Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Polimesin
Nur Mutiara Syahrian
Politeknik Negeri Sriwijaya
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Vision-Based Pipe Monitoring Robot for Crack Detection Using Canny Edge Detection Method as an Image Processing Technique Nur Mutiara Syahrian; Pola Risma; Tresna Dewi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 2, No 4, November-2017
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (547.521 KB) | DOI: 10.22219/kinetik.v2i4.243

Abstract

Piping setup is very important to ensure the safety and eligibility of the piping system before applied in industry. One of the techniques to facilitate perfect piping setup is by employing pipe monitoring robot. Pipe monitoring robot is designed in this research to monitor cracks or any other defects occur inside a pipe. This automatic monitoring is conducted by the application of image processing with canny edge detection. Canny edge detection method detects the edges or lines of any cracks inside the pipe and processes them to create differences in image, therefore only the cracks can be shown and finally, those cracks can be well analyzed. Canny edge detection has 5 processing techniques that are smoothing, finding gradients, non-maximum suppression, double thresholding, and edge tracking by hysteresis. In this research, the experiment was conducted by letting a robot monitoring new pipe and detecting cracks. Two cracks samples were taken and analyzed. The results show that the best value for smoothing is 10 and 5 for thresholding in getting not too blurred or to sharp result.
Sequential integration of optimized LSTM-RNN base-learners into a stacking ensemble for industrial PV output prediction Nur Mutiara Syahrian; Tresna Dewi; Rusdianasari Rusdianasari
Jurnal Polimesin Vol 24, No 4 (2026): August
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v24i4.9478

Abstract

Accurate photovoltaic (PV) power forecasting is critical for managing solar intermittency, especially within industrial power systems. The objective of this study is to develop a high-precision forecasting framework specifically tuned for the stochastic weather patterns at the Pertamina RU III Sungai Gerong facility. This study proposes a two-stage deep learning framework utilizing a full year of historical operational data (June 2024-June 2025), totaling 2,795 samples, to ensure it captures a wide range of seasonal variations. The system optimized RNN and LSTM models as base learners which were then integrated by an MLP meta-learner through a Stacking Ensemble. The results demonstrate that a Tapered MLP (64-32) configuration performs best, minimizing the Root Mean Squared Error (RMSE) to 130.26 kWh and maximizing the R² index to 0.816. The Storm Simulation scenario was constructed using an interactive environment to replicate stochastic solar radiation drops exceeding 50% of peak capacity caused by sudden cloud movement. The results reveal that the ensemble model maintains a low error rate of 5.3%, outperforming standalone models which failed with errors up to 19.7%. These findings has a potential to support proactive load-shifting and synchronization in industrial-scale PV systems.
Co-Authors Pola Risma Rusdianasari Rusdianasari Tresna Dewi