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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika 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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Decision Making System Using Fuzzy Mamdani in Detecting Cholestrol Disease Novita, Nanda; Anisa, Yuan; Zuhanda, Muhammad Khahfi; Eliska, Eliska; Widiantho, Yuri
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12084

Abstract

Cholestrol is a physical property of natural substances in the form of fat which is useful as an essential building material for the body for the synthesis of important substances such as cell membranes and insulation materials around nerve fibers. In addition, it is useful for sex hormones, kidney children and bile acids. High cholesterol levels or hypercholesterolemia in the blood triggers hypertension. This is because high cholesterol is the cause of blockages in peripheral blood vessels that reduce blood supply to the heart. Based on these studies, it shows that the influence of obesity (overweight) and blood pressure has a role in the risk of cholesterol disease. Based on the rule view, it can be seen that the cholesterol level is 210 or it can be said that it is in the alert category, it can be seen for blood pressure 100.6 and BMI 31.8.The fuzzy mamdani method is often also known as the min-max method. Where it uses min or minimum on the implication function and max or maximum on the composition between implication functions. In its application, the madani fuzzy method uses 4 stages, namely the formation of fuzzy sets, application of implication functions, rule composition, defuzzification. This research uses a simulation method of calculating cholesterol. This research analyzes using fuzzy mamdani and matlab software assistance.  
Earthquake detection in mountainous homes using the internet of things connected to photovoltaic energy supply Satria, Habib; Dayana, Indri; Syah, Rahmad B. Y.; Noviandri, Dian; Zuhanda, Muhammad Khahfi; Syafii, Syafii; Salam, Rudi
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp315-322

Abstract

The North Sumatra region is an area with the potential for earthquakes originating from volcanic and oceanic eruptions which have resulted in many fatalities. Therefore, through the application of automatic monitoring and control system technology connected to the internet of things (IoTs), it is the right solution to provide efforts to increase security for residents of the house to always be vigilant. The security enhancement method referred to in this study is a home security system protection system by anticipating earthquakes. The advantage of this tool is that it applies a notification security system method with a sensitivity sensor which is automatically sent via email and sonor buzzer which also acts as sound vibrations due to an earthquake. The test results show that when a vibration occurs, the system will send a short email message to the user's smartphone so that the user will receive an email in the form of a warning message that the state of the house has an earthquake and the light-emitting diode (LED) interrupts and the buzzer is also on so that the alarm sounds which has been integrated into IoT. Then an integrated security monitoring system using the web can be monitored in real time.
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.
Co-Authors Almoataz Youssef Abdelaziz Anil Hakim Syofra Anil Hakim Syofra Syofra Aramita, Finta Asmah Indrawati, Asmah Dadan Ramdan Dahlan Abdullah Dayana, Indri Desniarti D Desniarti Desniarti Devy Mathelinea Eliska, Eliska Erianto Ongko Fenny Fenny Finta Aramita Ginting, Manan Gio, Prana Ugiana Habib Satria Hartono Hartono Hartono Hartono Hasibuan, Samsul A Rahman Sidik Hendry Hendry Henry Jirwanto Hutagalung, Manahan Jaka Windarta Kelvin Leonardi Kohsasih Kurnia, Dian Maengseok Noh Mardingsih Mardingsih Muhammad Hafiz Muhammad Hafiz Nanda Novita Nanda Novita, Nanda Noviandri, Dian Opim Salim Sitompul 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 Sukiman Sukiman suswati suswati Syafii . Syah, Rahmad B. Y. Tarigan, Nurliana Tino Hermanto Widiantho, Yuri Wily Julitawaty Yuan Anisa Yuan Anisa Yuan Anisa Yuan Anisa Anisa