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JTAM (Jurnal Teori dan Aplikasi Matematika)
Published by Universitas Muhammadiyah Mataram
ISSN : 25977512     EISSN : 26141175     DOI : 10.31764/jtam
Core Subject : Education,
Mathematics
Jurnal Teori dan Aplikasi Matematika (JTAM) dikelola oleh Program Studi Pendidikan Matematika FKIP Universitas Muhammadiyah Mataram dengan ISSN (Cetak) 2597-7512 dan ISSN (Online) 2614-1175. Tim Redaksi menerima hasil penelitian, pemikiran, dan kajian tentang (1) Pengembangan metode atau model pembelajaran matematika di sekolah dasar sampai perguruan tinggi berbasis pendekatan konstruktivis (PMRI/RME, PBL, CTL, dan sebagainya), (2) Pengembangan media pembelajaran matematika berbasis ICT dan Non-ICT, dan (3) Penelitian atau pengembangan/design research di bidang pendidikan matematika, statistika, analisis matematika, komputasi matematika, dan matematika terapan.
Arjuna Subject : -
Articles 26 Documents
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Search results for , issue "Vol 8, No 3 (2024): July" : 26 Documents clear
Analysis of Critical Criteria for Assessment of Logistics Service Provider Company (Case Study: PT. Pos Logistik Indonesia) Suzana, Yenny; Irwansyah, Budi; Suparni, Suparni; Darmawan, Wanda
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.21670

Abstract

The quality of services provided by logistics providers has a direct impact on an organization's business. Organizations need to critically evaluate the performance of their LSPs and choose service providers rationally. LSPs understand the importance of service quality to their survival and growth and strive to provide high-quality services. PT Pos Logistik Indonesia (PLI) is a company that operates in the field of Logistics Delivery Services which can include documents, electronic products, logistics, and others.  PT Pos Logistik Indonesia (PLI) in Langsa City should have criteria as a logistics service provider to ensure customer satisfaction. This research aims to analyze these criteria through a survey collected from 120 Small and Medium Enterprises (SMEs) organizations in Langsa City that use the services of PT Pos Logistik Indonesia (PLI). Small and Medium Enterprises (SMEs) are individual business entities or legal entities that have small initial capital, or a small value of wealth (assets) and a small (limited) number of employees. Questionnaire strategies were used to identify criteria selection-based sustainable logistics service quality (SLSQ) theoretical framework, that is sustainable transport elements, training, collaboration, sustainable packaging, and sustainable information. The explanatory factor analysis method was employed. The top three ranked criteria for evaluating logistics service provider criteria selection based on sustainable logistics service quality are the trained PLI employees, the sustainable packaging, and the commitment to environmental goals. Through this research, it confirms that sustainable logistics practices can bring value to PLI as LSPs and better improve their performance and customer satisfaction.
Dynamical Analysis of a Predator-Prey Model Involving Intraspecific Competition in Predator and Prey Protection Resmawan, Resmawan; Nuha, Agusyarif Rezka; Nasib, Salmun K.; Nashar, La Ode
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22154

Abstract

This article explains the interaction of the prey-predator model in the presence of wild harvesting and competition intra-specific predator populations and prey protection zones.  Model construction is based on literature studies related to the basic theory of the model and the biological properties between predator and prey populations. This study aims to look at the dynamic conditions of the predator-prey model in the form of the existence of prey and predator populations and the impact that occurs in the long term for both populations due to changes in parameter values. The model analysis begins with the formulation of the solution conditions and boundaries model, and next with the determination of the equilibrium point. Every equilibrium point is analyzed by the characteristic of its stability is neither local or global. The model owns three equilibrium points, namely the equilibrium point of population extinction (E_0), the equilibrium point of predator extinction (E_1), and the equilibrium point of persistence of the two populations (E_2). These equilibrium points are stable locally or globally if certain conditions are met. Next, it is shown that bifurcation proceeds Which describes the changing of characteristic stability point equilibrium Which depends on the threshold parameter values h_1, Ω^*, and ρ^*. In the end, numerical simulations are presented in the form of phase, time-series, and bifurcation diagrams to support the analytical results of the model, as well as to visually show the dynamic behaviour of the interaction between the two populations based on changes in predation levels, illegal harvesting, prey refuge zones, and intra-specific competition.
Augmented Reality for Mathematics Learning: A Study for Enhancing Mathematical Comprehension in High School Students Yanuarto, Wanda Nugroho; Suanto, Elfis; Isnawan, Mohamad Galang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22778

Abstract

Numerous initiatives have sought to incorporate novel learning environments or technology, including Augmented Reality, into delivering more comprehensive education. But it's crucial to look at how this technology or settings impact different types of students. Research purposes: the main purposes of this study are the degree to which students are receptive to adopting augmented reality software as a learning tool and the effectiveness of such software in improving students' comprehension of probability and statistics in junior high school. Seventy-seventh graders from Purwokerto City, Indonesia's junior highs bordering urban and rural areas, participated. Research methods: two groups of students could be selected: one for the experiment and another for control purposes. In contrast to the control group, who stuck with tried-and-true teaching techniques, the experimental group conducted additional exploration of probability concepts either alone or in small groups using custom-built augmented reality software. While the control group continued to use more traditional methods of education, the experimental group utilised custom-built augmented reality software to explore probability concepts further, either individually or in small groups. All three courses met for a total of sixty-two days. Research results: The findings from the study showed that students' grasp of mathematical ideas can be improved by around 25.6% with the help of augmented reality learning apps. Furthermore, we analyse the differences in student learning and inquiry behaviours between two experimental conditions that differ in the complexity of augmented reality information. Furthermore, the results of the attitude questionnaire and the open-ended questions (5 items questions) corroborate the students' good opinions towards applications. However, in the future, researchers may look at how augmented reality affects students' more subjective characteristics, such as learning anxiety, and broaden the demographic of those who use these apps.
An Analysis of Water Infiltration in Furrow Irrigation Channels with Plants in Various Types of Soil in the Special Region of Yogyakarta Using Dual Reciprocity Boundary Element Method Irene, Yanne; Manaqib, Muhammad; Ramadhanty, Vina Wulandari; Ria Affriani, Asri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.19873

Abstract

The analysis of water infiltration channels requires significant time and cost when conducted through laboratory experiments. Alternatively, mathematical modeling followed by numerical method can be employed. The mathematical model of water infiltration in furrow irrigation channels takes the form of a boundary value problem, with the Helmholtz equation serving as the governing equation. The Dual Reciprocity Boundary Element Method (DRBEM) is a numerical method derived from the Boundary Element Method (BEM), utilized for solving partial differential equations encountered in mathematical physics and engineering. This research employs DRBEM to analyze infiltration in trapezoidal irrigation channels with root-water uptake across various homogeneous soil types prevalent in agricultural lands in each District/City of the Yogyakarta Special Region Province. The results demonstrate that DRBEM provides numerical solutions for suction potential, water content, and root water absorption for each soil type. It was found that sandy soil exhibits high water content but has a low rate of root water absorption. On the other hand, clayey soil has low water content but a higher rate of root water uptake. These findings indicate that sandy soil, such as those found in Sleman District and Yogyakarta city, are less efficient in water usage when employing the furrow irrigation system, whereas clayey soil, as found in Gunung Kidul regency, is more effective.
Effectiveness of Machine Learning Models with Bayesian Optimization-Based Method to Identify Important Variables that Affect GPA R, Arifuddin; Syafitri, Utami Dyah; Erfiani, Erfiani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.21711

Abstract

To produce superior human resources, the SPs-IPB Master Program must consider the factors influencing the GPA in the student selection process. The method that can be used to identify these factors is a machine learning algorithm. This paper applies the random forest and XGBoost algorithms to identify significant variables that affect GPA. In the evaluation process, the default model will be compared with the model resulting from Bayesian and random search optimization. Bayesian optimization is a method for optimizing hyperparameters that combines information from previous iterations to improve estimates. It is highly efficient in terms of computing time. Based on a balanced accuracy and sensitivity metrics average, Bayesian optimization produces a model superior to the default model and more time-efficient than random search optimization. XGBoost sensitivity metric is 25% better than random forest. However, random forest is 19% better in accuracy and 30% in specificity. Important variables are obtained from the information gain value when splitting the tree nodes formed. According to the best random forest and XGBoost model, variables that have the most influence on students' GPA are Undergraduate University Status (X8) and Undergraduate University (X6). Meanwhile, the variables with the smallest influence are Gender (X4) and Enrollment (X9).
Robust Continuum Regression Study of LASSO Selection and WLAD LASSO on High-Dimensional Data Containing Outliers Daulay, Nurmai Syaroh; Erfiani, Erfiani; Soleh, Agus M
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.23123

Abstract

In research, we often encounter problems of multicollinearity and outliers, which can cause coefficients to become unstable and reduce model performance. Robust Continuum Regression (RCR) overcomes the problem of multicollinearity by reducing the number of independent variables, namely compressing the data into new variables (latent variables) that are independent of each other and whose dimensions are much smaller and applying robust regression techniques so that the complexity of the regression model can be reduced without losing essential information from data and provide more stable parameter estimates. However, it is hampered in the computational aspect if the data has very high dimensions (p>>n). In the initial stage, it is necessary to reduce dimensions by selecting variables. The Least Absolute Shrinkage and Selection Operator (LASSO) can overcome this but is sensitive to the presence of outliers, which can result in errors in selecting significant variables. Therefore, we need a method that is robust to outliers in selecting explanatory variables such as Weighted Least Absolute Deviations with LASSO penalty (WLAD LASSO) in selecting variables by considering the absolute deviation of the residuals. This method aims to overcome the problem of multicollinearity and model instability in high-dimensional data by paying attention to resistance to outliers. Leverages the outlier resistant RCR and variable selection capabilities of LASSO and WLAD LASSO to provide a more reliable and efficient solution for complex data analysis. Measure the performance of RKR-LASSO and RKR-WLAD LASSO; simulations were carried out using low-dimensional data and high-dimensional data with two scenarios, namely without outliers (δ= 0%) and with outliers (δ= 10%, 20%, 30%) with a level of correlation (ρ = 0.1,0.5,0.9). The analysis stage uses RStudio version 4.1.3 software using the "MASS" package to generate data that has a multivariate normal distribution, the "glmnet" package for LASSO variable selection, the "MTE" package for WLAD LASSO variable selection. The simulation results show the performance of RKR-LASSO tends to be superior in terms of model goodness of fit compared to RKR-WLAD LASSO. However, the performance of RKR-LASSO tends to decrease as outliers and correlations increase. RKR-LASSO tends to be looser in selecting relevant variables, resulting in a simpler model, but the variables chosen by LASSO are only marginally significant. RKR-WLAD LASSO is stricter in variable selection and only selects significant variables but ignores several variables that have a small but significant impact on the model.

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