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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Applied Data Sciences
Syah, Rahmad B.Y
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Design of actuator motor acceleration model in dual axis tracker movement for stand-alone PV system Satria, Habib; Syah, Rahmad B.Y; Silviana, Nukhe Andri; Syafii, Syafii
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp1137-1146

Abstract

Stand-alone photovoltaic system or PV is a power generation technology with potential that is environmentally friendly and also one of the solutions for saving high electricity rates today. However, problems that often occur due to weather fluctuations that are always changing, especially North Sumatra, Indonesia result in the conversion produced by solar cells not being optimal. Therefore, it is necessary to do a new model with a dual tracker system and the development of accelerator motor actuators so that the resulting energy conversion is more optimal. The result of optimizing the reliability of the polycrystalline type solar panel which is designed with an additional photovoltaic tracker system to maximize the conversion of solar energy to solar panels is to obtain an output power of 303.72 volts DC and 267.52 volts DC in the position where the tracker is not used. Then the percentage increase in energy reached 29.80%. Dual axis tracker technology is able to maximize energy conversion in improving PV usage performance. The implementation of a stand-alone PV system will be beneficial if the installation is in Indonesian territory, especially in disadvantaged, frontier and outermost areas.
An Effective Hybrid Approach for Predicting and Optimizing Business Complexity Metrics and Data Insights Syah, Rahmad B.Y; Elveny, Marischa; Ananda, Rana Fathinah; Nasution, Mahyuddin K.M; Hartono, Hartono
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.830

Abstract

This study proposes a hybrid approach for optimizing complexity prediction in the domain of business intelligence by integrating three powerful techniques: the Multi-Objective Complexity Prediction Model (MPK), Principal Component Analysis (PCA), and the XGBoost regression algorithm. The MPK model serves as a state-based simulator to capture system complexity dynamics, while PCA is employed to reduce data dimensionality and eliminate redundancy among features. Subsequently, XGBoost is used as a non-linear predictive model to estimate complexity values based on the refined input features. The results show that this hybrid approach significantly improves prediction accuracy, reduces data noise, and streamlines the modelling process. Quantitative evaluation using Mean Squared Error (MSE), Mean Absolute Error (MAE), and the R-squared (R²) metric demonstrates exceptional performance, with an MAE of 0.000035, an MSE of 6.7 × 10⁻⁹, and an R² of 0.9999999. These results confirm that the integration of MPK, PCA, and XGBoost is highly effective for complexity prediction tasks and can provide accurate and insightful outcomes in business intelligence analytics.
Enhanced Detection of Consumer Behavioral Shifts in E-Commerce Platforms with Transformer-Based Algorithms Syah, Rahmad B.Y; Elveny, Maricha; Darmansyah, Soleh; Silviana, Lia
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.907

Abstract

This research aims to analyze changes in consumer behavior on e-commerce platforms using consumer interaction data such as view, add to cart, and purchase.  Identifying changes in consumer behavior on e-commerce platforms is very important because it can provide deeper insight into consumer motivations and preferences. By better understanding how consumers interact with products, companies can design more targeted strategies to increase conversions, reduce cart abandonment, and improve the overall customer experience. The DistilBERT based prediction model is applied to detect and predict changing patterns of consumer behavior in the purchasing process. DistilBERT was chosen because of its more efficient capabilities compared to previous models which enable faster data processing and lower resource usage, which is very important for real-time applications on e-commerce platforms with big data. The data used includes consumer interactions during a certain period, with model evaluation using precision, recall, F1-score, and accuracy metrics. The results showed that despite an increase in the number of actions such as View and Add to Cart, conversion to Purchase was still hampered, indicating a cart abandonment problem. The model used managed to achieve 90% accuracy, with a precision value of 0.87, recall of 0.85, and F1-score of 0.86, showing excellent performance in predicting changes in consumer behavior. Based on the results of this analysis, companies can optimize marketing strategies by targeting consumers who have added products to their basket but have not yet made a purchase, as well as making price adjustments, discounts, and limited time offers. This research also emphasizes the importance of using real-time data to dynamically adjust marketing strategies and improve customer experience.
Normalization Layer Enhancement in Convolutional Neural Network for Parking Space Classification rahman, sayuti; Ramli, Marwan; Sembiring, Arnes; Zen, Muhammad; Syah, Rahmad B.Y
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3871

Abstract

The research problem of this study is the urgent need for real-time parking availability information to assist drivers in quickly and accurately locating available parking spaces, aiming to improve upon the accuracy not achieved by previous studies. The objective of this research is to enhance the classification accuracy of parking spaces using a Convolutional Neural Network (CNN) model, specifically by integrating an effective normalizing function into the CNN architecture. The research method employed involves the application of four distinct normalizing functions to the EfficientParkingNet, a tailored CNN architecture designed for the precise classification of parking spaces. The results indicate that the EfficientParkingNet model, when equipped with the Group Normalization function, outperforms other models using Batch Normalization, Inter-Channel Local Response Normalization, and Intra-Channel Local Response Normalization in terms of classification accuracy. Furthermore, it surpasses other similar CNN models such as mAlexnet, you only look once (Yolo)+mobilenet, and CarNet in the same classification task. This demonstrates that EfficientParkingNet with Group Normalization significantly enhances parking space classification, thus providing drivers with more reliable and accurate parking availability information.
Co-Authors Ananda, Rana Fathinah Darmansyah, Soleh Elveny, Maricha Habib Satria Hartono Hartono Marischa Elveny, Marischa Marwan Ramli Muhammad Zen, Muhammad Nasution, Mahyuddin K.M Rahman, Sayuti Sembiring, Arnes Silviana, Lia Silviana, Nukhe Andri Syafii .