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All Journal IJIIS: International Journal of Informatics and Information Systems Jurnal Teknik Informatika (JUTIF) Electronic Journal of Education, Social Economics and Technology
Yulianto, Koko Edy
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Comparative Analysis of ArUco Marker Detection Techniques Using Adaptive Thresholding, CLAHE, and Kalman Filter for Smart Cane Applications Yulianto, Koko Edy; Saputro, Rujianto Eko; Utomo, Fandy Setyo
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4883

Abstract

This study aims to analyze and compare the effectiveness of three image processing techniques  Adaptive Thresholding, CLAHE, and Kalman Filter in enhancing the performance of ArUco marker detection for a smart cane system designed for visually impaired individuals at SLB Kuncup Mas Banyumas. The evaluation method includes detection accuracy, marker position precision, and computational time required by each technique under two different lighting conditions: daytime and nighttime. The results show that all three image processing techniques successfully achieved a 100% detection accuracy for ArUco markers. However, significant differences were observed in computational time, with Kalman Filter demonstrating the fastest processing speed, making it the most efficient option for real-time applications requiring quick response. CLAHE and Adaptive Thresholding performed better in uneven lighting conditions, although they required longer computational times. Kalman Filter is therefore recommended for marker-based navigation systems in environments demanding fast response times, while CLAHE and Adaptive Thresholding are better suited for settings with variable lighting intensities. The implications of these findings open opportunities for developing adaptive navigation systems capable of dynamically adjusting image preprocessing methods based on real-time environmental conditions. This study contributes practically to the advancement of assistive navigation technologies for visually impaired individuals, particularly in the development of visual marker-based detection systems. The results also provide a useful guideline for selecting appropriate image processing techniques according to environmental characteristics, thereby improving the accuracy and adaptability of navigation systems across diverse lighting conditions and operational environments.
Lightweight Visual Detection System for Object Identification with ArUco Markers in Resource-Constrained Environments Yulianto, Koko Edy; Saputro, Rujianto Eko; Utomo, Fandy Setyo
Electronic Journal of Education, Social Economics and Technology Vol 6, No 2 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i2.757

Abstract

Object detection is a fundamental task in computer vision systems used in robotics, automation, and real-time tracking applications. However, implementing accurate and responsive detection on low-cost embedded hardware presents significant challenges due to limited processing power and environmental variability. This study aims to evaluate the performance of an object detection system utilizing ArUco markers on a Raspberry Pi-based platform. The research investigates the system’s ability to detect and identify three types of physical objects a plastic bottle, a flower pot, and a glass cup as well as the performance when all three objects are present simultaneously. The system was tested under controlled static conditions using a camera to capture real-time video streams. Detection time, computation time, and accuracy were measured across five consecutive frames for each scenario. Results show that the system achieved consistent detection and processing times below 0.14 seconds per frame, meeting real-time performance criteria. Detection accuracy across all individual object scenarios exceeded 91%, with the highest accuracy recorded in the multi-object scenario at 93.44%. No detection failures occurred during the experiments, and frame-by-frame analysis confirmed temporal stability. These findings indicate that marker-based detection is a reliable and efficient approach for real-time applications in structured environments. The study provides a foundation for extending the system to more dynamic conditions in future research.
Enhancing Household Energy Consumption Forecasting Using the XGBoost Algorithm with Cross-Validation and Residual-Based Evaluation Sugianto, Dwi; Yulianto, Koko Edy
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.253

Abstract

Accurate forecasting of household energy consumption plays a crucial role in optimizing energy efficiency, supporting sustainable policy decisions, and improving operational management in smart grid systems. This study enhances conventional XGBoost-based forecasting by integrating cross-validation and residual-based evaluation to ensure model robustness and interpretability. Using a dataset of over 90,000 daily household energy records that include temperature, humidity, and appliance-level usage, a systematic preprocessing pipeline was applied—comprising data cleaning, normalization, temporal feature transformation, and partitioning into training and testing subsets. The proposed model was trained using 10-fold cross-validation to minimize overfitting and validated through residual error analysis to assess stability and bias. Evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), demonstrate superior predictive accuracy, achieving MAE = 0.48, RMSE = 0.64, and R² = 0.9864. Visualization of actual versus predicted consumption and symmetric residual distribution further confirm the model’s reliability. The findings highlight that the enhanced XGBoost model not only achieves high precision but also provides a robust foundation for real-time energy monitoring, anomaly detection, and sustainable household energy management. Future work will integrate SHAP-based interpretability and comparative benchmarking with deep learning approaches.
Co-Authors Fandy Setyo Utomo Rujianto Eko Saputro Sugianto, Dwi