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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Martabe : Jurnal Pengabdian Kepada Masyarakat 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 Yayasan Cita Cendikiawan Al Khwarizmi Jurnal Pengabdian Masyarakat (Jubdimas)
Rahmad B.Y Syah
Universitas Medan Area
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PELATIHAN PENERAPAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN JUMLAH PEMBERIAN PAKAN IKAN DI DESA MARIENDAL II Muhammad Khahfi Zuhanda; Hartono Hartono; Sayuti Rahman; Arnes Sembiring; Rahmad Syah; Dadan Ramdan; Mendarissan Aritonang; Citra Rahmadhani; Suswati Suswati; Habib Satria; Erianto Ongko
JUBDIMAS ( Jurnal Pengabdian Masyarakat) Vol 5 No 1 (2026): Artikel Pengabdian Maret 2026
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jubdimas.v5i1.403

Abstract

This community service activity aims to improve the efficiency of fish feeding management through the implementation of a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method in Mariendal II Village. The main problem faced by fish farmers is the manual feeding process based on estimation, leading to inefficiency and suboptimal fish growth. The method used in this activity is a participatory approach consisting of socialization, training, technology implementation, and evaluation. The developed system considers several criteria, including fish biomass, age, population, water quality, feeding time, and feed type. The results show that participants experienced significant improvements in knowledge and skills in using the DSS. The system successfully provided optimal feeding recommendations and was integrated with an automatic feeder, resulting in more consistent and efficient feeding practices. This activity also increased farmers’ awareness of technology adoption in aquaculture. Overall, the implementation of DSS contributes to reducing feed waste, improving productivity, and supporting sustainable fish farming practices.
Pemanfaatan Teknologi Digital untuk Meningkatkan Produktivitas dan Kemandirian Peternak Ikan Desa Mariendal II Bayu Aditya Pratama; Nasaruddin Nur Hasibuan; Sayuti Rahman; Dadan Ramdan; Rahmad Syah; Hartono; M. Khahfi Zuhanda; Arnes Sembiring; Asmah Indrawati; Habib Satria; Iqbal Giffari Ritonga
Dedikasi Sains dan Teknologi (DST) Vol. 5 No. 2 (2025): Artikel Pengabdian Nopember 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v5i2.7875

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan produktivitas dan kemandirian peternak ikan air tawar di Desa Mariendal II melalui penerapan sistem feeder dan monitoring pakan ikan berbasis Internet of Things (IoT). Permasalahan utama mitra meliputi pemberian pakan yang masih dilakukan secara manual, ketidakteraturan jadwal pakan, pemborosan pakan, serta keterbatasan literasi teknologi. Metode pelaksanaan kegiatan menggunakan pendekatan partisipatif yang meliputi tahap persiapan, pelatihan dan implementasi sistem IoT, pendampingan, serta evaluasi. Evaluasi dilakukan menggunakan pre-test dan post-test untuk mengukur peningkatan pemahaman dan keterampilan peserta. Hasil kegiatan menunjukkan peningkatan rata-rata kompetensi peserta sebesar 47%, dengan peningkatan tertinggi pada keterampilan instalasi dan pengoperasian perangkat serta kemampuan membaca hasil monitoring pakan. Nilai effect size yang sangat besar menunjukkan bahwa peningkatan kompetensi dipengaruhi secara signifikan oleh intervensi kegiatan. Selain itu, penerapan sistem feeder otomatis memberikan dampak operasional berupa peningkatan keteraturan pemberian pakan, pengurangan pemborosan pakan, serta efisiensi waktu dan tenaga kerja peternak. Tingginya tingkat keberterimaan teknologi tercermin dari konsistensi penggunaan sistem oleh sebagian besar peserta setelah kegiatan berakhir. Dengan demikian, kegiatan PkM ini membuktikan bahwa penerapan teknologi IoT yang tepat guna dan disertai pendampingan berkelanjutan mampu meningkatkan efisiensi budidaya ikan sekaligus mendorong kemandirian peternak secara berkelanjutan.
A Hybrid GDHS and GBDT Approach for Handling Multi-Class Imbalanced Data Classification Hartono Hartono; Muhammad Khahfi Zuhanda; Rahmad Syah; Sayuti Rahman; Erianto Ongko
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.894

Abstract

Multiclass imbalanced classification remains a significant challenge in machine learning, particularly when datasets exhibit high Imbalance Ratios (IR) and overlapping feature distributions. Traditional classifiers often fail to accurately represent minority classes, leading to biased models and suboptimal performance. This study proposes a hybrid approach combining Generalization potential and learning Difficulty-based Hybrid Sampling (GDHS) as a preprocessing technique with Gradient Boosting Decision Tree (GBDT) as the classifier. GDHS enhances minority class representation through intelligent oversampling while cleaning majority classes to reduce noise and class overlap. GBDT is then applied to the resampled dataset, leveraging its adaptive learning capabilities. The performance of the proposed GDHS+GBDT model was evaluated across six benchmark datasets with varying IR levels, using metrics such as Matthews Correlation Coefficient (MCC), Precision, Recall, and F-Value. Results show that GDHS+GBDT consistently outperforms other methods, including SMOTE+XGBoost, CatBoost, and Select-SMOTE+LightGBM, particularly on high-IR datasets like Red Wine Quality (IR = 68.10) and Page-Blocks (IR = 188.72). The method improves classification performance, especially in detecting minority classes, while maintaining high accuracy.
Performance of single axis tracker technology and automatic battery monitoring in solar hybrid systems Habib Satria; Sapto Nisworo; Jaka Windarta; Rahmad B. Y. Syah
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5506

Abstract

Utilization of elevation angles and azimuth angles is a very important part in maximizing solar energy into electrical energy in photovoltaic (PV). One way to maximize PV power output is to design a single axis tracker system and take into account the azimuth and elevation angles of the sun using the sun position calculator application. The single axis tracker system is set based on the position of the angle of inclination of the surface of the PV 45°, then the angle of 90° and the angle of inclination of 135°. The test results show that the single axis tracker PV system design can work based on the angle settings that have been programmed. Then the use of a battery control system to support the PV reliability system automatically cuts off electricity when the battery voltage drops below 12 V during cloudy weather conditions and excessive battery usage. The integration of the PZEM-017 module with the battery will support monitoring of battery power usage in real time. PV energy data conversion performance uses single axis tracker technology for maximum power reaching 631.72 Watt DC at 12.00 pm and the lowest power reaching 56.02 Watt DC at 6.00 pm.
Complexity prediction model: a model for multi-object complexity in consideration to business uncertainty problems Rahmad B. Y. Syah; Habib Satria; Marischa Elveny; Mahyuddin K. M. Nasution
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5380

Abstract

In a competitive environment, the ability to rapidly and successfully scale up new business models is critical. However, research shows that many new business models fail. This research looks at hybrid methods for minimizing constraints and maximizing opportunities in large data sets by examining the multivariable that arise in user behavior. E-metric data is being used as assessment material. The analytical hierarchy process (AHP) is used in the multi-criteria decision making (MCDM) approach to identify problems, compile references, evaluate alternatives, and determine the best alternative. The multi-objectives genetic algorithm (MOGA) role analyzes and predicts data. The method is being implemented to expand the information base of the strategic planning process. This research examines business sustainability along two critical dimensions. First, consider the importance of economic, environmental, and social evaluation metrics. Second, the difficulty of gathering information will be used as a predictor for making long-term business decisions. The results show that by incorporating the complexity features of input optimization, uncertainty optimization, and output value optimization, the complexity prediction model (MPK) achieves an accuracy of 89%. So that it can be used to forecast future business needs by taking into account aspects of change and adaptive behavior toward the economy, environment, and social factors.
A hybrid oversampling for imbalanced data using incremental SMOTE-KMeans and cluster-structured positive class-SVM Hartono Hartono; Muhammad Khahfi Zuhanda; Rahmad Syah
International Journal of Advances in Intelligent Informatics Vol 12, No 3 (2026): August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i3.2432

Abstract

Class imbalance remains a significant challenge in classification tasks, particularly when the minority class exhibits complex internal structures. This study proposes a unified framework that integrates Incremental SMOTE-KMeans with a cluster-structured positive class-SVM (CS-PC-SVM) to jointly address data imbalance and structural heterogeneity. The proposed method introduces structural alignment between oversampling and classification by generating synthetic samples only within reliable clusters and modeling the minority class as multiple subgroups. This design reduces noise, preserves local data structure, and enables more adaptive decision boundaries. Experimental results on five benchmark datasets demonstrate that the proposed approach achieves consistently strong and balanced performance, with Accuracy up to 0.981, G-Mean 0.969, Precision 0.967, and Recall 0.962, outperforming or remaining competitive with existing methods. These findings highlight the effectiveness of integrating structure-guided data generation with structure-aware classification for improving robustness in imbalanced learning scenarios.
INTERVENSI TEKNOLOGI TEPAT GUNA BERBASIS DIGITAL ECONOMY UNTUK PENGOPTIMALAN USAHA UMKM DODOL BENGKEL KOTA PERBAUNGAN KABUPATEN SERDANG BEDAGAI Retna Astuti Kuswardani; Rahmad Syah; Sri Ariani Safitri
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 7, No 12 (2024): MARTABE : JURNAL PENGABDIAN MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v7i12.5435-5439

Abstract

Penggunaan teknologi telah menjadi salah satu strategi kunci dalam meningkatkan daya saing dan jangkauan pasar bagi Usaha Mikro, Kecil, dan Menengah (UMKM) dodol Bengkel. Pengabdian Kepada Masyarakat ini bertujuan untuk  a) membantu UMKM dalam pengadaan peralatan proses pembuatan dodol dan melakukan differensiasi produk dodol dengan berbagai bentuk, warna dan rasa serta kemasan sehingga dapat menarik konsumen dan b) membantu UMKM dalam menerapkan digital marketing sehingga produk dodol pasar bengkel dapat dipasarkan secara online dan melakukan dokumentasi pembukuan secara digital. Pengabdian Kepada Masyarakat dilakukan dengan menggunakan metode forum group discussion dan melakukan pelatihan pada pemilik usaha UMKM Dodol Bengkel dan karyawan. Hasil dari pengabdian menunjukkan bahwa peserta pelatihan memahami penggunaan mesin pengaduk dodol, pembuatan produk dodol dengan variasi bentuk, rasa dan warna, pemahaman terkait design kemasan produk, pemasaran digital dan pembuatan laporan keuangan secara digital.
An Effective Hybrid Approach for Predicting and Optimizing Business Complexity Metrics and Data Insights Rahmad B.Y Syah; Marischa Elveny; Rana Fathinah Ananda; Mahyuddin K.M Nasution; 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.
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 Asmah Indrawati Bayu Aditya Pratama Citra Rahmadhani Dadan Ramdan Dian Noviandri Eky Ermal Muttaqin Erianto Ongko Faadhil, Faadhil Fordinand Halomoan Pasaribu Gilang Ramadhan Habib Satria Habib Satria Hartono Hartono Hartono Hartono Hartono Iqbal Giffari Ritonga Jaka Windarta Jaka Windarta M. Khahfi Zuhanda Mahyuddin K. M. Nasution Mahyuddin K.M Nasution Maricha Elveny Marischa Elveny Marischa Elveny Marwan Ramli Mendarissan Aritonang Mohd Yuhazri Yaakob Muhammad Khahfi Zuhanda Muhammad Zen Nasaruddin Nur Hasibuan Nos Sutrisno Nurul Khairina Rahman, Sayuti Rana Fathinah Ananda Retna Astuti Kuswardani Sabina Krisdayanti Sapto Nisworo Siti Aisyah Sri Ariani Safitri Susilawati Susilawati Suswati Suswati Syafii . Syafii Syafii