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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JTAM (Jurnal Teori dan Aplikasi Matematika) Indonesian Journal on Learning and Advanced Education (IJOLAE)
Raza, Wasim
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Computer Science & IT Education Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Novel Distance-Based Molecular Descriptors for Styrene Butadiene Rubber Structures Chidambaram, Natarajan; Kamran, Muhammad; Balasubramanian, Deepa; Hameed, Shahzaib; Mehmood, Saba; Raza, Wasim; Khan, Aamir Hussain; Iqbal, Zainab
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20037

Abstract

Styrene-butadiene rubber (SBR) is a general-purpose rubber produced from a copolymer of styrene and butadiene.  It is used largely in automobile and truck tires. In general, it is considered to be an abrasion-resistant replacement for natural rubber.  In this article, we compute a set of recently introduced distance-based topological descriptors namely, Zagreb connection indices and reformulated Zagreb connection indices for the SBR structures. Zagreb indices and Reformulated Zagreb indices  were applied to forecast numerous molecular properties through QSAR studies of boiling point, Molar volume and bioactivity. These indices offer a quick and effective measure of molecular complexity and features of their composition resulting in approximate molecular behavior.
Fuzzy Logic Speed Regulator for D.C. Motor Tuning Raza, Wasim; Adzikya, Dieky; Mehmood, Saba; Wasti, Syeda Rabbia; Hussain, Muhammad Jafar; Ahmad, Aftab; Usman, Muhammad Talha; Raza, Sajid
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.16919

Abstract

A D.C. motor's rotational speed is regulated in this study using a PID controller and a fuzzy logic controller. In contrast to the fuzzy logic controller, which uses rules based on knowledge and experience, the proportional-integral-derivative (PID) controller requires a mathematical system model.   This study investigates the regulation of a DC motor's velocity using PID and fuzzy logic controllers. The PID controller utilizes a mathematical model and parameter tuning by trial and error. Still, the fuzzy logic controller (FLC) operates on rule-based knowledge, enabling it to handle the nonlinear features of the DC motor effectively. The FLC design entails intricate determinations, including the establishment of a rule base and the process of fuzzification. A total of 49 fuzzy rules have been devised to achieve precise control. Based on MATLAB/SIMULINK simulations, the study concludes that the Fuzzy Logic Controller (FLC) beats the Proportional-Integral-Derivative (PID) controller. The FLC exhibits superior transient and steady-state responses, shorter response times, reduced steady-state errors, and higher precision. This study emphasizes the efficacy of the FLC (Fuzzy Logic Controller) in dealing with the difficulties associated with DC motor control. It presents a strong argument for the suitability and efficiency of FLCs in industrial environments compared to conventional PID (Proportional-Integral-Derivative) controllers. There are a wide variety of ways to construct a fuzzy logic controller. The speed error and the rate of change in the speed error are two inputs to the FLC. Defuzzification is done by focusing on the core of the problem. The results show that FLC is superior to PID controllers in efficiency and effectiveness due to its reduced transient and steady-state factors.
COVID-19 Predictions Using Regression Growth Model in Ireland and Israel Raza, Wasim; Adzkiya, Dieky; Subchan, Subchan; Mehmood, Saba
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i4.10944

Abstract

The World Health Organization (WHO) asserted the recently discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, a pandemic on March 11, 2020. Since the genesis and growth mechanisms of this virus are unclear and impossible to detect, there are still many uncertainties concerning it and no vaccination or effective treatment. The main goal is to halt its global spread. This paper employed a regression growth model with an extended Weibull function on the dynamics of COVID-19 to make predictions about its spread. Our findings demonstrate the viability of using this model to forecast the spread of the virus. Using a logistic growth regression model, the note tabulates the COVID-19-related final epidemic sizes for a few sites, including Ireland and Israel.
PERFORMANCE EVALUATION OF NEWTON–KONTOROVICH AND ADAPTIVE NEWTON LINE SEARCH ON MULTIVARIATE NONLINEAR SYSTEMS Muslimin, Ikhwanul; Syaharuddin; Mandailina, Vera; Mehmood, Saba; Raza, Wasim
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7370

Abstract

Solving multivariate nonlinear systems is essential in engineering, physics, and applied sciences. This study compares the performance of two numerical methods—Newton–Kontorovich and Interactive Newton–Raphson with Line Search—on trigonometric and exponential nonlinear systems. The methods are evaluated based on convergence rate, accuracy, and iteration efficiency through numerical simulations using MATLAB. The Newton–Kontorovich method, typically used for integral or differential equations, is compared with the adaptive line search strategy that enhances global convergence. Results show that the Interactive Newton–Raphson method achieves a smaller final error (5.95×10⁻²) with stable convergence, while Newton–Kontorovich converges in fewer iterations but with larger error (3.126). These findings highlight the superiority of adaptive strategies for complex nonlinear systems. Practical implications include improved numerical reliability for applications in structural engineering, optimization, and scientific modeling.
Optimizing Student Motivation and Learning Outcomes Through Group Investigation Cooperative Learning Model on Ratio Material: A Study of Mathematical Reasoning Ability Anggriyani, Meli; Syaharuddin, Syaharuddin; Mandailina, Vera; Abdillah, Abdillah; Raza, Wasim
Indonesian Journal on Learning and Advanced Education (IJOLAE) Vol. 7, No. 3, September 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijolae.v7i3.8743

Abstract

This study aims to examine the effectiveness of the application of the Group Investigation cooperative learning model in optimizing student motivation and learning outcomes on ratio material, as well as analyzing its contribution to mathematical reasoning skills. This study used a quantitative approach with a pre-test and post-test design involving 20 junior high school students in grade VII. Data were obtained through mathematical reasoning ability test, motivation questionnaire, and analysis of students' learning outcomes. The results showed an increase in the mean score from pre-test to post-test by -6.50 (p = 0.001) which reflects more uniform student learning outcomes. Regression analysis showed a very strong relationship between the independent variable (mathematical reasoning) and the dependent variable (motivation and learning outcomes) with an R value of 0.929 and R2 of 86.4%, indicating that motivation and learning outcomes explained most of the variation in reasoning ability. Specifically, the contribution of mathematical reasoning (X) to student learning outcomes (Y2) was 38.94% while the contribution of motivation (Y1) to mathematical reasoning reached 45.83%. In addition, regression analysis shows that motivation has a significant influence on mathematical reasoning ability, while learning outcomes have a positive but statistically insignificant influence. This finding indicates that the Group Investigation cooperative learning model is not only effective in improving student learning outcomes, but also has an important role in strengthening mathematical reasoning skills through increased motivation. Therefore, cooperative learning strategies that emphasize increased motivation need to be applied in mathematics learning to optimize students' analytical thinking skills.
Co-Authors Abdillah Abdillah Adzikya, Dieky Aftab Ahmad, Aftab Anggriyani, Meli Balasubramanian, Deepa Chidambaram, Natarajan Dieky Adzkiya Hameed, Shahzaib Hussain, Muhammad Jafar Iqbal, Zainab Kamran, Muhammad Khan, Aamir Hussain Mehmood, Saba Muslimin, Ikhwanul Raza, Sajid Subchan, Subchan Syaharuddin Syaharuddin Syaharuddin Usman, Muhammad Talha Vera Mandailina, Vera Wasti, Syeda Rabbia