Error (SSE) are used. Based on the results of the prediction error comparison, the best defuzzification method to diagnose the pre-eclampsia status in Atambua Hospital is a bisector method with an accuracy of 95,48%.