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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Ulul Albab JTAM (Jurnal Teori dan Aplikasi Matematika)
Raza, Wasim
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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.
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 Adzikya, Dieky Aftab Ahmad, Aftab 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 Usman, Muhammad Talha Vera Mandailina, Vera Wasti, Syeda Rabbia