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Alfian Ma'arif
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Control Systems and Optimization Letters
Published by Peneliti Teknologi Teknik Indonesia
ISSN : -     EISSN : 29856116     DOI : 10.59247/csol
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Control Systems and Optimization Letters is an open-access journal offering authors the opportunity to publish in all fundamental and interdisciplinary areas of control and optimization, rapidly enabling a safe and sustainable interconnected human society. Control Systems and Optimization Letters accept scientifically sound and technically correct papers and provide valuable new knowledge to the mathematics and engineering communities. Theoretical work, experimental work, or case studies are all welcome. The journal also publishes survey papers. However, survey papers will be considered only with prior approval from the editor-in-chief and should provide additional insights into the topic surveyed rather than a mere compilation of known results. Topics on well-studied modern control and optimization methods, such as linear quadratic regulators, are within the scope of the journal. The Control Systems and Optimization Letters focus on control system development and solving problems using optimization algorithms to reach 17 Sustainable Development Goals (SDGs). The scope is linear control, nonlinear control, optimal control, adaptive control, robust control, geometry control, and intelligent control.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 15 Documents
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Search results for , issue "vol 4, no 2 (2026)" : 15 Documents clear
Understanding Large Language Models: A Review Annastasya Nabila Elsa Wulandari; Purwono Purwono; Alfian Ma’arif; Noorulden Basil; Hamzah M. Marhoon
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.292

Abstract

Large Language Models (LLMs) have experienced rapid development and have been established as the dominant paradigm in modern Natural Language Processing (NLP), with high performance demonstrated across various language understanding and generation tasks. Increasing architectural complexity has led to the need for a structured conceptual framework to explain how architectural design, training paradigms, and inference mechanisms are collectively associated with model behavior. A conceptual and analytical review of LLMs is presented in this article through an examination of the relationship between Transformer-based architectures, multi-stage training processes, and the resulting capabilities and limitations. Encoder-only, decoder-only, and encoder–decoder architectural variants are examined in relation to structural characteristics and functional implications. The roles of pretraining, supervised fine-tuning, and instruction tuning are analyzed to clarify how output characteristics are shaped during model development. This study emphasizes how architectural and training strategies causally influence generative capabilities and inherent limitations. Fundamental issues, including hallucination, bias, data dependency, computational cost, and evaluation challenges, are critically examined as consequences of the probabilistic modeling paradigm adopted in LLMs. This review contributes a structured analytical perspective for evaluating LLMs design choices and their operational consequences, supporting more informed development and deployment practices.
A Review Multiphysics Modeling Techniques for PMSM-Based Electric Vehicle Drives Md. Abu Saleh; Md. Mehedi Hasan Mia; Md. Jasim Uddin; Md. Sumon Ali
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.188

Abstract

The objective of this paper is to review Multiphysics modeling techniques for PMSM-based electric vehicle (EV) drives. Because of its great efficiency, power density, and ability to precisely adjust power, permanent magnet synchronous motors (PMSMs) have emerged as the go-to option for electric vehicle (EV) drives. However, a Multiphysics approach that incorporates mechanical, thermal, electromagnetic, and control system dynamics is necessary to effectively describe PMSM-based EV drives. This paper examines the benefits, drawbacks, and uses of several Multiphysics modelling approaches applied to PMSM-based EV drives. Analytical methods and finite element analysis (FEA) are two examples of electromagnetic modelling techniques that are examined in connection with loss prediction and motor design optimization. While mechanical modelling techniques concentrate on vibration and acoustic noise difficulties, thermal modelling procedures are examined to address heat dissipation and performance reliability. One of the main issues lies in the accurate representation of coupled losses electromagnetic, thermal, and mechanical especially under dynamic operating conditions typical of EVs. To improve the dynamic performance and fault tolerance of PMSM drives, control-oriented modelling techniques are also examined. Co-simulation frameworks that combine these several physical domains are also presented in the review, offering a thorough understanding of practical EV applications. The paper concludes by discussing future research possibilities in Multiphysics modelling for PMSM-based EV drives, with a focus on real-time simulation capabilities, computational efficiency, and artificial intelligence integration.
Trends and Evolution Research on Digital-based Assessment: A Bibliometric Analysis using Scopus Database Zafrullah Zafrullah; Alfian Ma'arif; Astri Wahyuni; Aditya Gusti Mandala Putra
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.317

Abstract

This study aims to map global research trends and the evolution of digital based assessment systems through a bibliometric analysis. Using the PRISMA protocol on the Scopus database, publications from 1977 to 2026 were identified, resulting in 3,725 initial documents, which were filtered based on document type (articles), subject area (computer science), and relevance criteria to obtain a final dataset of 312 studies. Data were analyzed using Bibliometrix (R) and VOSviewer to examine publication trends, collaboration patterns, and keyword evolution. The findings indicate that digital based assessment research has evolved from basic computer based testing toward more adaptive and intelligent systems, with a steady annual growth rate of 2.87%. The results also reveal increasing cross regional collaboration, particularly between European and Asian institutions, along with the dominance of high impact journals such as Computers and Education and Computers in Human Behavior. Furthermore, five main research dimensions were identified: system infrastructure, digital pedagogy, advanced psychometric analytics, virtual learning governance, and user psychological behavior. Recent trends highlight the integration of adaptive testing, machine learning, and deep learning to enhance assessment accuracy and academic integrity; however, challenges related to inclusivity, ethical considerations, and student well being remain significant. Therefore, future research should focus on developing intelligent, ethical, and inclusive assessment systems that balance technological advancement with human centered considerations.
Optimized Hybrid Machine Learning Model for Real-Time Financial Fraud Detection Md Zahidul Islam Sany; Zhang Wubo; Shahin Alam
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.332

Abstract

The rapid growth of digital financial transactions has increased the demand for intelligent, scalable, and real-time fraud detection systems capable of identifying fraudulent activities with high accuracy and low latency. This paper proposes an optimized hybrid machine learning framework for real-time financial fraud detection by integrating Random Forest and XGBoost within a weighted soft-voting ensemble classifier. To address the severe class imbalance commonly found in financial datasets, the transaction data were preprocessed using the SMOTE-ENN hybrid resampling technique and Min-Max normalization. Bayesian optimization was employed to tune model hyperparameters and improve generalization while reducing overfitting. The proposed framework was trained and evaluated using stratified data partitioning and 5-fold cross-validation, with performance assessed using Accuracy, Precision, Recall, F1-score, AUC-ROC, and false-negative rate. Experimental results demonstrate that the hybrid ensemble consistently outperforms the individual base classifiers, achieving improved fraud detection capability while maintaining high processing throughput suitable for real-time deployment in a Kafka-based streaming environment. These findings indicate that the proposed framework provides an effective and scalable solution for modern financial fraud detection systems.
Optimized YOLOv11 Architecture for Accurate Multi-Class Vehicle Detection under Real-World Conditions Parag Hossain; Md. Hamim Ferdous; Md. Sabbir Mahmud; Shahin Alam
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.316

Abstract

Real-time detection of vehicles is essential for modern transportation systems, traffic surveillance, and autonomous driving technologies. With recent progress in deep learning, object detection models have become more reliable across complex and dynamic environments. This study presents a YOLOv11n (nano variant, 2.59M parameters)-based system designed to accurately detect three key vehicle classes cars, buses, and trucks in real time. Unlike prior YOLO versions, YOLOv11 introduces an improved attention mechanism and anchor-free detection head specifically addressing partially occluded and multi-scale vehicles. A customized dataset (compiled from Open Images v7 and BDD100K, spanning day/night and clear/rain/snow conditions; class distribution: cars 48%, buses 30%, trucks 22%) containing 9,989 training images and 1,998 validation images was used to fine-tune the network. Data augmentation techniques, including Mosaic augmentation, HSV color transformations, and random flips, were applied to enhance model robustness. All experiments were conducted on an RTX 3060 Laptop GPU (6GB VRAM). The trained model achieved strong detection performance, with precision of 0.806, recall of 0.751, mAP50 of 0.830, and mAP50–95 of 0.674 (the 15.6% drop indicates moderate localization errors at stricter IoU thresholds). Inference speed reaches 526 FPS (inference-only) and 263 FPS end-to-end, outperforming YOLOv8n by 3.2% in mAP50. These results highlight YOLOv11n’s ability to balance accuracy and computational efficiency, making it well-suited for real-time applications on resource-limited hardware. The proposed detection framework can support future developments in intelligent traffic management, mobility analytics, and automated road monitoring systems.
PLC-Based Trajectory Control of an Omnidirectional Mecanum-Wheel AGV Using Visual–Inertial Tracking and Grid-Based A* Path Planning Binh-Hau Nguyen; Hang-Ri Nguyen; Phuoc-Duy Nguyen; Trung-Kien Pham; Dang-Khoa Tran; Quoc-Trung Nguyen; Tu-Duc Nguyen; Thi-Ngoc-Thao Nguyen; Thi-Ngoc-Hieu Phu; Hoang-Lam Le; Hoang-Phuc Le; Cong-Tan Bien
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.318

Abstract

This paper presents a PLC-based omnidirectional Automated Guided Vehicle (AGV) using four Mecanum wheels for indoor material transportation. The system integrates a Mitsubishi Q-series PLC for motion execution, an Intel RealSense T265 visual–inertial camera for relative pose tracking, and a Python-based supervisory interface running on a laptop. The AGV operates in a known static workspace represented by a predefined grid map, where each cell corresponds to 0.1 m in the real environment. Static obstacles, pickup points, and drop-off points are manually defined before operation. The A* algorithm generates collision-free waypoint paths, which are converted into wheel velocity commands through the inverse kinematic model and transmitted to the PLC via Ethernet-based MC-Protocol. Experimental tests at a commanded speed of 0.2 m/s show that the prototype can perform eight-directional motion and complete predefined pickup–delivery tasks. The main limitations include tracking drift of the T265 camera, lighting sensitivity, wheel slippage on tiled floors, and communication latency. Future work will focus on sensor fusion, real-time obstacle detection, and fail-safe mechanisms.
Comparative Evaluation of Transfer Function and Detailed Switching Models for a PID-Controlled Single-Phase DC–AC Conversion System Fifin Nugroho; Hari Maghfiroh; Warindi Warindi; Fahmizal Fahmizal
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.324

Abstract

Transfer-function (TF) models are widely used in power electronics because of their simplicity and suitability for controller design. However, their ability to represent practical converter behavior is limited since switching actions and nonlinear effects are neglected. This paper presents a comparative evaluation of transfer-function and detailed switching models for a PID-controlled single-phase inverter system consisting of a DC–DC boost converter and a full-bridge inverter employing unipolar sinusoidal pulse width modulation (SPWM). The system is designed to convert a 46 VDC input into a regulated 220 VAC (RMS), 50 Hz output. Both models were developed in MATLAB/Simulink and evaluated under identical operating conditions. The results show that the TF model provides a computationally efficient representation suitable for preliminary controller design, while the detailed switching model captures switching-induced ripple, transient dynamics, and harmonic distortion. The detailed model achieved stable 220 VAC operation with a total harmonic distortion (THD) of 1.91%, satisfying IEEE Standard 519 requirements. The comparative analysis indicates that controller parameters obtained from the TF model can provide a useful initial tuning reference before refinement in the detailed switching model. Since the study is based exclusively on simulation, experimental validation remains necessary. The results suggest that TF models are suitable for early-stage controller development, whereas detailed switching models are required for realistic performance assessment and validation.
Pattern Analysis on Multi-Sensor Networks: Short-Term Forecasting at Parangtritis Coastal, Yogyakarta, Indonesia Haris Imam Karim Fathurrahman; Choirul Fajri; Chin Li-Yi; Khoirudin Wisnu Mahendra
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.333

Abstract

Reliable microclimatic records are a prerequisite for evidence-based agricultural planning, yet high-resolution ground-truth datasets for tropical coastal environments remain scarce in the Indonesian literature. This study analyzes six months (December 2025 – May 2026) of 10-minute interval observations across 18 meteorological variables from the Parangtritis Automated Weather Station (AWS), Bantul Regency, Yogyakarta, Indonesia. Rainfall was reconstructed via a differential method from the cumulative station counter, yielding a period total of 1748.4 mm over 180 days. Of these, 112 days (66.1%) recorded measurable precipitation, punctuated by seven dry-spell episodes; the longest extended 12 consecutive days (5–16 May 2026). Schmidt-Ferguson classification returned Q = 20%, placing the site in Climate Type B (Wet/Basah). Reference evapotranspiration (ET₀, Hargreaves–Samani) averaged 10.20 mm/day (total: 1796.0 mm), and a PDSI proxy indicated extreme drought conditions by the close of the observation period (PDSI = −7.45), a deficit attributable primarily to persistently high ET₀ demand rather than rainfall deficiency per se. Cross-correlation analysis identified relative humidity as the dominant concurrent temperature predictor (lag 0; r = −0.676). An XGBoost model achieved short-term temperature forecasting accuracy of MAE = 0.56°C and RMSE = 0.71°C (R² = −1.419, reflecting the site's narrow diurnal thermal variance rather than model failure). Prophet projected sharply reduced rainfall for June–August 2026 (0.25–4.77 mm), which the generic staple-crop suitability framework scores as Not Recommended, reflecting unsuitability for rainfed rice, yet these drier conditions are agronomically favourable for shallot in coastal Yogyakarta.
Robust Parameter Identification and Control Modeling of Low-Cost Brushed DC Motors Using the Nelder-Mead Algorithm Channareth Srun; Mengseu Pheng; Sovathana Um; Chivon Choeung; Seven Siren; Sros Nhek
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.321

Abstract

Brushed DC motors are widely used in next-generation automation systems due to their low complexity and ease of control. However, more affordable models often lack sufficient information about their detailed parameters, which makes accurate control and modeling difficult. This paper presents an estimation method for the main parameters of a low-cost brushed DC motor using the Nelder-Mead algorithm. Real-time measurements of speed were obtained through Arduino-based testing, followed by parameter estimation using MATLAB and Simulink. The estimated parameters include armature resistance, inductance, moment of inertia, viscous damping coefficient, back electromotive force constant, and torque constant. The estimated results, validated strictly against a high-specification reference motor datasheet, demonstrate strong accuracy in critical mechanical parameters. Specifically, the algorithm estimated the torque constant with a minimal error of 0.17% and the viscous damping coefficient with an error of 4.8%. However, due to the inherent structural unidentifiability when relying solely on macroscopic speed measurements, electrical parameters such as armature resistance, inductance, and moment of inertia exhibited severe deviations ranging from 39.6% to 52.7%. While the objective function's inability to fully decouple these intertwined variables restricts isolated physical parameter extraction, the method effectively captures the equivalent macroscopic dynamic behavior. The predictive validity of the proposed method was further confirmed by implementing a PI controller based on the estimated transfer function. The experimental results confirm that despite internal physical parameter discrepancies, the algorithm provides an equivalent and robust dynamic model that significantly improves motor performance in control systems. This work proposes an inexpensive and efficient system identification solution for low-cost motor control characterization.
Comparing LQR, SMC, and Backstepping for Active Suspension: Robustness, Energy Efficiency, and Frequency Response Ali Aniss Ebrahim
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.319

Abstract

While active suspension systems have advanced significantly, the literature still lacks a systematic compara tive framework that integrates time-domain robustness analysis, frequency-domain vibration isolation, and operational constraints such as actuator saturation. This study presents a comparative framework to bridge this gap through a quantitative evaluation of three advanced control strategies: Linear Quadratic Regulator (LQR), Sliding Mode Control (SMC), and Backstepping. The strategies were evaluated under multiple test scenarios, including: a step signal (0.05 m for 0.2 s), a 0.02 m amplitude sine wave with varying frequencies between 0.5 and 10 Hz, a random wave, ±20% variations in system parameters, and simulated actuator saturation constraints at ±1500 N. The SMC controller demonstrated exceptional robustness under uncertainty, achieving a 34.2% improvement in suspension deflection, while the LQR controller demonstrated superior energy efficiency, outperforming SMC by 28.5%. Frequency response analysis revealed that LQR is optimal in the low frequency band (0–2 Hz), while SMC excels in the mid band (2–8 Hz). Analysis of variance (ANOVA) confirmed statistically significant differences between the strategies (F(2,87) = 24.36, p 0.001). This framework provides a quantitative trade-off model that guides designers to: use SMC for applications requiring high robustness under uncertain conditions (such as vehicles operating on varying terrain), use LQR when energy efficiency is a top priority (such as electric vehicles), and use Backstepping as a compromise that ensures guaranteed mathematical stability with balanced performance.

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