Article Per Year (5 Year)
p-Index From 2021 - 2026
1.978
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Pelita Masyarakat Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology
Rahmad B.Y Syah
Universitas Medan Area
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
Optimization of Applied Detection Rate in the Simple Evolving Connectionist System Method for Classification of Images Containing Protein Rahmad Syah; Al-Khowarizmi Al-Khowarizmi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 7, No 1 (2021): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i1.20508

Abstract

Digital image processing in general to makes images that appear converted to a function of light intensity represented in a two-dimensional plane. The function is a value that will be processed for classification so that the computer is able to recognize the image. Besides classification requires training and testing to produce a small error value and optimal algorithm. The problem of optimization is closely related to the principles and findings of science. Getting the smallest error value by calculating using MAPE for that MAPE calculation is done by using the Detection Rate formula to generalize knowledge in order to find the optimal model. Thus, the application of ANN is very suitable for optimizing classification using the Simple Evolving Connectionist System Method and as the result, the classification of images containing protein with test data is that the eggs work with optimal proof of achieving MAPE without modification of 0.1947% and MAPE which has been modified with the formula detection rate of 0.05554633%.
E-Learning Sebagai Media Pembelajaran Berbasis Edmodo Rahmad Syah; Susilawati Susilawati; Eky Ermal Muttaqin
Pelita Masyarakat Vol 1, No 1 (2019): Pelita Masyarakat, September
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (960.567 KB) | DOI: 10.31289/pelitamasyarakat.v1i1.2807

Abstract

Community demands on the quality of education services from time to time are increasingly high. Along with the development of increasingly rapid science and technology, educational institutions in various levels and levels of education can no longer stand by to preserve the cultural and performance capabilities of a school, but must strive to innovate changes in various aspects so that they are not left behind by people who live in in the era of globalization. Computer-based Information Technology is one medium that is quite effective in managing school academic information systems. The use of e-learning in schools as learning media can open up broad insights for students how to communicate in teaching and learning activities using computers. With the construction of e-learning systems as edmodo-based learning media can improve the quality of students in using computers. In addition, it is expected to facilitate students in taking a computer-based national examination.
Sensitivity of solar panel energy conversion at sunrise and sunset on three weather fluctuations in equatorial climate Habib Satria; Rahmad Syah; Nukhe Andri Silviana; Syafii Syafii
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2449-2458

Abstract

The high sunlight intensity in tropical and equatorial regions makes the potential for installing photovoltaic (PV) panels. However, the initial design of PV installations must be analyzed. Their implementation is carried out in buildings with load power for household electricity scale. For this reason, the panel reliability system could be efficient by designing the initial PV requirements using systematic measurements. Collecting data on fluctuating sunlight intensity (unpredictable weather) conditions needs the use of manual measuring tools, namely digital light meters and PV data with sensor integration. The research sample consists of three fluctuating hot weather conditions, namely hot-sunny, hot-cloudy and hot-rainy conditions. These weather conditions were taken because the climate of West Sumatra tends to shift clouds which sometimes cover the sun's rays. The peak PV output for direct current (DC) power generated during hot- sunny conditions reaches 1827.17 W, in sunny-cloudy weather it reaches around 1626.85 W and during sunny-rainy weather conditions the resulting output is 1161.81 W. From daily measurements, the results show that the efficiency of the PV system is strongly influenced by the prevailing weather climate.
Analysis of The Multilayer Perceptron Algorithm on Twitter User’s Sentiment Towards The COVID-19 Vaccine Fordinand Halomoan Pasaribu; Nurul Khairina; Dian Noviandri; Susilawati Susilawati; Rahmad Syah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9664

Abstract

The World Health Organization (WHO) declared COVID-19 a global pandemic due to its rapid spread and infection of people worldwide. The emergence of COVID-19 vaccines has garnered both support and rejection from the public. Some people support the vaccines, while others remain cautious, even though the government provides them for free. The procurement of coronavirus vaccines has generated diverse opinions in society. COVID-19 vaccines have become a trending topic on social media, particularly on Twitter. This research aims to explore public opinions on the COVID-19 vaccine. The methods used in this study include data collection, text preprocessing, TF-IDF, multilayer perceptron algorithm, and testing with confusion matrices. Out of a total of 228,208 positive, negative, and neutral opinions from Twitter users about the COVID-19 vaccine, with a training-to-testing ratio of 90% to 100%, the model will learn more by using a large amount of training data. The performance results of this research obtained the highest accuracy of 81.2%, precision of 83.8%, and recall of 71.2%. The results of sentiment analysis can be seen in the public opinions on the COVID-19 vaccine, which are divided into three categories: 35% positive opinions, 16.3% negative opinions, and 48.7% neutral opinions. The word cloud results show that positive opinions revolve around three topics: availability, cost, and dosage. Negative opinions from Twitter users about the COVID-19 vaccine focus on two main issues: vaccine side effects and deaths. Neutral opinions cover three topics, including dosage, availability, age, and expiration date
Application of E-Glass Jute Hybrid Laminate Composite with Curved Shape on Compressive Strength of Cylindrical Column Concrete Achmad Jusuf Zulfikar; Mohd Yuhazri Yaakob; Rahmad Bayu Syah
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v5i1.2072

Abstract

This study provides a better understanding of reinforcing cylindrical concrete columns (CCC) using a hybrid laminated composite material (HLC) composed of jute and e-glass fibers, including the influence of layer quantity on strength and a comparison with previous research. The utilization of these alternative materials may lead to the development of novel and efficient solutions for constructing durable and robust structures. The primary objectives of this research are to assess the effects of employing HLC as a reinforcing layer on CCC compressive strength, optimize the reinforcement process by selecting appropriate layer sequences and types, and analyze the type of fiber damage in relation to the strength of HLC composite material. The materials utilized in this study encompass woven jute fabric sheets, e-glass fiber sheets, and epoxy resin. Compressive strength testing was conducted following ASTM C39 standards. Specimen variations were based on the number and type of reinforcing layers. The results revealed that CCC compressive strength increased by up to 100% with the application of up to three layers of jute compared to an unlayered specimen. Furthermore, CCC compressive strength experienced a remarkable enhancement of up to 150% with the incorporation of HLC composite. Hence, the implementation of HLC demonstrates significant potential for augmenting the strength of concrete structures.
Potential microgrid model based on hybrid photovoltaic/wind turbine/generator in the coastal area of North Sumatra Habib Satria; Rahmad Syah; Dadan Ramdan; Muhammad Khahfi Zuhanda; Jaka Windarta; Syafii Syafii; Almoataz Youssef Abdelaziz
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.pp768-776

Abstract

The high potential for renewable energy in the North Sumatra region, especially the coast of the Belawan area, needs to be exploited properly. The design that will be carried out is to explore the potential in coastal areas by simulating microgrid systems and hybrid system-based electricity installations. The method that will be used is to find the accuracy of strategic location points by considering the panel surface temperature which will later influence the power output of the power plant. Then find the ideal installation location as a reliable system when irregular climate conditions occur, of course this phenomenon will have a significant effect on energy balance and energy conversion, especially in coastal areas. The potential for installation construction will be carried out with a hybrid system using power sources from photovoltaics, wind turbines and diesel generators assisted by HOMER Pro software. The results of testing with simulations and information data that have been recorded in the software can later be used as a benchmark in planning electrical installations and also for identifying microgrid protection challenges. Then the measurement results that have been obtained for the installation of a hybrid-based microgrid system on Photovoltaic (PV) are DC output power of 618.80 W with measurements of sunny weather conditions, then the potential wind speed on the wind turbine reaches 5 m/s and the potential use of a diesel generator reaches 40% with power output capacity 1 kW.
Normalization Layer Enhancement in Convolutional Neural Network for Parking Space Classification sayuti rahman; Marwan Ramli; Arnes Sembiring; Muhammad Zen; Rahmad B.Y Syah
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.
A Hybrid PCA, MPK Method for Multi-Objective Human Resource Analytics in Plantation Management Gilang Ramadhan; Rahmad Syah; Ahmad Rafiki
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.6903

Abstract

This study presents a hybrid framework combining Principal Component Analysis (PCA) and Multi‐Objective Complexity Prediction (MPK) to extract actionable insights from multidimensional human resource (HR) data in plantation companies. Initially, PCA reduces nine original HR variables including age, tenure, absenteeism, leave, harvest yield, and performance scores to four principal components that capture over 90 % of total variance. These reduced features form the solution space for MPK agents, each of which simultaneously optimizes three objectives: absenteeism risk (predicted via SVR), performance score (via linear regression), and workload imbalance. The model is tuned with 5-fold cross-validation on 80 % of the data, yielding MAE ≈ 6.5 days and R² ≈ 0.82 for absenteeism, MAE ≈ 4.2 points and R² ≈ 0.78 for performance, MAPE ≈ 15 % for workload imbalance, and a Pareto‐front hypervolume of ≈ 0.90. Validation on the remaining 20 % hold-out set confirms similar generalization (hypervolume ≈ 0.88). Sensitivity analysis with ± 10 % input noise demonstrates the approach’s robustness under moderate perturbations. These results illustrate that PCA–MPK can both streamline high-dimensional HR datasets and deliver reliable, multi-objective forecasts to inform strategic workforce planning.
Predicting Burnout in Start-Up Environments: A Multivariate Risk Scoring Approach for Early Managerial Intervention Nos Sutrisno; Maricha Elveny; Andre Hasudungan Lubis; Rahmad Syah; Hartono Hartono; Sabina Krisdayanti
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1663

Abstract

Start-up organisations operate under fast timelines, lean staffing, and constantly shifting priorities, exposing employees to chronic workload pressure and emotional strain. Unmanaged burnout in these settings threatens individual well-being, talent retention, and long-term execution capacity. This study proposes a multivariate burnout risk scoring approach that aims to identify and prioritise employees at elevated risk before full deterioration occurs, enabling early managerial intervention rather than reactive recovery. The proposed pipeline integrates principal component analysis (PCA), Random Forest, and Support Vector Machine (SVM). PCA is first applied to reduce redundancy across workplace indicators, yielding five principal components (PC1–PC5) that together explain 88% of the total variance in self-reported stress level, job satisfaction, emotional exhaustion, work-life balance, performance, and social interaction. These components are then used as predictors in two supervised classification models, Random Forest and SVM, to estimate the likelihood that each employee belongs to a high-burnout-risk class. The Random Forest model achieved an accuracy of 88%, and the SVM model achieved an accuracy of 86%, demonstrating strong predictive capability in distinguishing higher-risk employees from lower-risk employees. The resulting predicted probability is interpreted as an individualised burnout risk score, which can be mapped to action categories such as workload redistribution, role clarification, targeted supervisory check-ins, or temporary protection from critical-path tasks. In this way, the framework operationalises burnout prediction not only as a detection task but also as an actionable decision-support signal for leaders. The study therefore offers both a quantitative method for forecasting burnout in start-up environments and a practical structure for translating prediction into preventive intervention.
An Integrated Linguistic and Metaheuristic-Optimized Elman Neural Network Framework for Cyberbullying Detection Siti Aisyah; Arnes Sembiring; Faadhil Faadhil; Hartono Hartono; Rahmad Syah; M. Khahfi Zuhanda
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.v7i3.1337

Abstract

The rapid growth of social media platforms has intensified the need for accurate cyberbullying detection systems capable of understanding contextual and linguistically complex expressions. Existing machine learning and deep learning approaches often suffer from limited interpretability, insufficient contextual understanding, and suboptimal parameter optimization, reducing their effectiveness in identifying harmful online content. This study proposes a novel cyberbullying detection framework that integrates Linguistic Rule-Based Feature Extraction, an Elman Neural Network (ENN), and the Local Search-Based Improved Bat Algorithm (LSBIA). The main contribution of this research lies in the synergistic combination of interpretable linguistic knowledge, contextual sequence modeling, and metaheuristic optimization within a unified classification framework. Linguistic rules are employed to capture negation patterns, intensifiers, and adjective–noun relationships, while ENN models contextual dependencies through recurrent memory structures. LSBIA is utilized to optimize network parameters and improve convergence stability. Experiments were conducted using textual data collected from Instagram, Twitter, and Facebook and evaluated using stratified 10-fold cross-validation. The proposed method achieved an accuracy of 99.12%, precision of 94.73%, recall of 97.45%, and F1-score of 93.91%, outperforming Support Vector Machine (91.20% accuracy), Naïve Bayes (89.75%), and Decision Tree (90.10%). Ablation experiments further demonstrated the importance of each component, where removing linguistic rules reduced accuracy to 94.90%, removing sentiment scoring reduced accuracy to 96.30%, and replacing ENN with LSTM, GRU, or Transformer architectures resulted in lower accuracies of 92.50%, 91.90%, and 93.20%, respectively. These findings confirm that integrating linguistic feature engineering, contextual neural modeling, and metaheuristic optimization significantly enhances cyberbullying detection performance while maintaining interpretability. The novelty of this study resides in the integration of linguistic rule-based representation with LSBIA-optimized ENN for context-aware cyberbullying classification.
Co-Authors Achmad Jusuf Zulfikar Ahmad Rafiki Al Khowarizmi Almoataz Youssef Abdelaziz Andre Hasudungan Lubis Arnes Sembiring Arnes Sembiring Dadan Ramdan Dian Noviandri Eky Ermal Muttaqin Faadhil, Faadhil Fordinand Halomoan Pasaribu Gilang Ramadhan Habib Satria Hartono Hartono Jaka Windarta M. Khahfi Zuhanda Maricha Elveny Marwan Ramli Mohd Yuhazri Yaakob Muhammad Khahfi Zuhanda Muhammad Zen Nos Sutrisno Nurul Khairina Rahman, Sayuti Sabina Krisdayanti Siti Aisyah Susilawati Susilawati Syafii . Syafii Syafii