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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Manajemen & Keuangan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Mantik Indonesian Journal of Electrical Engineering and Computer Science Ihsan: Jurnal Pengabdian Masyarakat International Journal of Engineering, Science and Information Technology Educational Building: Jurnal Pendidikan Teknik Bangunan dan Sipil Prioritas : Jurnal Pengabdian Kepada Masyarakat Nanggroe: Journal Of Scholarly Service Nanggroe: Journal of Scholarly Service
Muhammad Khahfi Zuhanda
Universitas Medan Area
Religion Agriculture, Biological Sciences & Forestry 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions 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 Other

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Penerapan Smart Farming Sebagai Upaya Modernisasi Pertanian Cabai Rahman, Sayuti; Indrawati, Asmah; Sembiring, Arnes; Hartono, Hartono; Zuhanda, Muhammad Khahfi; Ongko, Erianto
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 02 (2024): EDISI SEPTEMBER 2024
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/prioritas.v6i02.1050

Abstract

Cabai merupakan salah satu komoditas hortikultura yang memiliki nilai ekonomi tinggi, namun produktivitasnya sering terganggu oleh berbagai penyakit daun yang disebabkan oleh hama, seperti bercak daun, layu fusarium, embun tepung, dan virus kuning. Penyakit-penyakit ini tidak hanya memengaruhi kualitas hasil panen, tetapi juga menyebabkan kerugian ekonomi yang signifikan bagi petani. Untuk mengatasi permasalahan ini, dilakukan pengabdian kepada masyarakat dengan mengimplementasikan teknologi Convolutional Neural Network (CNN) untuk klasifikasi penyakit daun cabai secara cepat dan akurat. Metode yang digunakan melibatkan observasi lapangan untuk mengidentifikasi permasalahan yang dihadapi petani di Desa Lubuk Cuik, Batu Bara, Sumatera Utara. Data berupa gambar daun cabai yang terinfeksi dikumpulkan dan digunakan untuk melatih model CNN. Model yang dikembangkan, efficientChiliNet, mampu mengklasifikasikan penyakit daun cabai dengan akurasi pelatihan 99,8% dan akurasi validasi 96,5%. Aplikasi berbasis web dan desktop kemudian dibuat untuk mempermudah petani dalam mendiagnosis penyakit daun cabai secara mandiri. Aplikasi ini juga disosialisasikan kepada petani melalui pelatihan untuk memastikan implementasi teknologi yang optimal. Hasil pengabdian ini menunjukkan bahwa teknologi berbasis CNN mampu memberikan solusi efektif dalam mengidentifikasi penyakit daun cabai dan membantu petani meningkatkan produktivitas pertanian. Rekomendasi selanjutnya adalah pengembangan fitur tambahan dalam aplikasi untuk memberikan panduan penanganan hama dan integrasi teknologi Internet of Things (IoT) untuk pemantauan lingkungan secara real-time. Dengan pendekatan ini, diharapkan terciptanya modernisasi pertanian berbasis smart farming yang berkelanjutan.
Bibliometric analysis of model vehicle routing problem in logistics delivery Zuhanda, Muhammad Khahfi; Hartono, Hartono; Sidik Hasibuan, Samsul Abdul Rahman; Abdullah, Dahlan; Gio, Prana Ugiana; Caraka, Rezzy Eko
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp590-600

Abstract

This bibliometric analysis focuses on the vehicle routing problem (VRP) model in the field of logistics delivery. The study utilizes a comprehensive dataset of 2,000 VRP-related publications obtained from the Scopus database, spanning the years 2007 to 2023. Through the application of bibliometric methods, this research aims to uncover key insights regarding research trends, country contributions, and recent topics within the VRP research network. Various bibliometric indicators, including publication count, author productivity, relevant sources, institutional affiliation, and citation frequency, are employed to conduct the analysis. The findings shed light on the evolution and trajectory of VRP research, while also highlighting noteworthy countries and topics that have received significant attention. This study not only enhances the overall understanding of VRP but also serves as a foundation for future investigations aimed at enhancing the efficiency and effectiveness of logistics delivery.
Inovasi Mesin Batu Bata Merah dan Formulasi Material Ramah Lingkungan Hasibuan, Samsul A Rahman Sidik; Zuhanda, Muhammad Khahfi; Hermanto, Tino
IHSAN : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 2 (2024): Ihsan: Jurnal Pengabdian Masyarakat (Oktober)
Publisher : University of Muhammadiyah Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/ihsan.v6i2.21417

Abstract

Program Pengabdian Kepada Masyarakat ini bertujuan untuk meningkatkan efisiensi dan keberlanjutan industri batu bata merah melalui inovasi teknologi dan material ramah lingkungan. Fokus utama program adalah pengembangan mesin batu bata inovatif dan formulasi material alternatif menggunakan limbah industri. Metode yang digunakan meliputi analisis kebutuhan, pengembangan teknologi, formulasi material, implementasi, dan evaluasi. Hasil menunjukkan bahwa mesin inovatif yang dikembangkan dapat meningkatkan efisiensi produksi, sementara formulasi material menggunakan abu terbang, abu sekam padi, dan abu janjang kelapa sawit menghasilkan batu bata dengan kuat tekan yang lebih tinggi dibandingkan bata konvensional. Program ini berhasil mentransfer teknologi dan pengetahuan kepada mitra UKM, membuka jalan bagi transformasi industri batu bata menuju praktik yang lebih berkelanjutan.
Hybrid Deep Fixed K-Means (HDF-KMeans) Zuhanda, Muhammad Khahfi; Kohsasih, Kelvin Leonardi; Octaviandy, Pieter; Hartono, Hartono; Kurnia, Dian; Tarigan, Nurliana; Ginting, Manan; Hutagalung, Manahan
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.v5i3.913

Abstract

K-Means is one of the most widely used clustering algorithms due to its simplicity, scalability, and computational efficiency. However, its practical application is often hindered by several well-known limitations, such as high sensitivity to initial centroid selection, inconsistency across different runs, and suboptimal performance when dealing with high-dimensional or non-linearly separable data. This study introduces a hybrid clustering algorithm named Hybrid Deep Fixed K-Means (HDF-KMeans) to address these issues. This approach combines the advantages of two state-of-the-art techniques: Deep K-Means++ and Fixed Centered K-Means. Deep K-Means++ leverages deep learning-based feature extraction to transform raw data into more meaningful representations while employing advanced centroid initialization to enhance clustering accuracy and adaptability to complex data structures. Complementarily, Centered K-Means improve the stability of clustering results by locking certain centroids based on domain knowledge or adaptive strategies, effectively reducing variability and convergence inconsistency. Integrating these two methods results in a robust hybrid model that delivers improved accuracy and consistency in clustering performance. The proposed HDF-KMeans algorithm is evaluated using five benchmark medical datasets: Breast Cancer, COVID-19, Diabetes, Heart Disease, and Thyroid. Performance is assessed using standard classification metrics: Accuracy, Precision, Recall, and F1-Score. The results show that HDF-KMeans outperforms traditional K-Means, K-Means++, and K-Means-SMOTE algorithms across all datasets, excelling in overall accuracy and F1 Score. While some trade-offs are observed in specific precision or recall metrics, the model maintains a solid balance, demonstrating reliability. This study highlights HDF-KMeans as a promising and effective solution for complex clustering tasks, particularly in high-stakes domains like healthcare and biomedical analysis.
A Hybrid GDHS and GBDT Approach for Handling Multi-Class Imbalanced Data Classification Hartono, Hartono; Zuhanda, Muhammad Khahfi; Syah, Rahmad; Rahman, Sayuti; Ongko, Erianto
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.
IMPROVING CYBERSECURITY TRAFFIC ANALYSIS VIA ENHANCED K-MEANS CLUSTERING WITH TRIANGLE INEQUALITY-BASED INITIALIZATION Hartono, Hartono; Khahfi Zuhanda, Muhammad; Rahman, Sayuti
Jurnal TIMES Vol 14 No 1 (2025): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51351/jtm.14.1.2025823

Abstract

Clustering algorithms are essential in data mining and pattern recognition for grouping unlabeled data into meaningful clusters based on similarities. Among them, K-Means is widely used due to its simplicity and efficiency but suffers from sensitivity to initial centroid selection and inability to capture feature dependencies. This study proposes an Enhanced Mutual Information-based K-Means (MIK-Means) algorithm combined with Triangle Inequality and Lower Bound (TILB) seeding to improve clustering accuracy and computational efficiency, particularly in the context of network traffic classification for cybersecurity applications. The TILB method accelerates the initialization phase by reducing redundant distance calculations using mathematical pruning techniques, thereby selecting well-distributed initial centroids efficiently. Meanwhile, MIK-Means incorporates mutual information as a similarity measure during clustering assignment, enabling the algorithm to capture complex statistical dependencies among features, which traditional Euclidean distance metrics fail to address. The combination of these two approaches results in a robust clustering framework capable of handling large-scale, high-dimensional, and noisy datasets commonly found in network intrusion detection. The proposed method was evaluated on several benchmark datasets including Darpa 1998-99, KDD Cup99, NSL-KDD, UNSW-NB15, and CAIDA. Comparative experiments with state-of-the-art algorithms such as K-Means++, K-NNDP, and DI-K-Means showed that the proposed approach consistently outperformed or matched competitors in terms of Silhouette Coefficient, Calinski-Harabasz index, and Davies-Bouldin index, indicating better cluster cohesion, separation, and compactness. Additionally, the computational efficiency gained from TILB seeding facilitates faster convergence without compromising clustering quality. Furthermore, a threshold-based cluster labeling mechanism was applied to translate clustering results into practical classifications for detecting attacks versus normal traffic, enhancing the usability of the method in real-world cybersecurity systems. Overall, this research demonstrates that the integration of TILB seeding and mutual information-based clustering provides an effective and efficient solution for network traffic classification challenges.
Pemanfaatan Limbah Organik untuk Pakan Ikan Berbasis Serangga BSF di Desa Marindal II: Utilization of Organic Waste using BSF Insect-Based Fish Feed in Marindal II Village Hartono, Hartono; Zuhanda, Muhammad Khahfi; Aramita, Finta; Suswati, Suswati; Rahman, Sayuti
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 8 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i8.9714

Abstract

This community service activity addresses two main issues in Marindal II Village, Patumbak Subdistrict, Deli Serdang Regency, North Sumatra Province: the high volume of organic waste and the need for fish feed production technology. The partner is a Women Farmers Group that manages chicken farming, goldfish and tilapia cultivation, and a banana plantation. Organic waste, particularly chicken manure, will be used as a medium for cultivating Black Soldier Fly (BSF) larvae, which produce maggots as fish feed. In addition to chicken manure, other waste such as vegetables, fruits, and kitchen scraps are also utilized. However, maggots alone are insufficient to meet the fish's nutritional needs, so an additional feed composition in pellets is required. Pellets are essential to prevent fish from being selective in their diet, thus ensuring their dietary needs are met. The community service team conducted awareness activities on waste utilization and nutritious pellet production for the partner and the community to promote the use of waste and prevent environmental pollution.
Pelatihan Pemanfaatan Gemini AI untuk Mendukung Pembelajaran pada SMA di Sumatera Utara Sukiman, Sukiman; Hendry, Hendry; Zuhanda, Muhammad Khahfi; Fenny, Fenny; Sjukun, Sjukun
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 01 (2024): EDISI MARET 2024
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/prioritas.v6i01.927

Abstract

Pada saat ini ditengah perkembangan teknologi informasi dan komunikasi yang pesat maka setiap individu diharapkan memiliki kemampuan untuk berpikir secara kritis dan sanggup merespon perkembangan zaman. Di sinilah peran teknologi, khususnya kecerdasan buatan (AI), menjadi penting dalam mendukung proses pembelajaran dan meningkatkan daya kritis siswa. Salah satu AI yang populer adalah Gemini AI. Model AI ini dikembangkan oleh Google. Gemini AI dapat memfasilitasi pembelajaran kolaboratif dan diskusi antara siswa. Platform ini memungkinkan siswa untuk bekerja sama dalam proyek, bertukar ide, dan saling memberikan umpan balik. Hal ini dapat meningkatkan kemampuan komunikasi, kerja sama tim, dan toleransi antar budaya. Kegiatan PKM ini dilaksanakan secara luring oleh Tim PKM untuk siswa SMA di Sumatera Utara. Hasil evaluasi post test menunjukkan adanya peningkatan pemahaman siswa terhadap pemanfataan Gemini AI. Selain itu juga respon siswa selama kegiatan PKM juga positif dan tim PKM berharap agar kegiatan ini dapat memberikan manfaat terhadap peningkatan kualitas pembelajaran pada SMA di Sumatera Utara.
Pelatihan Marketing Kreatif Di SMA Harapan Medan Zuhanda, Muhammad Khahfi; Salqaura, Siti Alhamra; Aramita, Finta; Julitawaty, Wily; Jirwanto, Henry
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 01 (2024): EDISI MARET 2024
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/prioritas.v6i01.928

Abstract

Era Revolusi Industri 4.0 membawa perubahan signifikan dalam berbagai aspek kehidupan, termasuk dalam bidang pendidikan. Pendidikan 4.0 merupakan konsep yang menekankan pada pemanfaatan teknologi digital dan internet dalam proses belajar mengajar untuk menghasilkan lulusan yang siap menghadapi tantangan di masa depan. Konsep ini juga menekankan pentingnya keterampilan abad 21 seperti berpikir kritis, kreativitas, kolaborasi, dan komunikasi. SMA Harapan Medan sebagai institusi pendidikan yang berkomitmen untuk mempersiapkan generasi muda yang unggul, memahami pentingnya adaptasi terhadap perubahan ini. Marketing kreatif dalam konteks pendidikan bertujuan untuk membuat proses belajar menjadi lebih menarik dan relevan bagi siswa, sehingga dapat meningkatkan motivasi dan partisipasi mereka. Pendekatan ini melibatkan penggunaan media digital, platform pembelajaran online, serta kampanye kreatif yang dapat menumbuhkan minat dan semangat belajar di kalangan siswa. Melalui program PKM ini, diharapkan para siswa dapat memahami konsep dasar digital marketing, mampu mengelola media sosial secara efektif, dan memiliki kemampuan untuk merancang kampanye promosi digital yang berhasil. Dengan demikian, para siswa akan memiliki keunggulan kompetitif ketika mereka memasuki dunia kerja atau memilih untuk menjadi pengusaha mandiri. Program ini bertujuan untuk memberikan pengetahuan dan keterampilan digital marketing kepada siswa SMA Harapan Medan agar mereka dapat memahami dan mengaplikasikan konsep tersebut dalam meningkatkan promosi dan pemasaran produk atau jasa di masa mendatang.
Financial Behavior and Locus of Control: Key Factors in Understanding Financial Fraud Tendencies Sugiyanto; Zuhanda, M. Khahfi; Jahara, Aini; Abdillah, Azrul; Nabilla, Vivi; Panjaitan, Ema Sari
Jurnal Manajemen dan Keuangan Vol 14 No 2 (2025): Journal Management and Finance
Publisher : Program Studi Manajemen Fakultas Ekonomi Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33059/jmk.v14i2.12620

Abstract

Fraud is a serious challenge and threat faced by various institutions and organizations. Financial reporting fraud is relatively rare but has a greater impact on losses. Financial pressure, opportunities and rationalization drive individuals to engage in fraudulent behavior (fraud). This study aims to determine whether financial behavior and internal locus of control influence the tendency toward financial fraud. A total of 401 village officials were sampled in Deli Serdang Regency and measured using multiple regression analysis. The results showed a negative influence of financial behavior and internal locus of control on financial fraud tendencies, contributing 25.8% simultaneously. The implications of this study are expected for village officials should enhance their internal locus of control and establish healthy management of financial management, through training and workshops.
Co-Authors Abdillah, Azrul Almoataz Youssef Abdelaziz Anil Hakim Syofra Anil Hakim Syofra Syofra Aramita, Finta Asmah Indrawati Dadan Ramdan Dahlan Abdullah Desniarti D Desniarti Desniarti Devy Mathelinea Eliska, Eliska Erianto Ongko Fenny Fenny Ginting, Manan Gio, Prana Ugiana Habib Satria Hartono Hartono Hartono Hartono Hasibuan, Samsul A Rahman Sidik Hendry Hutagalung, Manahan Indri Dayana Jahara, Aini Jaka Windarta Jirwanto, Henry Kelvin Leonardi Kohsasih Kurnia, Dian Maengseok Noh Mardingsih Mardingsih Muhammad Hafiz Muhammad Hafiz Nabilla, Vivi Nanda Novita Nanda Novita, Nanda Noviandri, Dian Opim Salim Sitompul Panjaitan, Ema Sari Pieter Octaviandy, Pieter Prana Ugiana Gio Rahmad B.Y Syah Rahmad Syah, Rahmad Rahman, Sayuti Rezzy Eko Caraka Rizka Fahruza Siregar Rudi Salam Saib Suwilo Salqaura, Siti Alhamra Samsul A Rahman Sidik Hasibuan Sembiring, Arnes Sjukun SUGIYANTO Sukiman Sukiman suswati suswati Syafii . Syah, Rahmad B. Y. Tarigan, Nurliana Tino Hermanto Widiantho, Yuri Wily Julitawaty, Wily Yuan Anisa Yuan Anisa Yuan Anisa Yuan Anisa Anisa