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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal Teknologi Prosiding Semnastek International Journal of Advances in Intelligent Informatics Annual Research Seminar Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Martabe : Jurnal Pengabdian Kepada Masyarakat JIKA (Jurnal Informatika) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Computing Theories and Applications
Ardi Pujiyanta
Program Studi Teknik Informatika Fakultas Teknologi Industri Universitas Ahmad Dahlan Yogyakarta Jl. Prof. Dr. Soepomo, S.H., Warungboto, Janturan, Yogyakarta 55164 Telp : (0274) 563515 Ext. 3208
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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Journal : journal of applied informatics and computing

 1 
Fuzzy Logic and Neural Network-Based Self-Tuning PID for Vacuum Pressure Stabilization Sanjaya, Berza H.; Pujiyanta, Ardi; Puriyanto, Riky Dwi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10945

Abstract

The conventional PID controller is widely used for vacuum pressure control; however, it has limitations when faced with nonlinear system characteristics and external disturbances, leading to a decline in performance. Several previous studies have proposed the integration of PID with intelligent methods, such as neural networks or fuzzy logic separately. Nevertheless, these singular approaches still encounter limitations in terms of adaptability and robustness. This study aims to develop a self-tuning PID method based on the combination of Neural Networks (NN) and Fuzzy Inference Systems (FIS) to enhance the stability and accuracy of vacuum pressure control. A nonlinear vacuum system plant model is constructed within the Simulink environment to generate a dataset used for training the NN with the Levenberg-Marquardt algorithm. The NN is employed to predict changes in PID parameters adaptively, while the FIS provides fine corrections to strengthen system stability. Simulation results demonstrate that the proposed approach effectively reduces overshoot from 36.47% to 31.51%, decreases steady-state error from 0.069 to 0.052, and lowers the RMSE value from 0.125 to 0.108 compared to conventional PID. Thus, the integration of NN and FIS within the self-tuning mechanism proves to be more effective in addressing nonlinear dynamics and external disturbances, resulting in a more stable and accurate system response.
Comparison of LSTM and Random Forest for Hydrogen Production Prediction in Alkaline Water Electrolysis Ulil Amri Ulil; Ardi Pujiyanta; Sunardi Sunardi
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13254

Abstract

Hydrogen has emerged as a promising clean energy carrier capable of supporting the transition toward sustainable energy systems. In alkaline water electrolysis systems, accurate prediction of hydrogen production is essential for improving system monitoring, operational efficiency, and future control strategies. This study aims to compare the performance of Random Forest (RF) and Long Short-Term Memory (LSTM) algorithms in predicting hydrogen production based on operational parameters of an alkaline water electrolysis system. The dataset used in this study consists of more than 100,000 operational data samples collected from laboratory-scale electrolysis experiments, including voltage, current, temperature, and hydrogen gas pressure measurements. Hydrogen production was calculated using the ideal gas law and used as the target variable for model development. Prior to model training, the dataset underwent preprocessing, including data cleaning, normalization, and train-test splitting. The predictive performance of both models was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Experimental results show that the Random Forest model achieved superior performance with an MAE of 0.0085, RMSE of 0.0106, and R² of 0.9905, while the LSTM model obtained an MAE of 0.0302, RMSE of 0.0361, and R² of 0.7907. The findings indicate that Random Forest is more effective than LSTM in modeling the relationship between operational parameters and hydrogen production in the investigated alkaline water electrolysis system. This study demonstrates the potential of machine learning approaches for accurate hydrogen production prediction and provides insights into the suitability of different predictive models for electrolysis-based hydrogen generation systems.
Co-Authors Agus Arif Setiyawan Ali Akbar, Son Alvi Mufidatun Andri Pranolo Ari Pujiantoro Ari Pujiantoro Aridansyah, Aridansyah Arnusani Rohmatika Bambang Robiin Berza H. Sanjaya Cahyoko Hariya Pamilu Cendi Praseptyo Darucandra, Nawa Diana Riyanti Elfani Elfani Faisal Fajri Rahani Ferbriyanto, Suko Fernandya Riski Hartantri Fiftin Novianto Fiftin Noviyanto Handoko Dwi Cahyo Iklima, Tia Irfan Maulana, Irfan Kairudin Kairudin Khawa Sukmawati Kirana Permata Putri Kurnia Adhiguna Lukito Edi Nugroho Moh Amin Aqib Mukhlis Mukhtar Mulyadi Mulyadi Pujiantoro, Ari Puriyanto, Riky Dwi Puriyanto, Riky Dwi Redha Rizky Firdaus Reny Retnowati Romy Dwiputra Roy Widiono Rusydi Umar Sanjaya, Berza H. Sany Yuliani Sigit Buddy Prakoso Siti Nurul Khofiyah Sunardi, Sunardi Supriyanto Taufik Ismail Taufiq Ismail Ulil Amri Ulil Widyawan