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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Rekayasa elektrika
Alfatirta Mufti
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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The Effects of P, I and D Parameters in Automatic Liquid Level Control Using UniTrain Module Alfatirta Mufti
Jurnal Rekayasa Elektrika Vol 10, No 3 (2013)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v10i3.1013

Abstract

The research discusses some experiments to control the level of liquid inside a tank by using PID controllers which can be divided into four categories. The experiments describe the effect of P, I, and D element. It also discusses the best possible controller, which is a PI controller, for the liquid level tank system. The liquid level controlling is done by adjusting the voltage pump which will further regulate the flow rate of the fluid entering the inlet valve. The liquid that flows through the outlet valve is considered as the disturbance variable to the system. The liquid tank sensor needs to be calibrated prior to the experiments. Calibration can be done manually by using a digital multimeter or by using the computer sofware that is connected directly to the plant system. Set point and PID parameters are determined by the UniTrain and the computer interface. In these experiments, PI controller has the best result with a medium proportional gain (KP = 5) and a small integral gain (TN = 0.2).
Comparative Analysis of Multispectral Image Classification Based on EfficientNetB0, ResNet152, DenseNet161, DenseNet121, and HSV Segmentation Melinda; Nurdin, Yudha; Mufti, Alfatirta; Anzella, Syifa; Rusdiana, Siti; D Acula, Donata
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6873

Abstract

This study established a classification system based on Convolutional Neural Networks (CNNs) to detect High-High Fluctuation (HHF) patterns in multispectral data derived from pure water (H2O) and a water-sodium hydroxide (NaOH) solution. This study combines HSV color-space-based segmentation to identify areas with the highest signal amplitude, thereby enhancing the feature extraction of the CNN model. Data augmentation techniques, including random flipping, rotation, and color jitter, along with training parameters such as a learning rate of 0.0001 and a batch size of 32, have been shown to effectively improve model generalization and reduce overfitting. Four different CNN architectures were evaluated: ResNet-152, DenseNet-161, DenseNet-121, and EfficientNet-B0. As a result, ResNet152 achieved the highest accuracy of 97.6%, attributed to its network depth and residual connections that effectively address the vanishing gradient problem. DenseNet161 and DenseNet121 also demonstrated competitive performance, achieving accuracies of 96.7% and 96.2%, respectively, which is supported by their dense connectivity that optimizes feature reuse. Conversely, EfficientNetB0, despite showing lower accuracy (90%), provides significant computational efficiency, making it suitable for real-time applications. These results underscore the importance of selecting a CNN architecture that balances accuracy and efficiency for multispectral data classification.
Co-Authors Anzella, Syifa D Acula, Donata Melinda Siti Rusdiana Yudha Nurdin