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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics Jurnal TIMES International Journal of Advances in Intelligent Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JTAM (Jurnal Teori dan Aplikasi Matematika) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Mantik Indonesian Journal of Electrical Engineering and Computer Science Ihsan: Jurnal Pengabdian Masyarakat Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Yayasan Cita Cendikiawan Al Khwarizmi EXPLORER Educational Building: Jurnal Pendidikan Teknik Bangunan dan Sipil Prioritas : Jurnal Pengabdian Kepada Masyarakat Nanggroe: Journal Of Scholarly Service Nanggroe: Journal of Scholarly Service Jurnal Pengabdian Masyarakat (Jubdimas)
Muhammad Khahfi Zuhanda
Universitas Medan Area
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Analisis Komparatif CNN Ringan untuk Klasifikasi Penyakit Daun Tomat Menggunakan Visualisasi Grad-CAM Rahman, Sayuti; Hartono, Hartono; Sembiring, Arnes; Khahfi Zuhanda, muhammad; Aditya Pratama, Bayu; Martini, Dewi
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2601

Abstract

Tomato leaf disease classification based on digital imagery has become an important approach in supporting smart agriculture, particularly for early detection of plant disease attacks. This study aims to compare the performance of several lightweight Convolutional Neural Network (CNN) architectures, namely MobileNetV3-Small, MobileNetV2, and EfficientNet-B0, in classifying tomato leaf diseases using the PlantVillage dataset. The dataset consists of 3,628 images distributed across 10 classes (9 disease classes and 1 healthy class), with a data split scheme of 80% for training and 20% for validation. Performance evaluation was conducted using classification reports, confusion matrices, and interpretability analysis through Grad-CAM and feature map visualization. The experimental results show that all models achieved very high accuracy, exceeding 99%. EfficientNet-B0 obtained the best performance with a validation accuracy of 99.59%, followed by MobileNetV2 at 99.45% and MobileNetV3-Small at 99.04%. However, model complexity increased along with accuracy, where EfficientNet-B0 had the largest number of parameters and FLOPs. Grad-CAM analysis revealed that higher-accuracy models demonstrated more precise activation focus on leaf lesion regions. This study confirms that lightweight CNN architectures are capable of delivering excellent classification performance while offering strong potential for deployment in plant disease detection systems on resource-limited devices
Hybrid Approach for Class Imbalance Handling using Adaptive Weighted Oversampling and Instance Hardness-Based Undersampling Hartono Hartono; Erianto Ongko; Muhammad Khahfi Zuhanda
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.36972

Abstract

Class imbalance remains a major challenge in multi-class classification, where existing hybrid resampling methods often combine oversampling and undersampling in a loosely coupled manner, without explicitly coordinating minority enrichment and majority reduction. In this experimental study, we propose a novel hybrid resampling method, Adaptive Weighted Oversampling and Instance Hardness-Based Undersampling (AWO-IHU), which differs from existing hybrid approaches by explicitly aligning boundary-aware minority oversampling with instance hardness-based majority undersampling. Rather than independently applying oversampling and undersampling, the proposed method integrates both processes through a coordinated design guided by classification difficulty to improve decision boundary quality. Methodologically, AWO-IHU first applies adaptive weighted oversampling to emphasize informative minority instances near class boundaries, followed by instance hardness-based undersampling that selectively removes redundant majority samples using an ensemble-based difficulty estimation. The experimental evaluation is conducted using multiple benchmark datasets with varying numbers of instances, attributes, and classes. Classification performance is evaluated using Accuracy, Precision, Recall, and Cohen’s Kappa, enabling a comprehensive assessment of overall correctness, minority sensitivity, and agreement beyond chance under class imbalance. Experimental results show that AWO-IHU consistently outperforms SMOTE, Random Undersampling, and conventional hybrid sampling methods. In particular, the proposed method achieves perfect or near-perfect Recall values up to 1.0, while maintaining high Precision values above 0.89 and producing the highest Cohen’s Kappa values up to 0.86. These findings demonstrate that explicitly coordinating minority enrichment with difficulty-aware majority reduction yields more reliable decision boundary learning and improved generalization in imbalanced multi-class classification. 
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.
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.
Metaheuristic nurse scheduling with hospital clustering using flower pollination algorithm Muhammad Khahfi Zuhanda; Hartono Hartono; Sayuti Rahman; Prana Ugiana Gio; Erianto Ongko
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11243

Abstract

Effective nurse scheduling is essential to ensure balanced workloads, reduce fatigue, and maintain healthcare service quality. However, the nurse scheduling problem (NSP) is complex due to constraints related to nurse skills, task requirements, and legal working-hour limits. This study proposes an integrated framework combining a mathematical optimization model with metaheuristic algorithms to generate optimal daily nurse activity schedules. Genetic algorithm (GA) and simulated annealing (SA) are employed to produce near-optimal solutions for nurse populations ranging from 3 to 50 individuals, considering skill-level compatibility, workload balance, and maximum working hours. Experimental results using real scheduling data from 30 nurses across three skill levels demonstrate that all generated schedules satisfy the imposed constraints, with no nurse exceeding the 12hour daily working limit. Comparative analysis shows that GA achieves lower scheduling costs for larger nurse populations, while SA consistently requires significantly shorter computation times, making it suitable for time-sensitive applications. In addition, the flower pollination algorithm (FPA) is used to cluster 3,155 hospitals based on bed capacity, service variety, and workforce size, supporting data-driven workforce distribution analysis. The proposed framework integrates operational scheduling optimization with hospital-level clustering, providing practical decision support for healthcare workforce planning.
Sosialisasi Pemanfaatan IoT Berbasis Machine Learning pada Deteksi Penyakit Tanaman Sawit untuk Pertanian Berkelanjutan di Dusun I Bukit Gantung Desa Sumber Mulyo Hartono; M. Khahfi Zuhanda; Sayuti Rahman; Retna Astuti Kuswardani; Suswati; Muhammad Zen; Erianto Ongko; Lili Suryati
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.v5i2.7148

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilaksanakan untuk meningkatkan literasi digital petani sawit di Dusun I Bukit Gantung, Desa Sumber Mulyo, melalui sosialisasi pemanfaatan Internet of Things (IoT) berbasis machine learning untuk smart agriculture dalam deteksi dini penyakit tanaman sawit. Pelatihan dirancang dalam empat tahapan, yaitu persiapan perangkat, penyampaian materi konseptual, praktik instalasi IoT, penerapan machine learning, serta pendampingan lapangan. Evaluasi dilakukan menggunakan pre-test dan post-test untuk mengukur perubahan kompetensi peserta terhadap konsep IoT, machine learning, instalasi sensor, dan interpretasi hasil deteksi. Berdasarkan analisis, evaluasi hasil pelatihan menunjukkan peningkatan sebesar 47%, dengan kenaikan tertinggi pada keterampilan instalasi sensor dan membaca hasil aplikasi (+54%). Penerapan teknologi ini membantu petani melakukan deteksi penyakit lebih cepat dan akurat sehingga penggunaan pestisida dapat ditekan melalui penyemprotan selektif. Selain menghasilkan peningkatan kompetensi teknis, kegiatan ini meningkatkan keberterimaan teknologi di kalangan petani, terbukti dari 14 dari 15 kelompok yang secara konsisten menggunakan perangkat IoT pascapelatihan. Secara sosial, program ini mendorong perubahan perilaku kolektif menuju praktik budidaya yang lebih aman, efisien, dan berbasis data. Implementasi ini menunjukkan bahwa integrasi IoT–machine learning mampu memperkuat keberlanjutan pertanian sawit sekaligus meningkatkan kualitas pengelolaan kebun masyarakat, serta memberikan dasar penting bagi pengembangan sistem monitoring kesehatan tanaman yang lebih komprehensif dan adaptif pada skala komunitas.
Sosialisasi Pembuatan Pakan Fermentasi untuk Ternak Sapi di Dusun I Bukit Gantung Desa Sumber Mulyo Sayuti Rahman; Muhammad Khahfi Zuhanda; Dadan Ramdan; Hartono Hartono; Rahmad Syah; Arnes Sembiring; Retna Astuti Kuswardani; Suswati Suswati; Ramadhan Ady Pratama; Dewi Martini
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 02 (2025): EDISI SEPTEMBER 2025
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ketersediaan pakan berkualitas menjadi tantangan utama bagi peternak sapi di Dusun I Bukit Gantung, Desa Sumber Mulyo, terutama pada musim kemarau ketika hijauan terbatas dan harga pakan komersial meningkat. Ketergantungan pada rumput dan jerami tanpa pengolahan menyebabkan rendahnya nilai nutrisi serta pertumbuhan ternak yang tidak optimal. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan memberikan edukasi dan pelatihan kepada peternak mengenai pembuatan pakan fermentasi sebagai solusi aplikatif, murah, dan berkelanjutan. Metode pelaksanaan mencakup sosialisasi konsep fermentasi, demonstrasi teknis, dan hands-on training mulai dari pemilihan bahan, pencacahan, pencampuran dengan EM4, pengaturan kadar air, hingga pengemasan anaerob dan proses inkubasi. Evaluasi dilakukan melalui pre-test dan post-test untuk mengukur peningkatan pengetahuan dan keterampilan peserta. Hasil kegiatan menunjukkan peningkatan signifikan antara 40% hingga 54% pada aspek pemahaman konsep dasar, teknik pengolahan, serta kemampuan menganalisis kesalahan fermentasi. Pelatihan ini terbukti efektif dalam membangun kapasitas peternak untuk memproduksi pakan fermentasi secara mandiri, memanfaatkan limbah pertanian, dan mengurangi ketergantungan pada pakan komersial. Penerapan teknologi fermentasi diharapkan mampu meningkatkan produktivitas ternak serta mendukung praktik peternakan yang lebih efisien dan 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.
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.
Co-Authors Aditya Pratama, Bayu Almoataz Youssef Abdelaziz Anil Hakim Syofra Anil Hakim Syofra Syofra Arnes Sembiring Arnes Sembiring Asmah Indrawati Citra Rahmadhani Dadan Ramdan Dadan Ramdan Dahlan Abdullah Desniarti Desniarti D Desniarti Desniarti Devy Mathelinea Dewi Martini Dian Noviandri Eliska Eliska Erianto Ongko Erianto Ongko Erianto Ongko Erianto Ongko Faadhil, Faadhil Fenny Fenny Finta Aramita Ginting, Manan Gio, Prana Ugiana Habib Satria Hartono Hartono Hartono Hartono Hartono Hartono Hartono Hasibuan, Samsul A Rahman Sidik Hutagalung, Manahan Indri Dayana Jaka Windarta Jirwanto, Henry Kelvin Leonardi Kohsasih Kurnia, Dian Lili Suryati Maengseok Noh Mardingsih Mardingsih Martini, Dewi Mendarissan Aritonang Muhammad Hafiz Muhammad Hafiz Muhammad Zen Nanda Novita Nanda Novita, Nanda Opim Salim Sitompul Pieter Octaviandy, Pieter Prana Ugi Prana Ugiana Gio Rahmad B.Y Syah Rahmad Syah Rahman, Sayuti Ramadhan Ady Pratama Retna Astuti Kuswardani Retna Astuti Kuswardani Rezzy Eko Caraka Rizka Fahruza Siregar Rudi Salam Saib Suwilo Samsul A Rahman Sidik Hasibuan Sayuti Rahman Sembiring, Arnes Siti Aisyah Siti Alhamra Salqaura Sjukun Sukiman Sukiman Suswati suswati suswati Suswati Suswati Suswati Suswati Syafii . Syah, Rahmad B. Y. Tarigan, Nurliana Tino Hermanto Widiantho, Yuri Wily Julitawaty, Wily Yuan Anisa