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All Journal Jurnal Sains dan Teknologi Jurnal Elemen JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Ulul Albab JTAM (Jurnal Teori dan Aplikasi Matematika) Mosharafa: Jurnal Pendidikan Matematika Advance Sustainable Science, Engineering and Technology (ASSET)
Mehmood, Saba
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Humanities Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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The geometric concepts of the Istana Dalam Loka traditional house: An ethnomathematics study Meyundasari, Maulidia Dwi; Hastuti, Intan Dwi; Syaharuddin, Syaharuddin; Mehmood, Saba
Jurnal Elemen Vol 10 No 2 (2024): May
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jel.v10i2.25208

Abstract

This study explores the philosophical aspects of traditional architecture and ethnomathematics geometry in the Istana Dalam Loka traditional house, located in West Nusa Tenggara. Employing a qualitative approach with an ethnographic design, data were gathered through observations and interviews with key, primary, and additional informants. Findings indicate that mathematical concepts are reflected in every architectural element, such as rectangles in doors, terraces, stairs, shields (kantar), kandaga, the sultan's palanquin (tandu), pineapple decoration, and frames. The study also identifies square concepts in the sultan's child's palanquin, glass boxes, and windows. Circular concepts are found in badong, and the concept of an isosceles triangle is present in the roof, offering potential for creative geometry learning. This research has implications in enriching students' mathematical education by utilising geometric examples from local culture. Integrating ethnomathematics into elementary school curricula can broaden students' cultural perspectives, increase student engagement in learning, and enrich students' understanding of mathematics.
Evaluating the Effectiveness of Artificial Intelligence Models in Predicting Economic Indicators: an in-Depth Review Mehmood, Saba; Raza, Wasim
Ulul Albab: Majalah Universitas Muhammadiyah Mataram Vol 29, No 1 (2025): Januari
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jua.v29i1.30341

Abstract

Abstrak: Di era digital saat ini, kecerdasan buatan (artificial intelligence/AI) memainkan peran yang semakin penting dalam analisis ekonomi; namun, efektivitas berbagai model AI dalam memprediksi indikator ekonomi masih memerlukan evaluasi menyeluruh. Penelitian ini bertujuan untuk mengatasi kesenjangan ini dengan menilai efektivitas model AI dalam konteks yang lebih luas melalui pendekatan tinjauan literatur yang sistematis. Penelitian ini mengidentifikasi metode yang efektif dan mengeksplorasi tantangan dan keberhasilan yang terkait dengan implementasinya. Dengan menggunakan pendekatan penelitian kualitatif dan tinjauan literatur sistematis, literatur yang digunakan bersumber dari database pengindeksan seperti Scopus, DOAJ, dan Google Scholar, dengan tanggal publikasi mulai dari tahun 2014 hingga 2024. Hasil evaluasi menunjukkan bahwa model AI, khususnya deep learning dan model hybrid, menawarkan keuntungan yang substansial dibandingkan metode konvensional dalam memprediksi indikator ekonomi. Jaringan syaraf, seperti LSTM dan CNN, unggul dalam menangkap pola temporal dan spasial yang kompleks, sementara model hibrida meningkatkan akurasi prediksi dengan mengintegrasikan berbagai teknik AI. Penggabungan sumber data alternatif, seperti media sosial dan tren penelusuran, memberikan wawasan tambahan di luar data ekonomi tradisional, sehingga memperkaya prediksi. Explainable AI (XAI) semakin mendukung efektivitas model-model ini dengan meningkatkan transparansi dan kepercayaan di antara para pemangku kepentingan. Selain itu, Natural Language Processing (NLP) meningkatkan akurasi prediksi dengan menganalisis sentimen pasar dan berita ekonomi, sehingga menambah konteks yang berharga.Abstract: In the current digital era, artificial intelligence (AI) plays an increasingly pivotal role in economic analysis; however, the effectiveness of various AI models in predicting economic indicators still requires thorough evaluation. This research aims to address this gap by assessing the effectiveness of AI models within a broader context through a systematic literature review approach. The study identifies effective methods and explores the challenges and successes associated with their implementation. Employing a qualitative research approach and systematic literature review, the literature used is sourced from indexing databases such as Scopus, DOAJ, and Google Scholar, with publication dates ranging from 2014 to 2024. The evaluation results reveal that AI models, particularly deep learning and hybrid models, offer substantial advantages over conventional methods in predicting economic indicators. Neural networks, such as LSTM and CNN, excel at capturing complex temporal and spatial patterns, while hybrid models enhance predictive accuracy by integrating various AI techniques. The incorporation of alternative data sources, such as social media and search trends, provides additional insights beyond traditional economic data, enriching predictions. Explainable AI (XAI) further supports the effectiveness of these models by increasing transparency and trust among stakeholders. Additionally, Natural Language Processing (NLP) enhances predictive accuracy by analyzing market sentiment and economic news, thereby adding valuable context.
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.
Wind Speed Regression Model in Forecasting Wave Height in the Shipping Channel Zone Asmaul Husnah; Abdillah, Abdillah; Vera Mandailina; Syaharudin, Syaharudin; Mehmood, Saba
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 1 (2023): April
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i1.50981

Abstract

Nowadays, there are often accidents such as the sinking of ships in water areas that are caused by weather factors, one of which is the extreme wind speed. Wind speed and strong waves in the sea are interrelated because the factor that affects the strength of the wave blow is wind speed. This study aims to provide information about the angina speed regression model to find out the wave height at the sea crossing port. This research is a quantitative research. The data used is in the form of wind speed data taken at the coordinates of Lembar Port and Labuan Bajo Port from the NASA sub-agroclimatology website from January 1, 2012, to December 31, 2021 (for 10 years). This case study uses linear regression analysis and is operated using SPSS software. The data shows that in Lembar Port has the most extreme wind speed fluctuations in the 6th and 7th years. While the wind speed fluctuations in Labuan Bajo are consistent, but slightly extreme in the 7th year surpassing the 1st year. The results showed that the R-Square of Lembar port was 15.4% while the R-Square of Labuan Bajo port was 16.2%. The results of this study can be used in formulating marine transportation safety policies in both regions.
Improved Problem-Solving Skills Using Mathematics Module Nadila, Desi; Mandailina, Vera; Mahsup; Mehmood, Saba; Abdillah; Syaharuddin
Mosharafa: Jurnal Pendidikan Matematika Vol. 12 No. 2 (2023): April
Publisher : Department of Mathematics Education Program IPI Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/mosharafa.v12i2.798

Abstract

Banyaknya peneliti yang telah melakukan penelitian yang berkaitan dengan peningkatan kemampuan pemecahan masalah dengan menggunakan modul matematika. Tujuan dari penelitian ini adalah untuk menganalisis kembali hasil-hasil penelitian pada perbandingan tingkat hasil belajar siswa terhadap pemecahan masalah dengan menggunakan modul matematika, berdasarkan jenjang pendidikan. Metode yang di gunakan dalam penelitian ini adalah Meta-analisis. Hasil penelusuran ditemukan sebanyak 61 data yang memenuhi syarat dan ketentuan dari database Geogle Scolar, DOAJ, dan Scopus dari jumlah peserta didik (N), nilai uji fisher (F), nilai uji student (t), nilai uji korelasi (r) dan ketuntasan klasikal yang diambil bersdasarkan dengan kata kunci. Dan hasil data akan dianalisis menggunakan sofware JASP dengan menginput nilai Effect Size (ES) dan standart Error (SE) dari tiap data. Dari hasil analis data dapat terlihat peningkatan yang signifikan. Hasil analisis data menunjukkan tingkat perbandingan hasil pemecahan masalah menggunakan modul matematika pada semua jenjang, pada jenjang SD sebesar 96% (kategori tinggi), tingkat SMP sebesar 99% (kategori tinggi), tingkat SMA 101% (kategori tinggi) dan PT sebesar 86% (kategori tinggi). Disimpulkan tingkat perbandingan pada jenjang SMA lebih tinggi dibandingkan dengan jenjang SD, SMP, dan PT. Many researchers have conducted research related to improving mathematical problem-solving skills using mathematical modules. The purpose of this study is to re-analyze the research on the level of comparison of studies learning outcomes to problem-solving skills with mathematics modules, based on the level of education, namely elementary, junior high, high school, and higher education. The method used in this study is Meta-analysis. The search results found as much as 61 data that met the terms and conditions from the Google scholar, DOAJ, and Scopus databases fisher test value (F), student test value (t), correlation test value (r), and classical completeness taken based on keywords. And the data results next analyzed using JASP software by inputting the Effect Size (ES) and Standard Error (SE) values from each data. From the results of the data analysis, a significant increase can be seen. Improving problem-solving skills using mathematics modules is more widely used at the high school level compared to the elementary, junior high, and tertiary levels. The results of the data analysis showed that there was a comparative level of problem-solving results using mathematics modules at all levels, at the SD level of 96% (high category), SMP level of 99% high (category), SMA level of 101% (high category), and PT level by 86% (high category). It can be concluded that the comparison level at the SMA level is the higher than at the SD, SMP, SMA and PT levels.
The Role of Mathematics in Machine Learning for Disease Prediction: An In-Depth Review in the Healthcare Domain Abdillah; Syaharuddin, Syaharuddin; Mandailina, Vera; Mehmood, Saba
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.845

Abstract

The rapid advancements in healthcare technologies and the increasing complexity of medical data have made it imperative to explore and optimize predictive models for disease management. This study aims to conduct a systematic literature review to identify advancements, challenges, and opportunities in disease prediction using machine learning (ML) within the healthcare domain. The literature sources include Scopus, DOAJ, and Google Scholar, covering the period from 2013 to 2024. The findings reveal that both machine learning (ML) and deep learning (DL) algorithms have significant potential for disease prediction and treatment outcomes in various clinical contexts. Algorithms such as Random Forest, Logistic Regression, and ensemble techniques like Boosting have demonstrated strong performance in numerous studies. However, the effectiveness of these algorithms is highly context-dependent, including the type of disease, patient characteristics, and available data. Deep learning, particularly Convolutional Neural Networks (CNNs) and hybrid Long Short-Term Memory (LSTM) models, excels in handling complex, high-dimensional data, providing higher prediction accuracy compared to traditional ML models. This research shows that deep learning models, especially CNN and hybrid LSTM, achieve higher accuracy in disease prediction compared to traditional ML models. However, challenges related to data quality, privacy, and the underlying mathematical modeling of these algorithms remain to be overcome for wider applications.
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.
Co-Authors Abdillah Abdillah Abdillah Adzikya, Dieky Aftab Ahmad, Aftab Asmaul Husnah Balasubramanian, Deepa Chidambaram, Natarajan Dieky Adzkiya Hameed, Shahzaib Hussain, Muhammad Jafar Intan Dwi Hastuti Iqbal, Zainab Kamran, Muhammad Khan, Aamir Hussain Mahsup Meyundasari, Maulidia Dwi Muslimin, Ikhwanul Nadila, Desi Raza, Sajid Raza, Wasim Subchan, Subchan Syaharuddin Syaharuddin Syaharuddin Syaharudin, Syaharudin Usman, Muhammad Talha Vera Mandailina Vera Mandailina, Vera Wasti, Syeda Rabbia