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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Computer Science
Padma Nyoman Crisnapati
Rajamangala University of Technology Thanyaburi
Computer Science & IT Electrical & Electronics Engineering Engineering

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Trolls: a novel low-cost controlling system platform for walk-behind tractor Padma Nyoman Crisnapati; Dechrit Maneetham; Evi Triandini
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp842-858

Abstract

A novel low-cost controlling system platform for walk-behind hand tractors (Quick G3000 and G1000) was designed and developed to solve the fatigue problem faced by farmers when ploughing the rice field. This platform is dedicated to designing and manufacturing mechanical, electrical, and software components. The tractor was modified and added with an embedded control system that functioned as the slave, while the direction of the tractor movement was controlled remotely by humans through Bluetooth communication with the smartphone application as the master. Several servos and direct currents (DCs) were used as the actuator to move some levers and clutches instead of the tractor to make it remotely controllable. This system has been directly tested in the paddy farming land through two tractors: Quick G3000 and G1000. The testing results showed that this system could be used within more or less six hours; there is a cost-efficiency of 21.74% and 84.62% battery usage efficiency. More efficient mechanics caused this cost efficiency, and the reduction in electronic devices affects battery efficiency. A low-cost platform for controlling walk-behind tractors has been successfully developed; this platform assists farmers in ploughing their fields.
IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model I Komang Agus Ady Aryanto; Dechrit Maneetham; Padma Nyoman Crisnapati
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp900-912

Abstract

This research aims to develop a monitoring system and temperature prediction model in neonatal premature infant incubators by applying the internet of things (IoT) concept and the 1-dimensional convolutional neural network (1D-CNN) method. The system is designed by integrating sensors, actuators, and microcontrollers connected through Wi-Fi network with message queue telemetry transport (MQTT) protocol. Sensor data in the incubator is stored in a database and displayed in real-time on a web application. The data in the database is also used for creating a temperature prediction model in the incubator. Test results indicate that the best model configuration consists of 5 neurons in the first layer, 20 neurons in the second layer, and a dense layer with 100. The evaluation of this model yields a high level of accuracy with an root mean square error (RMSE) of 0.200 °C, MSE of 0.004 °C, mean absolute error (MAE) of 0.152 °C, and mean absolute percentage error (MAPE) of 0.4%. Based on the error values obtained between the predicted and actual values from each evaluation technique in the model, it can be concluded that the range between the real and predicted values is approximately 0.2 °C. Overall, this research contributes to improving the quality of care for premature infants.
AGV maneuverability simulation and design based on pure pursuit algorithm with obstacle avoidance Singhanart Ketsayom; Dechrit Maneetham; Padma Nyoman Crisnapati
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp835-847

Abstract

This paper discusses the simulation of an automated guided vehicle (AGV) with the differential-drive mobile robot (DDMR) concept. Using this wheel configuration, the AGV can maneuver in tight workspaces. However, controlling a self-driving AGV with obstacle avoidance is not easy. Therefore, this paper proposes a control system to drive an AGV with several process stages. First, a kinematic model is formulated to represent the AGV with the concept of two wheels that can be controlled differentially. In the second stage, the pure pursuit control method is applied to the model so that the AGV can follow the waypoint coordinates determined and combine them with obstacle avoidance. Finally, the effectiveness of the control system was verified using simulation. The look-ahead parameter with a value of 0.2 meters shows optimal results so that pure pursuit control can reach all waypoint coordinates. Based on this simulation, the AGV prototype was then designed, assembled, and equipped with an internet of things-based obstacle avoidance system. While the simulation proves promising, the anticipated challenges identified in the AGV field test, such as GPS inaccuracies and signal obstructions, underscore the need for ongoing improvements in real-world applications.
Neuromarketing case study: recognition of sweet and sour taste in beverage products based on EEG signal features Yuri Pamungkas; Riva Satya Radiansyah; Padma Nyoman Crisnapati; Yamin Thwe
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26626

Abstract

Consumers’ acceptance of a food product hinges on its taste. Culinary practitioners typically conduct organoleptic tests to evaluate a food/beverage’s taste. Organoleptic tests have a subjective nature, making a clear description difficult. In this study, we suggest implementing a brain signal-based electroencephalogram (EEG) taste assessment system to evaluate consumer responses to the tastes of a drink, specifically sour and sweet. The system distinguishes flavors based on EEG data. These classifiers, including recurrent neural network (RNN), long-short term memory (LSTM), and gated recurrent unit (GRU), are utilized for the classification process. Total 35 participants’ EEG data were recorded for this study. Temporal (T3 and T4) and centro parietal (CP1 and CP2) channels are used for recording. EEG signal processing involves filtering, artefact elimination, and band decomposition into delta, theta, alpha, beta, and gamma frequencies. In the time domain of clean EEG data, mean absolute value, standard deviation, and variance are used for signal feature extraction. Several classifiers (RNN, LSTM, and GRU) will be fed with the signal feature values as input. An accuracy of 88.62% was achieved using LSTM in the classification. The RNN and GRU models achieved classification accuracies of 88.56% and 87.15% respectively.
Efficient YOLO-based models for real-time ceramic crack detection Benchalak Maungmeesri; Sasithorn Khonthon; Dechrit Maneetham; Padma Nyoman Crisnapati
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp852-860

Abstract

The following research work systematically compares four variants of you only look once (YOLO), namely, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 proposed recently, considering the key properties required to perform ceramic surface crack detection tasks with high computational efficiency, real-time inference speed, and low memory usage. A total of 300 images of ceramic surfaces were collected with manually labeled cracks and divided into training, validation, and testing sets in portions of 263, 22, and 15 images, respectively. Each of the four YOLO variants was trained for 50 and 100 epochs, and each was evaluated regarding mean average precision (mAP), inference time, model size, and computational complexity in giga floating point operations per second (GFLOPs). YOLOv9 produced the highest accuracy with mAP values as high as 0.752-0.79 but the highest cost in terms of increased computational complexity. However, among these methods, YOLOv8 can produce the fastest inference (~2-2.3 ms) with a small memory footprint (~6 MB) with an acceptable accuracy of ~0.65-0.67. The results showed that YOLOv8 is the most feasible to deploy in resource constrained industrial automation environments. By offering this comparative study, the research attempts to provide hints for the selection of appropriate YOLO-based models by practitioners in quality control applications related to ceramic manufacturing.
Co-Authors Benchalak Maungmeesri Dechrit Maneetham Evi Triandini I Komang Agus Ady Aryanto Riva Satya Radiansyah Sasithorn Khonthon Singhanart Ketsayom Yamin Thwe Yuri Pamungkas