Article Per Year (5 Year)
p-Index From 2021 - 2026
0.702
P-Index
This Author published in this journals
All Journal Jurnal Tekno Insentif IJIIS: International Journal of Informatics and Information Systems Jurnal Teknik Informatika (JUTIF) Electronic Journal of Education, Social Economics and Technology
Yulianto, Koko Edy
Unknown Affiliation
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Veterinary Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
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.
Penerapan Algoritma Kalman Filter Dan Yolo Mengukur Efektifitas Aruco Marker Bagi Tunanetra YULIANTO, KOKO EDY; SAPUTRO, RUJIANTO EKO; UTOMO, FANDY SETYO
Jurnal Tekno Insentif Vol 19 No 2 (2025): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v19i2.1948

Abstract

Abstrak Mobilitas dan aksesibilitas merupakan tantangan utama bagi individu dengan disabilitas visual. Penelitian ini mengevaluasi efektivitas sistem navigasi berbasis multi-sensor untuk tunanetra dengan menggabungkan Algoritma Kalman Filter dan YOLO dalam mendeteksi Aruco Marker. Fokus penelitian adalah meningkatkan akurasi dan efisiensi navigasi melalui integrasi teknik deteksi deep learning dan prediksi matematis secara real-time. Hasil eksperimen menunjukkan bahwa Kalman Filter memiliki waktu deteksi lebih cepat, dengan rata-rata 0,1090 detik untuk objek Botol Plastik dan 0,1069 detik untuk objek Kombinasi. YOLO mencatat waktu sedikit lebih cepat namun dengan komputasi lebih berat. Kalman Filter mencatat efisiensi waktu 12,5%–13,3% lebih baik pada objek tertentu dan akurasi sebesar 94,50% (Botol Plastik) serta 96,00% (objek Kombinasi), lebih tinggi dibandingkan YOLO. Kombinasi kedua algoritma ini memberikan solusi navigasi yang akurat dan efisien untuk tunanetra, serta berpotensi dikembangkan lebih lanjut sebagai sistem navigasi real-time yang andal. Kata kunci: Kalman Filter, YOLO, Aruco Marker, Navigasi Tunanetra, Efektivitas Deteksi Abstract Mobility and accessibility remain major challenges for individuals with visual impairments. This study evaluates the effectiveness of a multi-sensor navigation system for the visually impaired by integrating the Kalman Filter algorithm and YOLO for Aruco Marker detection. The research focuses on improving the accuracy and efficiency of navigation by combining deep learning-based detection with real-time mathematical prediction. Experimental results show that the Kalman Filter achieves faster detection times, averaging 0.1090 seconds for Plastic Bottle objects and 0.1069 seconds for Combination objects. While YOLO recorded slightly faster raw detection times, Kalman Filter demonstrated 12.5%–13.3% better computational efficiency for certain objects. In terms of accuracy, Kalman Filter achieved 94.50% for Plastic Bottle objects and 96.00% for Combination objects, outperforming YOLO’s 92.00% and 93.50%, respectively. The integration of both algorithms offers a promising and optimal solution for the development of reliable real-time navigation systems for the visually impaired. Keywords: Kalman Filter, YOLO, Aruco Marker, Blind Navigation, Detection Effectiveness
Co-Authors Fandy Setyo Utomo Rujianto Eko Saputro Sugianto, Dwi