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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal TIMES CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JurTI (JURNAL TEKNOLOGI INFORMASI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Query : Jurnal Sistem Informasi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURIKOM (Jurnal Riset Komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA JPM: JURNAL PENGABDIAN MASYARAKAT International Journal of Engineering, Science and Information Technology Yayasan Cita Cendikiawan Al Khwarizmi Djtechno: Jurnal Teknologi Informasi JIKEM: Jurnal Ilmu Komputer, Ekonomi dan Manajemen INCODING: Journal of Informatics and Computer Science Engineering EXPLORER Prosiding Snastikom Jurnal ABDIMAS Budi Darma Journal of Practical Computer Science (JPCS) Jurnal Informatika Teknologi dan Sains (Jinteks) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Indonesia Sosial Teknologi Jurnal Ilmu Komputer dan Sistem Informasi CompTech : Jurnal Ilmu Komputer dan Teknologi
Rahman, Sayuti
Universitas Medan Area, Medan, Indonesia
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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Rancang Bangun Miniatur Sistem Alat Pengukur Standar Kebisingan Knalpot Sepeda Motor Berbasis Arduino Uno Budi Santoso; Sayuti Rahman; Arnes Sembiring
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 2 No. 1 (2023): Januari 2023
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v2i1.40

Abstract

Sound is one of the vibrations of an object where the vibration occurs around the air that propagates from all directions where the results vary and the air pressure that applies to the surface of the eardrum that hear. Motorcycles are vehicles that are often heard on the road and there are some motorcycles that have some non-standard exhausts used on these motorcycles. Based on this problem, the author designed a miniature motorcycle exhaust noise measuring device system. Using this design uses Arduino Uno as the center of control rangkian LM393 sound sensor to measure the noise level of the sound produced by the exhaust and OLED displaying the sensor value. For the application of this tool, simply close the sensor to the motorcycle exhaust that will be tested, then the sound value produced by the motorcycle will be displayed on the miniature OLED of the exhaust noise standard measuring device, to determine whether the value produced by the user can see the result on the OLED screen.
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.
ConciseCarNet: convolutional neural network for parking space classification Ramli, Marwan; Rahman, Sayuti; Bayu Syah, Rahmad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4158-4168

Abstract

The car is a mode of transportation that brings numerous benefits to the community. As a result, the growth of vehicles is increasing, which has a negative impact. Some of the negative impacts include noise, air pollution, traffic congestion, and the need for parking spaces. Drivers that drive around looking for parking places increase the negative impact as well as boredom and even worry for the driver. Therefore, the driver needs this information on the availability of parking spaces. A convolutional neural network (CNN) using a camera is one of the best methods that can be used to solve this problem. We built a more efficient CNN architecture for classifying parking spaces, which was named ConciseCarNet. ConciseCarNet uses 33 and 11 convolution filters, which cause fewer parameters than previous architectures. ConciseCarNet has two branches, each with a different branch structure. This branch is designed to generate additional feature variations, which will help improve the accuracy. Based on testing, the accuracy of ConciseCarNet2x outperforms the accuracy of mAlexnet, Carnet, EfficientParkingNet, and you look once (YOLO)+MobilNet architectures, which is 99.37%. ConciseCarNet has fewer parameters, file sizes, and floating point operations (FLOPs) compared to other architectures.
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 : LPPM 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.
ANALISIS ALGORITMA KRIPTOGRAFI BACONS CIPHER DAN ALGORITMA KOMPRESI EVEN RODEH RODE UNTUK OPTIMASI KEAMANAN PESAN FILE TEKS Daffa, Daffa Zain Shahriza; Rahman, Sayuti
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 12 No. 1 (2025): Prosisko Vol. 12 No. 1 2025
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/prosisko.v12i1.9289

Abstract

Keamanan dan kerahasiaan sebuah informasi pada file teks merupakan hal yang sangat penting untuk dilakukan, terutama informasi sensitif atau pribadi yang hanya boleh diakses oleh pihak yang berhak saja. Selain aspek keamanan, maka hal yang perlu diperhatikan juga adalah tentang memori penyimpanan. Di era modern yang serba digital saat ini, pertukaran informasi dapat dilakukan secara nirkabel melalui media digital dimana saja dan kapan saja, hal ini mengharuskan pengguna untuk memiliki ruang penyimpanan (storage) yang memadai dan waktu pengiriman yang singkat. Semakin besar file teks yang akan dikirimkan maka semakin lama juga waktu yang dibutuhkan. Oleh karena itu, diperlukan langkah tambahan untuk mengefisiensikan media penyimpanan dengan melakukan kompresi agar ukurannya menjadi lebih kecil. Penelitian ini menggabungkan perpaduan teknik kriptografi Bacons Cipher dan teknik kompresi Even Rodeh Code. Penelitian ini menghasilkan sebuah aplikasi yang dapat dijadikan sebagai alternatif solusi dalam menjaga kerahasiaan file teks sehingga hanya dapat diakses oleh pemilik data dan dapat menghemat kebutuhan akan ruang penyimpanan (storage) data menjadi lebih efisian. Penerapan kompresi Even Rodeh Code juga dapat meningkatkan keamanan dari algoritma Bacons Cipher yang merupakan algoritma kriptografi klasik karena setelah dikompresi akan menghasilkan teks yang lebih acak serta tidak memperlihatkan pola-pola keterhubungannya dengan teks asli.
Meningkatkan Deteksi Email Phising Melalui Pendekatan SVM yang Dioptimalkan NLP Tanjung, Rino Nurcahyo Fauzi; Rahman, Sayuti
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 1 (2025): INCODING APRIL
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i1.831

Abstract

Phishing email attacks are a serious threat in the digital ecosystem because they can trick users into leaking sensitive information or accessing malicious links. This study aims to develop a phishing email classification model based on the Support Vector Machine (SVM) algorithm combined with Natural Language Processing (NLP) techniques to improve detection accuracy. The process begins with the tokenization, text cleansing, and feature extraction stages using the TF-IDF approach, which is further used as input into the classification model. Various SVM kernels, including linear, radial basis function (RBF), and polynomial, are tested through the grid search method with parameter tuning such as C, gamma, and degree. The results showed that SVMs with polynomial kernels produced the highest accuracy of 97.85%, surpassing other algorithms such as Naïve Bayes, Random Forest, and Logistic Regression. These findings indicate that the integration of NLP and SVM with proper parameter tuning provides an effective solution in mitigating phishing email attacks. This model can be the foundation for the development of a more adaptive and efficient cybersecurity system.
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.
MobileChiliNet: convolutional neural network for chili leaves classification Rahman, Sayuti; Elveny, Marischa; Ramli, Marwan; Manurung, Dionikxon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3757-3770

Abstract

Chili pepper (Capsicum annuum) is an important crop in many countries, including Indonesia, which plays an important role in local economy and food production. To meet the high demand, effective agricultural management, especially the diagnosis and treatment of plant diseases, is essential. This study aims to improve the accuracy of chili leaf disease classification while reducing the computational cost so that it can be applied to low-cost smart farming systems. Through the development of the MobileChiliNet architecture, which is the result of pruning and fine-tuning of MobileNetV2, this model achieves the best accuracy, better than other CNNs such as ResNet50 and VGG16. Testing with various optimizers and learning rate schedulers shows that AdamW with PolynomialDecay provides the best performance by increasing the validation accuracy to 96.48%. This approach successfully reduces the computational complexity while maintaining high accuracy, so that it can be implemented in smart farming systems at a lower cost.
Co-Authors Adinda Titania Ady Pratama, Ramadhan Alfyanang Fattulah Andi Marwan Elhanafi Ari Usman Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Asih, Munjiat Setiani Asmah Indrawati Bayu Aditya Pratama Bayu Syah, Rahmad Budi Santoso Budi Santoso Chairul Rizal Chairul Rizal Chiuloto, Kalvin Dadan Ramdan Daffa, Daffa Zain Shahriza Desi Yanti Dodi Siregar Emil Fitranshah Aliff S Erianto Ongko Fera Damayanti Finta Aramita Fiqi Arfian Hafifah, Febri Haida Dafitri Haida Dafitri Haida Dafitri Harahap, Herlina Hartono Hartono Hartono Hartono Hartono Hartono Hasibuan, Ade Zulkarnain Hasibuan, Muhammad Ridwan Herlina Andriani Simamora Ilham Faisal Ilham Faisal Irwan Irwan Khahfi Zuhanda, Muhammad Kharunnisa Kharunnisa Lili Suryati Lubis, Husni lubis, ihsan M F Verri Anggriawan Manurung, Dionikxon Mardiatul Hasanah Marischa Elveny, Marischa Martini, Dewi Marwan Ramli Marwan Ramli Muchzakhir Bustari Mufida Khairani Mufida Khairani Muhammad Khahfi Zuhanda Muhammad Rizky Irwansyah Muhammad Zen Muhammad Zen, Muhammad Munadi Munadi Muzdalifah Ulfayani Putra, Andre Kurnia Rachmat Aulia Rachmat Aulia Rachmat Aulia, Rachmat Rahmad B.Y Syah Rahmad Syah, Rahmad Retna Astuti Kuswardani Risko Liza Robby Darwis Sembiring, Arnes Setyadi, Rahmat Arief Shidqi, Sultan Siregar, Rosyidah Siti Sundari Sri Eka Riyani Harahap Sultan Shidqi Sumi Khairani Suriati Suriati Suriati Suriati Suriati, Suriati Suswati suswati suswati Syah, Rahmad B.Y Tanjung, Rino Nurcahyo Fauzi Tengku Mhd Diansyah Tengku Mohd Diansyah, Tengku Mohd Ulfa Sahira Winanda, Icha Yasir, Amru Yessi Fitri Annisah Lubis Zuhanda, M. Khahfi