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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal TIMES CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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 Jambura Journal of Electrical and Electronics Engineering 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 JIKEM: Jurnal Ilmu Komputer, Ekonomi dan Manajemen Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) 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 Jurnal Pengabdian Masyarakat (Jubdimas) CompTech : Jurnal Ilmu Komputer dan Teknologi
Rahman, Sayuti
Universitas Medan Area, Medan, Indonesia
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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.
Analisis Pengaruh Indeks Pembangunan Manusia dan Pertumbuhan Ekonomi terhadap Kemiskinan Winanda, Icha; Rahman, Sayuti; Siregar, Rosyidah
JIKEM: Jurnal Ilmu Komputer, Ekonomi dan Manajemen Vol 4 No 2 (2024): JIKEM: Jurnal Ilmu Komputer, Ekonomi dan Manajemen
Publisher : Universitas Muhammadiyah Enrekang

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

Abstract

Kecepatan kendaraan yang tinggi di jalan raya merupakan salah satu penyebab terjadinya kecelakaan lalu lintas dan sangat berpengaruh bagi keamanan pengendara lain dan demi terciptanya keselamatan terhadap pengendara lain, timbul gagasan untuk merancang sebuah sistem yang mampu mengukur dan memantau kecepatan kendaraan yang melewati jalan raya. Saat ini sedang ramai marak teknologi yang dikembangkan dengan tujuan dapat melakukan pendeteksian di jalan raya, teknologi tersebut diharuskan untuk dapat mengetahui kondisi dan situasi yang ada di sekelilingnya, karena di jalan raya melintas berbagai jenis kendaraan yang berbeda. Oleh karena itu dibuatlah program yang dapat mendeteksi kecepatan pada kendaraan yang melintas dijalan raya. Algoritma yangditerapkan pada penelitian ini adalah algoritma YOLO ( You Only Look Once) versi V3. Algoritma tersebut diterapkan karena mampu melakukan klasifikasi kendaraan secara efektif dan efisien. Algoritma tersebut diterapkan karena mampu melakukan klasifikasi kendaraan secara efektif dan efisien. Hasil dari penelitian ini adalah agar system dapat mendeteksi dan mengklasifikasikan kendaraan yang melintas di jalan raya dengan akurasi yang tinggi. Objek yang diklasifikasikan yaitu pada kendaraan mobil, bus, dan truk
Pelatihan Penulisan Karya Ilmiah untuk Mahasiswa Teknik Informatika Sumatera Utara Rahman, Sayuti; Hartono, Hartono; Sembiring, Arnes; Ongko, Erianto; Aulia, Rachmat
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.v6i01.926

Abstract

Penulisan karya ilmiah merupakan salah satu keterampilan penting yang harus dikuasai oleh mahasiswa dalam menyelesaikan studi mereka. Namun, masih banyak mahasiswa yang mengalami kesulitan dalam menghasilkan karya ilmiah yang berkualitas. Oleh karena itu, kegiatan pengabdian masyarakat ini bertujuan untuk memberikan pelatihan penulisan karya ilmiah kepada mahasiswa Teknik Informatika di Sumatera Utara. Kegiatan ini dilaksanakan dengan kerjasama antara lembaga pendidikan tinggi dan organisasi profesi, yaitu Ikatan Profesi Komputer Informatika Nusantara (IKAPKIN). Pelatihan dilakukan melalui platform Zoom dan melibatkan 81 peserta dari berbagai perguruan tinggi di Sumatera Utara. Materi pelatihan mencakup berbagai trik dan tool dalam penulisan karya ilmiah, serta panduan dalam memanfaatkan tool penelitian seperti ChatGPT, DeepL.Com, Grammarly, Quillbot, dan Mendeley. Hasil evaluasi postest menunjukkan adanya peningkatan yang signifikan dalam pemahaman peserta terhadap penggunaan tool penelitian, dengan jumlah peserta yang memahami meningkat secara signifikan. Respon positif terhadap penyampaian materi pelatihan juga tercatat. Diharapkan kegiatan ini dapat memberikan dampak positif dalam meningkatkan kualitas penulisan karya ilmiah mahasiswa Teknik Informatika di Sumatera Utara
Rancang Bangun Pelembab Ruangan Otomatis Dan Monitoring Menggunakan Android Putra, Andre Kurnia; Hasibuan, Ade Zulkarnain; Rahman, Sayuti
Journal of Practical Computer Science Vol. 4 No. 1 (2024): Mei 2024
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v4i1.2951

Abstract

Teknologi otomatisasi sistem kendali dan mikrokontroler dirancang untuk membantu manusia dalam kehidupan sehari - hari sehingga dapat mempermudah rutinitas manusia dalam mengatur kelembaban ruangan menjadi optimal. Kelembapan yang tepat pada ruangan penting untuk dijaga agar terhindar dari iritasi yang disebabkan oleh udara kering, seperti kulit kering, bibir pecah-pecah, pilek, dan sakit tenggorokan. Oleh karena itu, diciptakanlah teknologi otomatis yang dapat mengontrol kelembaban udara di ruangan secara otomatis dengan menggunakan mist maker sebagai alat melembabkan udara di ruangan tertutup. Penelitian ini mengembangkan sistem pelembab ruangan otomatis berbasis mikrokontroler NodeMCU ESP8266. Sensor DHT11 dan sensor water level memiliki fungsi untuk membaca keluaran nilai variabel dari sensor yang ditampilkan melalui remote Arduino IOT ketika ESP8266 sudah terhubung ke internet melalui jaringan WiFi. Jika sensor DHT11 mendeteksi kelembaban udara berada pada rentang 45% - 55%, alat pelembab udara akan otomatis hidup, dan jika kelembaban udara sudah optimal, alat akan otomatis mati.
IMPLEMENTASI METODE RANDOM FOREST DALAM PREDIKSI PENYEBAB TUNGGAKAN PEMBAYARAN PAJAK KENDARAAN BERMOTOR DI SAMSAT MEDAN UTARA Hasibuan, Muhammad Ridwan; Rahman, Sayuti; Chiuloto, Kalvin
CompTech : Jurnal Ilmu Komputer dan Teknologi Vol 1, No 2 (2025): Maret
Publisher : Compart Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63854/comptech.v1i2.28

Abstract

Penelitian ini bertujuan untuk menerapkan metode Random Forest dalam melakukan prediksi penyebab tunggakan pembayaran pajak kendaraan bermotor di Samsat Medan Utara. Metode Random Forest digunakan untuk mengidentifikasi faktor-faktor yang berkontribusi terhadap tunggakan pembayaran pajak kendaraan bermotor, sehingga dapat membantu dalam pengambilan keputusan yang lebih efektif dalam menangani tunggakan tersebut. Data yang digunakan dalam penelitian ini merupakan data historis tentang pembayaran pajak kendaraan bermotor dan faktor-faktor terkait dari Samsat Medan Utara. Proses analisis dimulai dengan tahap pra-pemrosesan data, termasuk pemilihan fitur yang relevan dan penanganan missing data. Selanjutnya, model Random Forest dikembangkan dan dilatih menggunakan data yang ada. Hasil prediksi kemudian dievaluasi menggunakan metrik evaluasi yang tepat. Dengan menerapkan metode Random Forest, penelitian ini dapat memberikan informasi yang berharga tentang faktor-faktor yang berkontribusi terhadap tunggakan pembayaran pajak kendaraan bermotor di Samsat Medan Utara. Hasil prediksi yang akurat dapat membantu pihak berwenang untuk mengidentifikasi faktor-faktor risiko yang berpotensi menyebabkan tunggakan pembayaran pajak dan mengambil tindakan yang sesuai untuk mengurangi jumlah tunggakan tersebut.Penelitian ini diharapkan dapat memberikan kontribusi positif dalam pengelolaan pembayaran pajak kendaraan bermotor di Samsat Medan Utara dan dapat menjadi acuan dalam pengambilan keputusan yang lebih efektif dalam menangani masalah tunggakan pajak kendaraan bermotor.
Teknologi Pengembangan Jaringan Internet Untuk Sekolah di Pedesaan Tengku Mohd Diansyah; Ilham Faisal; Dodi Siregar; Ade Zulkarnain Hasibuan; Sayuti Rahman
JPM: Jurnal Pengabdian Masyarakat Vol. 3 No. 3 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v3i3.413

Abstract

In this community service activity we are developing an internet network that will be used by schools in rural areas, one of the areas in Stabat City in building this internet network we use the ubnt antenna which is reliable in spreading signals in the countryside and our goal is to build an internet network in the village, namely to help the community in obtaining information that is currently very fast and the obstacles that the surrounding community has are very difficult to connect to the internet network after the team pays attention to the problem because of the large number of palm trees that make it very difficult to get a signal in the village and even the school when the school is very fast. The obstacle that the village has is that the signal in the village is not up to 2 bars so that the surrounding community is very difficult to connect to the internet network after the team noticed the problem because of the large number of palm trees which made the signal very difficult to get by local residents and even schools currently have difficulty in the learning process, let alone accessing dapodik owned by the school which must be connected to the internet network.
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 : 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 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
Co-Authors Abdul Malik Adam Adinda Titania Aditya Pratama, Bayu Agung Y S Halawa Alfyanang Fattulah Andi Marwan Elhanafi Ari Usman Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arnes Sembiring Arwadi Sinuraya Asih, Munjiat Setiani Asmah Indrawati Bayu Aditya Pratama Bayu Aditya Pratama Bayu Syah, Rahmad Beby Suryani Fithri Billiam Zealtiel Budi Santoso Budi Santoso Chairul Rizal Chiuloto, Kalvin Citra Rahmadhani Dadan Ramdan Daffa, Daffa Zain Shahriza Dedi Agustriaman Zebua Deseari Baeha Desi Yanti Dodi Siregar Dodi Siregar Emil Fitranshah Aliff S Erianto Ongko Erianto Ongko Eswanto Eswanto Fera Damayanti Finta Aramita Fiqi Arfian Habib Satria Hafifah, Febri Haida Dafitri Haida Dafitri Haida Dafitri, Haida Harahap, Herlina Hartono Hartono Hartono Hasibuan, Ade Zulkarnain Hasibuan, Muhammad Ridwan Herdianto Herdianto Herlina Andriani Simamora Ilham Faisal Ilham Faisal Iqbal Giffari Ritonga Irfandi Irfandi Irwan Irwan Isnaini Kharunnisa Kharunnisa Layla Syalsyadilla Lili Suryati Liza, Risko Lubis, Husni lubis, ihsan M F Verri Anggriawan M. Khahfi Zuhanda Manurung, Dionikxon Marischa Elveny, Marischa Martini, Dewi Marwan Ramli Marwan Ramli Mendarissan Aritonang Muchzakhir Bustari Mufida Khairani Mufida Khairani Muhammad Khahfi Zuhanda Muhammad Rizky Irwansyah Muhammad Zen Muhammad Zen, Muhammad Munadi Munadi Muzdalifah Ulfayani Nasaruddin Nur Hasibuan Nia Ramadani Novalia Aprianti Ginting Olnes Y. Hutajulu Prana Ugi Putra, Andre Kurnia Rachmat Aulia Rachmat Aulia, Rachmat Rafiqi Rahmad B.Y Syah Retna Astuti Kuswardani Riki Agusetiawan Risko Liza Robby Darwis Rudi Salman Sembiring, Arnes Shabila Shaharani Tanjung Siregar, Rosyidah Siti Sundari Sri Eka Riyani Harahap Sultan Shidqi Sumi Khairani Suriati Suriati Suriati Suriati Suriati, Suriati Suswati Suswati Suswati suswati suswati Syarifah Yusnaini Putri Tanjung, Rino Nurcahyo Fauzi Taufik Siregar Tengku Mhd Diansyah Tengku Mohd Diansyah, Tengku Mohd Ulfa Sahira Winanda, Icha Windy Sri Wahyuni Wiraswan Duha Yasir, Amru Yessi Fitri Annisah Lubis Yuni Syahputri