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Analyzed New Design Data Driven Modelling of Piezoelectric Power Generating System Solly Aryza; Zulkarnain Lubis; M. Isa Indrawan; Syahril Efendi; Poltak Sihombing
Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences Vol 4, No 3 (2021): Budapest International Research and Critics Institute August
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v4i3.2350

The piezoelectric speed bumps designed in this study consist of a speed bump mechanical system that functions to receive input from motorized vehicle pressure, piezoelectric cantilever system as a component producing electrical energy and energy harvesting system as energy harvester from piezoelectric material. One system module consists of a piezoelectric parallel circuit connected to the MB39C811 buck converter. In this paper, we will discuss the mechanism for analyzing the design and modeling of a piezoelectric power plant system on a bluff body-based speed bump with a cantilever system for variations in motor vehicle speed by means of physical modeling. In the piezoelectric speed bump in the form of a bluff body, it can be seen that the air flow when passing through the triangular cross section has an increase in speed so that the resulting vortex has a high speed. This is what causes high vibrations in the piezoelectric so that the maximum voltage and the average voltage are the highest. Piezoelectric speed bumps are capable of generating electrical power with an input of 60 times the motorized vehicle track of 2.166mWh with an efficiency of 2.87% compared to manual input.

Diffusion2D and Anchored Inference for Asymptotic Stabilization of Diffusion-Convolutional Neural Networks in Multidomain Medical Image Classification Hanna Willa Dhany; Sutarman Sutarman; Poltak Sihombing; Mohammad Andri Budiman
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1215

Medical image classification across heterogeneous domains remains challenging due to domain shift, spatial variability, and unstable inference behavior. This study proposes a diffusion-stabilized Diffusion-Convolutional Neural Network (DCNN) framework that integrates Diffusion2D and post-hoc Anchored Diffusion to improve inference stability, probabilistic consistency, and robustness in multidomain medical image classification. The main contribution of this work is the introduction of a two-stage stabilization mechanism in which Diffusion2D performs controlled intra-image diffusion on feature representations before graph construction, while Anchored Diffusion refines uncertain predictions in the logit space through a k-nearest neighbors graph without retraining. The framework was evaluated on heterogeneous medical imaging datasets consisting of brain MRI, leukemia microscopy, and COVID-19 chest radiographs. Experimental results show that the proposed approach maintained baseline classification performance with an accuracy of 64.70% while improving the Macro-F1 score from 0.7045 to 0.7061. The diffusion mechanism reduced the average Laplacian value from 0.864355 to 0.187525, corresponding to a 78.23% reduction in spatial gradient variability. Internal analysis further demonstrated stable diffusion coefficients with a mean value of 0.141734 and a standard deviation of 0.003757, indicating controlled diffusion behavior. Anchored Diffusion selectively refined uncertain predictions, affecting only 0.6% of evaluated samples while preserving overall decision consistency. Repeated inference experiments across 40 iterations also revealed highly stable confidence trajectories with no observable variance after diffusion stabilization. The novelty of this research lies in combining feature-level diffusion stabilization, post-hoc anchored inference, and asymptotic regularization within a unified DCNN framework, providing a theoretically grounded and uncertainty-aware approach for robust multidomain medical image classification.

A Climate Driven Decision Support System for Rice Management Using SPI-3 Prediction and Particle Swarm Optimization Eka Putra; Syahril Efendi; Poltak Sihombing; T. Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1408

Climate variability and irregular rainfall patterns have become critical challenges affecting rice productivity, irrigation planning, and agricultural sustainability. Previous studies have primarily focused on rainfall forecasting or drought monitoring independently, with limited attention given to transforming climate predictions into actionable agricultural management strategies. This study addresses this gap by proposing an integrated climate-driven decision support framework that combines predictive drought-index modeling with optimization-based agronomic decision-making for adaptive rice field management. The proposed framework integrates satellite-based rainfall observations, seasonal climatic characteristics, and large-scale climate variability indicators to predict short-term moisture conditions represented by the three-month standardized precipitation index. The framework consists of three interconnected stages: climate prediction, optimization, and recommendation generation. In the prediction stage, a gradient boosting regression model enhanced with Bayesian hyperparameter optimization was employed to model nonlinear relationships among rainfall accumulation, lag rainfall patterns, seasonal cyclic features, and climate variability indicators. In the optimization stage, particle swarm optimization was applied to determine optimal fertilizer dosage, irrigation allocation, and harvest timing under varying climate conditions. Experimental procedures included comparative evaluations across multiple machine learning models, hyperparameter tuning strategies, and optimization iterations. The research figures and tables demonstrate the complete framework architecture, prediction performance comparisons, optimization convergence behavior, and adaptive rice management recommendations. Experimental results show that the proposed framework achieved strong predictive performance with a coefficient of determination of 0.851, a root mean square error of 0.391, and a mean absolute error of 0.322. Comparative analysis further confirmed that integrating climate variability indicators significantly improved predictive accuracy compared with baseline models using only historical rainfall information. The optimization process also demonstrated stable convergence toward climate-adaptive agronomic recommendations.

Deep Deterministic Policy Gradient for Simulation-Based Control of Water Quality in Nano Aquatic Systems Iwan Fitrianto Rahmad; Syahril Efendi; Poltak Sihombing; Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1447

This study proposes a simulation-based Deep Deterministic Policy Gradient (DDPG) framework for water-quality control in nano aquatic systems. Nano tanks are highly sensitive to small disturbances because their limited water volume reduces buffering capacity and causes rapid changes in dissolved oxygen, ammonia, pH, temperature, biological oxygen demand, and chemical oxygen demand. To represent these coupled dynamics, a nonlinear simulation model adapted from the Continuously Stirred Tank Reactor concept is developed and implemented as an OpenAI Gym-compatible reinforcement learning environment. The DDPG agent learns continuous control actions related to aeration, feeding, and filtration through repeated interaction with the simulated nano-tank environment. The proposed nonlinear CSTR-DDPG framework is evaluated against a linear-model DDPG baseline using RMSE, cumulative reward, and closed-loop control performance. Simulation results show that the nonlinear model reduced RMSE by 45.2% for dissolved oxygen, 64.0% for ammonia, and 61.3% for pH compared with the linear baseline. The DDPG agent also achieved a 41.7% higher cumulative reward under the same reward structure. These findings indicate that nonlinear simulation can provide a more informative training environment for DDPG-based water-quality control. However, the present study remains limited to simulation-based evaluation, and physical validation using calibrated sensors, actuators, communication-delay analysis, and real nano-tank experiments is required in future work.

A Climate Driven Decision Support System for Rice Management Using SPI-3 Prediction and Particle Swarm Optimization Eka Putra; Syahril Efendi; Poltak Sihombing; T. Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1408

Climate variability and irregular rainfall patterns have become critical challenges affecting rice productivity, irrigation planning, and agricultural sustainability. Previous studies have primarily focused on rainfall forecasting or drought monitoring independently, with limited attention given to transforming climate predictions into actionable agricultural management strategies. This study addresses this gap by proposing an integrated climate-driven decision support framework that combines predictive drought-index modeling with optimization-based agronomic decision-making for adaptive rice field management. The proposed framework integrates satellite-based rainfall observations, seasonal climatic characteristics, and large-scale climate variability indicators to predict short-term moisture conditions represented by the three-month standardized precipitation index. The framework consists of three interconnected stages: climate prediction, optimization, and recommendation generation. In the prediction stage, a gradient boosting regression model enhanced with Bayesian hyperparameter optimization was employed to model nonlinear relationships among rainfall accumulation, lag rainfall patterns, seasonal cyclic features, and climate variability indicators. In the optimization stage, particle swarm optimization was applied to determine optimal fertilizer dosage, irrigation allocation, and harvest timing under varying climate conditions. Experimental procedures included comparative evaluations across multiple machine learning models, hyperparameter tuning strategies, and optimization iterations. The research figures and tables demonstrate the complete framework architecture, prediction performance comparisons, optimization convergence behavior, and adaptive rice management recommendations. Experimental results show that the proposed framework achieved strong predictive performance with a coefficient of determination of 0.851, a root mean square error of 0.391, and a mean absolute error of 0.322. Comparative analysis further confirmed that integrating climate variability indicators significantly improved predictive accuracy compared with baseline models using only historical rainfall information. The optimization process also demonstrated stable convergence toward climate-adaptive agronomic recommendations.

Deep Deterministic Policy Gradient for Simulation-Based Control of Water Quality in Nano Aquatic Systems Iwan Fitrianto Rahmad; Syahril Efendi; Poltak Sihombing; Henny Febriana Harumy
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1447

This study proposes a simulation-based Deep Deterministic Policy Gradient (DDPG) framework for water-quality control in nano aquatic systems. Nano tanks are highly sensitive to small disturbances because their limited water volume reduces buffering capacity and causes rapid changes in dissolved oxygen, ammonia, pH, temperature, biological oxygen demand, and chemical oxygen demand. To represent these coupled dynamics, a nonlinear simulation model adapted from the Continuously Stirred Tank Reactor concept is developed and implemented as an OpenAI Gym-compatible reinforcement learning environment. The DDPG agent learns continuous control actions related to aeration, feeding, and filtration through repeated interaction with the simulated nano-tank environment. The proposed nonlinear CSTR-DDPG framework is evaluated against a linear-model DDPG baseline using RMSE, cumulative reward, and closed-loop control performance. Simulation results show that the nonlinear model reduced RMSE by 45.2% for dissolved oxygen, 64.0% for ammonia, and 61.3% for pH compared with the linear baseline. The DDPG agent also achieved a 41.7% higher cumulative reward under the same reward structure. These findings indicate that nonlinear simulation can provide a more informative training environment for DDPG-based water-quality control. However, the present study remains limited to simulation-based evaluation, and physical validation using calibrated sensors, actuators, communication-delay analysis, and real nano-tank experiments is required in future work.

Diffusion2D and Anchored Inference for Asymptotic Stabilization of Diffusion-Convolutional Neural Networks in Multidomain Medical Image Classification Hanna Willa Dhany; Sutarman Sutarman; Poltak Sihombing; Mohammad Andri Budiman
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1215

Medical image classification across heterogeneous domains remains challenging due to domain shift, spatial variability, and unstable inference behavior. This study proposes a diffusion-stabilized Diffusion-Convolutional Neural Network (DCNN) framework that integrates Diffusion2D and post-hoc Anchored Diffusion to improve inference stability, probabilistic consistency, and robustness in multidomain medical image classification. The main contribution of this work is the introduction of a two-stage stabilization mechanism in which Diffusion2D performs controlled intra-image diffusion on feature representations before graph construction, while Anchored Diffusion refines uncertain predictions in the logit space through a k-nearest neighbors graph without retraining. The framework was evaluated on heterogeneous medical imaging datasets consisting of brain MRI, leukemia microscopy, and COVID-19 chest radiographs. Experimental results show that the proposed approach maintained baseline classification performance with an accuracy of 64.70% while improving the Macro-F1 score from 0.7045 to 0.7061. The diffusion mechanism reduced the average Laplacian value from 0.864355 to 0.187525, corresponding to a 78.23% reduction in spatial gradient variability. Internal analysis further demonstrated stable diffusion coefficients with a mean value of 0.141734 and a standard deviation of 0.003757, indicating controlled diffusion behavior. Anchored Diffusion selectively refined uncertain predictions, affecting only 0.6% of evaluated samples while preserving overall decision consistency. Repeated inference experiments across 40 iterations also revealed highly stable confidence trajectories with no observable variance after diffusion stabilization. The novelty of this research lies in combining feature-level diffusion stabilization, post-hoc anchored inference, and asymptotic regularization within a unified DCNN framework, providing a theoretically grounded and uncertainty-aware approach for robust multidomain medical image classification.

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