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All Journal Jurnal Informatika dan Teknik Elektro Terapan Sinkron : Jurnal dan Penelitian Teknik Informatika Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Nasional Komputasi dan Teknologi Informasi JETL (Journal Of Education, Teaching and Learning) MAJAMATH: Jurnal Matematika dan Pendidikan Matematika Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Pendidikan Teknologi Informasi dan Vokasional JATI (Jurnal Mahasiswa Teknik Informatika) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Sistem Komputer Jurnal Ilmiah Wahana Pendidikan Journal of Software Engineering, Information and Communication Technology Bulletin of Information Technology (BIT) Jurnal Informasi Sains dan Teknologi (ISAINTEK) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Informatika Progres Blend Sains Jurnal Teknik Journal of Education Technology Information Social Sciences and Health Golden Ratio of Data in Summary Jurnal Umum Pengabdian Masyarakat (JUPEMAS) Journal of Informatics and Data Science (J-IDS) Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Ilmiah Sistem Informasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) SAKOLA: Journal of Sains Cooperative Learning and Law
Dedy Kiswanto
Universitas Negeri Medan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Transportation Other

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Developing of Thesis Guidance Learning Media Using The Five Stage Model (MANTAP) in French Language Study Program Zulherman Zulherman; Dedy Kiswanto; Rabiah Adawi
JETL (Journal of Education, Teaching and Learning) Vol 9, No 2 (2024): Volume 9 Number 2 September 2024
Publisher : STKIP Singkawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26737/jetl.v9i2.5244

Abstract

The COVID-19 pandemic has been proven to have a significant impact on changes in various aspects of human life, including education. One group that is specifically affected is final semester students who face challenges in the thesis preparation process due to the large-scale social restriction (PSBB) policy in Indonesia. With limited social interaction and face- to-face learning, the thesis guidance process becomes more complex, increasing the psychological and academic burden for students. including the thesis supervision process for French Language Students at Medan State University. As an alternative solution, research was carried out by examining the use of GitHub as a cloud storage medium and Telegram as a communication platform to speed up the thesis guidance process, where when students make corrective changes to the thesis files contained in Github, the lecturer will automatically get the information via notification on Telegram. This research uses  the MANTAP model in system development, where the results of each stage will become input for the next stage so that the process of improving each other occurs. The test results of the real-time notification system show that the notification messages to lecturers and students are completely accurate, namely 100%, meaning that every change made to the thesis file on Github will be notified automatically by Telegram. Apart from that, from measuring the system response time with 2 scenarios, it was found that the average response time was very good based on QoS (Quality of Service) standards, namely 0.47 ms for scenario 1 and 0.82 ms for scenario 2, this difference is the average response time for scenario 1 and scenario 2 This occurs due to differences in internet speed for each collabator which has an impact on differences in response time obtained.
Implementation of IoT and Machine Learning for Monitoring and Prediction of Tank Water Levels Rizky Wahyudi; Dedy Kiswanto; Windy Aulia; Selfi Audy Priscilia
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1936

Abstract

The availability and quality of clean water in household storage tanks are essential yet often overlooked until problems such as depletion or contamination occur. Manual monitoring methods that rely on physical inspection tend to be inefficient, prone to delay, and unable to support predictive decision-making. This study proposes an automated monitoring solution by integrating Internet of Things (IoT) technology with Machine Learning-based analysis. The system is developed using an ESP32 microcontroller that continuously collects real-time data from an ultrasonic sensor to measure water level and a turbidity sensor to assess water clarity. The time-series data obtained is then analyzed using two algorithmic approaches. Linear Regression is employed to model the water depletion rate and generate predictions regarding the estimated remaining duration before the tank reaches an empty state. In parallel, Random Forest is applied as a comparative model to validate prediction accuracy under non-linear consumption patterns. Experimental results demonstrate that the combined IoT–Machine Learning framework provides accurate, timely, and informative insights for users. The proposed system improves water usage efficiency and strengthens early warning capabilities, making it a practical solution for supporting effective household water management.
Smart Safety Room: ESP32 Decision Tree-Based Multi-Hazard Detection System Jogi Purba; Dedy Kiswanto; John Bush Henrydunan; Revidamurti Dly
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1947

Abstract

Physical space security and safety remain fundamental challenges in various sectors, ranging from residential buildings to critical server rooms. Conventional security systems often rely on single sensors or passive alarms that cannot respond comprehensively to multiple simultaneous threats. This research proposes a Smart Safety Room, an ESP32-based integrated multi-sensor security system that combines gas sensors (MQ-2), fire sensors (flame sensors), PIR sensors, and visual-audio output components including OLED displays, RGB LEDs, and buzzers. The system implements a decision tree algorithm with hierarchical priorities to classify room conditions into three categories: SAFE, ALERT, and DANGER based on a combination of sensor data. Testing was conducted through four main scenarios: normal conditions, fire detection, intrusion detection, and dual threat conditions. The results show that the system achieved an overall accuracy of 96.5% with detailed performance of 96% for the fire sensor, 94% for the gas sensor, and 98% for the PIR sensor. The average response time was under 300 milliseconds for all types of detection, meeting the real-time system requirements. The decision tree showed excellent classification performance with an F1-score ranging from 95-97% for all categories. The web-based real-time monitoring dashboard successfully displayed sensor status with auto-refresh every 1 second and a data loss rate of only 0.8% during continuous operation.
Implementasi Zero Trust Architecture pada Sistem Pengaduan Masyarakat Berbasis Web untuk Meningkatkan Keamanan Data dan Akses Pengguna Dedy Kiswanto; Gerhard Hasangapon Parapat
SAKOLA: Journal of Sains Cooperative Learning and Law Vol. 3 No. 1 (2026): April 2026
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/sakola.v3i1.8262

Abstract

Sistem pengaduan masyarakat berbasis web memiliki peran penting dalam meningkatkan kualitas pelayanan publik serta transparansi informasi antara masyarakat dan pemerintah. Namun, permasalahan keamanan data dan akses pengguna masih menjadi tantangan utama, terutama terkait potensi kebocoran data dan penyalahgunaan akses. Oleh karena itu, penerapan konsep Zero Trust Architecture (ZTA) menjadi solusi untuk meningkatkan keamanan sistem melalui mekanisme verifikasi yang ketat. Penelitian ini bertujuan untuk mengimplementasikan dan menganalisis penerapan Zero Trust Architecture pada sistem pengaduan masyarakat berbasis web guna meningkatkan keamanan data dan kontrol akses pengguna. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan tahapan analisis kebutuhan, perancangan arsitektur, implementasi sistem, dan pengujian keamanan. Sistem dikembangkan dengan fitur utama seperti registrasi, login dengan verifikasi OTP, pengaduan masyarakat, serta kritik dan saran, yang dilengkapi dengan mekanisme autentikasi dan otorisasi berbasis peran. Hasil penelitian menunjukkan bahwa penerapan Zero Trust Architecture mampu meningkatkan keamanan sistem melalui validasi berlapis, pembatasan akses, serta perlindungan data pengguna. Temuan ini diharapkan dapat menjadi solusi dalam pengembangan sistem pelayanan publik yang lebih aman, andal, dan terpercaya.
Pengembangan Sistem Otomatisasi Pakan Ikan dan Monitoring Kualitas Lingkungan Berbasis IoT dan Machine Learning untuk Budidaya Ikan Berbasis Web Muhammad Alfin; Dedy Kiswanto; Muhammad Budi Akbar; Najwa Latifah Hasibuan

Publisher :

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10246

Abstract

Abstrak - Pemberian pakan yang tidak efisien dan kurangnya pemantauan kondisi lingkungan merupakan tantangan utama dalam budidaya ikan tradisional, yang berdampak pada peningkatan biaya operasional dan penurunan produktivitas. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Otomatisasi Pakan dan Monitoring Kualitas Lingkungan Budidaya Ikan berbasis Internet of Things (IoT) dan Machine Learning (ML) sederhana. Sistem ini menggunakan mikrokontroler ESP32 sebagai pusat kendali untuk membaca data sensor suhu dan menggerakkan servo motor sebagai mekanisme feeder pakan otomatis. Data sensor lingkungan dan parameter ikan (jumlah dan umur) dikirim ke Flask API yang berfungsi sebagai jembatan komunikasi dan pengolah data. Di sisi server, Flask API mengaplikasikan model Regresi Sederhana untuk mengestimasi kebutuhan pakan harian secara adaptif. Hasil estimasi kemudian dikirimkan kembali ke ESP32 untuk eksekusi pemberian pakan. Seluruh proses monitoring dan input parameter dilakukan melalui Dashboard Web berbasis PHP. Hasil pengujian menunjukkan bahwa sistem mampu melakukan pemantauan suhu secara real-time dan melaksanakan mekanisme pemberian pakan secara akurat sesuai hasil perhitungan ML. Integrasi yang efisien antara IoT, API, dan model ML ini diharapkan dapat mengoptimalkan manajemen pakan, mengurangi limbah, dan mendukung praktik akuakultur yang lebih berkelanjutan.Kata kunci : Internet of Things (IoT); Machine Learning; ESP32; Servo Motor; Pakan Otomatis; Budidaya Ikan; Abstract - Inefficient feeding practices and the lack of environmental condition monitoring are major challenges in traditional aquaculture, leading to increased operational costs and reduced productivity. This study aims to design and implement an Automated Feeding and Environmental Quality Monitoring System for fish cultivation based on the Internet of Things (IoT) and simple Machine Learning (ML). The system uses an ESP32 microcontroller as the central controller to read temperature sensor data and operate a servo motor as the automatic feeding mechanism. Environmental sensor data and fish parameters (quantity and age) are transmitted to a Flask API, which functions as a communication bridge and data processor. On the server side, the Flask API applies a Simple Regression model to estimate daily feed requirements adaptively. The estimation results are then sent back to the ESP32 for feed dispensing execution. All monitoring processes and parameter inputs are conducted through a PHP-based web dashboard. Experimental results show that the system is capable of performing real-time temperature monitoring and executing accurate feeding mechanisms according to the ML calculations. The efficient integration of IoT, API, and ML models is expected to optimize feed management, reduce waste, and support more sustainable aquaculture practices.Keywords: Internet of Things (IoT); Machine Learning; ESP32; Servo Motor; Automatic Feeding; Aquaculture;
Sistem Keamanan Pintu Berbasis Computer Vision dengan Biometric Face Recognition dan Physical Tampering Detection Felix John Pardamean Hutabarat; Dedy Kiswanto; Paskah Abadi Simanullang; Fadilla Amanah

Publisher :

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10270

Abstract

Abstrak - Keamanan akses pintu pada lingkungan hunian dan kos membutuhkan sistem yang tidak hanya mampu memverifikasi identitas pengguna, tetapi juga responsif terhadap ancaman fisik terhadap perangkat. Penelitian ini mengusulkan dan merealisasikan S.I.G.H.T. (Secure Intelligent Gate Hardware Tamper-detection), sebuah sistem keamanan pintu berbasis computer vision yang menggabungkan biometric face recognition menggunakan algoritma Local Binary Patterns Histograms (LBPH) dengan physical tampering detection berbasis sensor getaran. Arsitektur sistem terdiri atas backend FastAPI, dashboard web berbasis React sebagai pusat pemantauan dan kontrol, agen kamera untuk pemrosesan citra pada perangkat edge, serta modul IoT gerbang yang mengendalikan kunci dan alarm secara real-time melalui antrian perintah terpusat. Metode pengembangan yang digunakan adalah pendekatan Research and Development (RD) dengan model Waterfall yang mencakup analisis kebutuhan, perancangan, implementasi, dan pengujian terstruktur. Validasi fungsional dilakukan menggunakan black box testing pada skenario utama, seperti otentikasi wajah, pengelolaan data penghuni, respons sensor getaran, dan kontrol aktuator pintu serta alarm. Hasil pengujian menunjukkan seluruh skenario berjalan sesuai harapan dengan status “Lulus”, sehingga S.I.G.H.T. dinilai layak sebagai prototipe solusi keamanan pintu berlapis yang adaptif dan berpotensi dikembangkan lebih lanjut pada skala implementasi yang lebih luas.Kata kunci : Computer vision; Deteksi tampering fisik; Internet of Things; LBPH; Sistem keamanan pintu; Abstract - Door access security in residential and boarding environments requires a system that not only verifies user identity, but also responds to physical threats directed at the device. This study proposes and implements S.I.G.H.T. (Secure Intelligent Gate Hardware Tamper-detection), a computer-vision-based door security system that combines biometric face recognition using the Local Binary Patterns Histograms (LBPH) algorithm with physical tampering detection using a vibration sensor. The system architecture consists of a FastAPI backend, a React-based web dashboard as the central monitoring and control interface, a camera agent for image processing on edge devices, and an IoT gate module that controls the lock and alarm in real time through a centralized command queue. The development process follows a Research and Development (RD) approach with the Waterfall model, covering requirements analysis, system design, implementation, and structured testing stages. Functional validation is carried out using black box testing on key scenarios such as face authentication, resident data management, vibration sensor response, and actuator control for the door and alarm. The results show that all scenarios meet the expected outcomes with a “Pass” status, indicating that S.I.G.H.T. is feasible as a layered and adaptive door security prototype that can be further extended to broader deployment contexts.Keywords: Computer vision; Door security system; Internet of Things; LBPH; Physical tampering detection;
Rancang Bangun Sistem Penyortiran Kualitas Buah Tomat Berbasis IoT dan Computer Vision(YOLOv8) Menggunakan Modul ESP32-CAM Alfin Syahri; Dedy Kiswanto; Albert Ramadhan Manik; Yuda Advis Ambrosius Sitohang

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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10267

Abstract

Abstrak - Penyortiran kualitas tomat masih banyak dilakukan secara manual sehingga prosesnya lambat, membutuhkan tenaga kerja besar, dan sering menghasilkan penilaian yang tidak konsisten. Penelitian ini bertujuan merancang sistem penyortiran tomat otomatis berbasis Internet of Things (IoT) dan Computer Vision menggunakan ESP32-CAM dan model YOLOv8. Sistem terdiri dari ESP32 DevKit untuk membaca sensor ultrasonik dan mengendalikan servo, ESP32-CAM untuk mengambil citra, serta server Flask yang memproses gambar menggunakan YOLOv8. Dataset diperoleh dari Roboflow dan dilatih melalui proses augmentasi dan preprocessing sehingga model dapat mengklasifikasikan tomat menjadi tiga kategori, yaitu matang, mentah, dan busuk. Hasil pengujian menunjukkan bahwa sensor ultrasonik mampu mendeteksi tomat secara stabil pada jarak 10 cm, ESP32-CAM berhasil mengirim gambar ke server, dan servo dapat menyortir tomat sesuai hasil prediksi. Sistem web monitoring yang dibangun mampu menampilkan prediksi terbaru, grafik statistik, serta riwayat prediksi harian secara real-time. Beberapa kendala ditemukan, seperti kesulitan model membedakan tomat merah busuk dan tomat matang yang memiliki kemiripan visual, serta motor conveyor yang kurang kuat pada kecepatan rendah. Secara keseluruhan, sistem berhasil berfungsi sebagai prototipe penyortiran tomat otomatis yang terintegrasi dan dapat dikembangkan lebih lanjut untuk penggunaan skala industri.Kata kunci : IoT; ESP32-CAM; YOLOv8; Penyortiran Tomat; Computer Vision; Abstract - Manual tomato quality sorting is still widely practiced, resulting in slow processing, high labor requirements, and inconsistent assessment outcomes. This study aims to design an automatic tomato sorting system based on the Internet of Things (IoT) and Computer Vision using ESP32-CAM and the YOLOv8 model. The system consists of an ESP32 DevKit to read ultrasonic sensor data and control servo motors, an ESP32-CAM to capture tomato images, and a Flask server to process images using the YOLOv8 model. The dataset was obtained from Roboflow and trained through augmentation and preprocessing processes to enable the model to classify tomatoes into three categories: ripe, unripe, and rotten. Experimental results show that the ultrasonic sensor can stably detect tomatoes at a distance of 10 cm, the ESP32-CAM successfully transmits images to the server, and the servo motor can sort tomatoes according to the prediction results. The developed web-based monitoring system is capable of displaying real-time predictions, statistical graphs, and daily prediction history. Several limitations were identified, including the model’s difficulty in distinguishing between rotten red tomatoes and ripe tomatoes due to visual similarity, as well as insufficient conveyor motor strength at low speeds. Overall, the proposed system functions effectively as an integrated automatic tomato sorting prototype and can be further developed for industrial-scale applications.Keywords: IoT; ESP32-CAM; YOLOv8; Tomato Sorting; Computer Vision;
Sistem Keamanan Pintu Otomatis Berbasis Face Recognition dan Machine Learning Menggunakan ESP32-CAM dengan Web Dashboard Nazwar Farezi; Dedy Kiswanto; Muhammad Alby Savana Hasibuan; Ega Pratama

Publisher :

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10273

Abstract

Abstrak - Penelitian ini membahas pembuatan dan penerapan sistem keamanan pintu otomatis yang menggunakan teknologi pengenalan wajah. Sistem ini memanfaatkan modul ESP32-CAM sebagai alat utama untuk mengambil gambar dan melakukan pemrosesan awal. Selain itu, sistem juga menggabungkan machine learning untuk proses mengenali wajah dan menggunakan dashboard web sebagai alat untuk pemantauan serta pengelolaan pengguna secara langsung. Data wajah dikumpulkan melalui proses registrasi langsung dengan kamera, kemudian diolah melalui beberapa tahap pra-pemrosesan dan ekstraksi ciri sebelum digunakan untuk membandingkan dan mengenali identitas pengguna. Hasil pengujian menunjukkan bahwa sistem bisa melakukan autentikasi secara otomatis, membuka pintu saat wajah pengguna terdeteksi, serta mencatat seluruh aktivitas akses ke dalam dashboard. Uji coba dilakukan dalam berbagai kondisi cahaya dan jarak, dan sistem menunjukkan kinerja yang stabil pada situasi normal. Dashboard yang dibuat juga mampu menampilkan kondisi perangkat, riwayat akses, serta informasi pengguna secara akurat. Dari hasil tersebut, sistem ini dianggap bisa memberikan solusi keamanan rumah yang lebih praktis, efisien, dan mudah dikelola dibandingkan metode tradisional.Kata kunci: Face Recognition; ESP32-CAM; Sistem Keamanan Pintu Otomatis; Machine Learning; Internet of Things (IoT); Abstract - This research discusses the development and implementation of an automatic door security system using facial recognition technology. This system utilizes an ESP32-CAM module as the primary tool for image capture and initial transmission. Furthermore, the system incorporates machine learning for facial recognition and utilizes a web dashboard for real-time user monitoring and management. Facial data is collected through a direct registration process with a camera, then processed through several stages of pre-processing and feature extraction before being used to recognize and identify users. Test results show that the system can perform automatic authentication, open the door upon face detection, and record all access activity in the dashboard. Tests were conducted under various lighting conditions and distances, and the system demonstrated stable performance under normal conditions. The dashboard is also capable of accurately displaying device status, access history, and user information. These results suggest that this system can provide a more practical, efficient, and easier-to-manage home security solution than traditional methods.Keywords: Facial Recognition; ESP32-CAM; Automatic Door Security System; Machine Learning; Internet of Things (IoT); Web Dashboard;
Pemeliharaan dan Penyiraman Tanaman Pot Berbasis IoT dan Machine Learning dengan Rekomendasi Perawatan Tanaman Muhammad Hidayatul Arifin; Dedy Kiswanto; Muhammad Raffi Akbar Tanjung; Ririn Amelia Br Siregar

Publisher :

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10275

Abstract

Abstrak - Metode irigasi konvensional sering kali memiliki keterbatasan dalam merespons dinamika mikroklimat, yang berpotensi mengganggu pertumbuhan tanaman. Penelitian ini bertujuan merancang sistem pemeliharaan tanaman otonom yang memadukan teknologi Internet of Things (IoT) dengan Hybrid Artificial Intelligence dalam kerangka arsitektur server terpusat (VPS). Berbeda dengan sistem berbasis penjadwalan waktu (timer), solusi ini menerapkan algoritma Decision Tree untuk menghasilkan keputusan adaptif yang didasarkan pada pembacaan sensor secara real-time. Pendekatan metodologi mencakup pembangkitan 3.000 data latih sintetis berdasarkan logika pakar guna melatih model dalam menangani berbagai skenario kompleks, termasuk kondisi kekeringan ekstrem dan anomali pencahayaan. Hasil evaluasi komputasional menunjukkan kinerja sistem yang presisi, dengan akurasi diagnosis mencapai 100% dan rata-rata kesalahan prediksi volume air (Mean Absolute Error) hanya sebesar 0,66 ml. Lebih lanjut, pengujian fungsional pada perangkat keras memvalidasi stabilitas dan responsivitas sistem dalam menjalankan logika kendali cerdas, seperti penundaan penyiraman saat suhu tinggi (Smart Delay) dan penguncian pompa otomatis pada malam hari (Night Mode). Studi ini menyimpulkan bahwa integrasi AI dalam arsitektur IoT terpusat efektif untuk mewujudkan sistem manajemen tanaman yang presisi, adaptif terhadap lingkungan, dan handal untuk implementasi praktis.Kata kunci : Internet of Things; Machine Learning; Perawatan Tanaman; Smart Farming; Irigrasi Tanaman; Abstract - Conventional irrigation methods often have limitations in responding to microclimate dynamics, potentially disrupting plant growth. This study aims to design an autonomous plant maintenance system that combines Internet of Things (IoT) technology with Hybrid Artificial Intelligence within a centralized server (VPS) architecture. Unlike timer-based systems, this solution applies a Decision Tree algorithm to generate adaptive decisions based on real-time sensor readings. The methodological approach includes generating 3,000 synthetic training data sets based on expert logic to train the model to handle various complex scenarios, including extreme drought conditions and lighting anomalies. Computational evaluation results demonstrate precise system performance, with a diagnostic accuracy reaching 100% and an average water volume prediction error (Mean Absolute Error) of only 0.66 ml. Furthermore, functional testing on hardware validates the system's stability and responsiveness in executing intelligent control logic, such as delaying watering during high temperatures (Smart Delay) and automatic pump locking at night (Night Mode). This study concludes that the integration of AI within a centralized IoT architecture is effective in realizing a precise, environmentally adaptive, and reliable plant management system for practical implementation.Keywords: Internet of Things; Machine Learning; Plant Care; Smart Farming; Plant Irrigation;
PERBANDINGAN ALGORITMA RANDOM FOREST DAN KNN UNTUK DETEKSI SERANGAN DDOS SECARA REAL-TIME PADA JARINGAN LOKAL YUSRIDA JELIANTI SIHITE SIHITE; Dedy Kiswanto; Muhammad Rois Lukman Damanik
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3207

Abstract

Distributed Denial of Service (DDoS) attacks are a cyber security threat capable of massively disrupting network services within a short period of time. Static rule-based detection methods have proven inadequate in the face of constantly evolving attack patterns, making machine learning approaches a more adaptive alternative. This study compares the performance of the Random Forest (RF) and K-Nearest Neighbour (KNN) algorithms in detecting DDoS attacks in real-time on a local network. The local network topology was physically constructed using a Router 1941, a Switch, an Attacker PC, and a Normal PC, whilst model training utilised the CICIDS2017 dataset comprising 225,711 samples with 78 features via Google Colab. The system was built entirely using Python, with Scapy as the packet sniffing engine and Streamlit as the interactive web dashboard framework, allowing detection results to be monitored simultaneously via both the command-line interface (CLI) and a browser-based visual display. Experimental results show that RF achieved an accuracy of 99.99% with a prediction time of 0.74 seconds and only 2 misclassifications, whilst KNN achieved an accuracy of 99.96% with a prediction time of 87.33 seconds and 14 misclassifications. In real-time latency testing, RF recorded 40.027 ms and KNN 34.678 ms. RF is recommended as the primary algorithm for DDoS detection systems on local networks.
Co-Authors Abdi Azzaki G, Fikri Abid Syuja, Muhammad Adidtya Perdana, Adidtya Adventino Gulo, Steven Afiq Alghazali Lubis Afrrahman S. Effendi, Ali Agi Berutu, Iwan Ahmad Fahrezi, Bryan Ahmad Zulfan Hafiz Harahap Akbar, Muhammad Budi Al-Kautsar, Muhammad Zidane Albert Ramadhan Manik Alfin Syahri Alvansyah, Oka Alvin Hafiz Ananda Irya Shakila Syukron Andreas Sinabariba, Ade Anggraini Yolandari, Nezza Aqilah Defiyanti Ardani Achmad Arifin, Muhammad Hidayatul Arion Pardede Ashillah, Salma Asro Harahap, Fatimah Audy Priscilia, Selfi Aulia Artika, Delvita Aulia, Windy Auzi, Sybil Azima Lubis, Fauzan Azis, Zainal Azril Arfansyah Azzahra, Dita Putri Bicanro Gebriyan Panjaitan Bob Valentino Bonifasius Simbolon, Aldo Br Hutagalung, Fhadillah Citra Hasiana Rajagukguk, Gloria Davina, Sherly Dealva Arsyad, Thania Defi, Aqilah Dewi Lestari Dly, Revidamurti Drilanang, Mhd Ilyasyah Dwi Febrianti, Bunga Ega Pratama Evanthe, Hansel Evanthe, Hansel Valent Fadilla Amanah Fahra Pebiana Putri Fauzan Azima Lubis Felix John Pardamean Hutabarat Fhadillah Br Hutagalung Fitra, Muhammad Rizki Andrian Gaol, Anwar Shaleh Lbn Gerhard Hasangapon Parapat Gloria Citra Hasiana Rajagukguk Gulo, Steven Adventino Hafika, Rizky Ananda Hafiz, Alvin Hafizh Ariiq Hanafiah Hanafiah Harahap, Fatima Asro Harahap, Salsa Nabila Heppy Ria Sibarani, Ronasip Hermawan Syahputra Hidayat, M Fauzan Human Sukma, Ayman Hutagalung, Fhadillah Br Ichwanul Muslim Karo Karo Idris Putra Hatoguan Insan Pratama Siagian, Raihan Iwan Agi Berutu Jehian, Neysa Talitha Jibran Muzakki Khan, Adhevta Jogi Purba John Bush Henrydunan Josua Pinem Juliana Silalahi, Feby Khildan Rifail Azis Khoiriah, Najwatul Kristin Impana Manik Latifah Hasibuan, Najwa Lubis, Ardilla Syahfitri Lubis, Fauzan Azima M.Pd., Zulherman Malau, Mei Lammi Manik, Albert Ramadhan Manik, Kristin Impana Meliala, Ruth Amelia Vega S Melly Br Bangun Mhd Ilyasyah Drilanang Muhammad Agus Syaputra Lubis Muhammad Alby Savana Hasibuan Muhammad Alfin Muhammad Budi Akbar Muhammad Dzaki Arjun Muhammad Hidayatul Arifin Muhammad Iqbal Fahrezzi Muhammad Naufal Musyaafa Muhammad Raffi Akbar Tanjung Muhammad Rois Lukman Damanik Muhammad Zidane Al-Kautsar Muslim Sinaga, Rizal Nababan, Sirus Daniel Nababan, Sirus Daniel Haholongan Nadrah Afiati Nasution Najwa Latifah Hasibuan Nasution, Adzkia Nasution, Afifah Naila Nasution, Aurela Khoiri Nasution, Siti Ananda Nazwar Farezi Nezza Anggraini Yolandari Noor, Muhammad Yazid Nurul Maulida Surbakti Panggabean, Suvriadi Parapat, Gerhard Hasangapon Paskah Abadi Simanullang Pebiana Putri, Fahra Peter Tymothy Hutabarat Prana Walidin, Adamsyach Pritiy Singgam Purba, Jogi Putra Paskah Halawa, Sovantri Putri Handayani Simbolon, Agata Putri Syaifullah, Sarah Putri, Fahra Pebiana Putri, Rezkya Nadilla Rabiah Adawi Raffi Akbar Tanjung, Muhammad Raja Ansel Hartama Sihombing Ramadhani, Fanny Rangga Wahyu Pratama Rani Indah Sari Revidamurti Dly Ridho Affandi Rifail Azis, Khildan Ririn Amelia Br Siregar Riyan Wardhana Rizal Muslim Sinaga Rizki Andrian Fitra, Muhammad Rizky Ananda Hafika Rizky Wahyudi Safitri, Eli Safrida Napitupulu Sapta Warman Zai, Tri Selfi Audy Priscilia Sembiring, Febe Gracia Shaleh Lbn Gaol, Anwar Shaqila Rahmayani Siagian, Raihan Insan Pratama Sianipar, Freyro Dobry silalahi, evelyn keisha Silalahi, Feby Juliana Siregar, Dean Sitanggang, Yoseph Christian Sitepu, Ahmad Denil Sitepu, Keysa Shifa Adwitia Siti Mamduhah siti wulandari Situmorang, Romatua SM Sidabutar, Yusiva Sovantri Putra Paskah Halawa Sri Dewi Stefen Agus Waruwu Sukma, Ayman Human Suryaningsih, Embun Suvriadi Panggabean Syti Salwaa Nafiisah Syukron, Ananda Irya Shakila Talitha Jehian, Neysa Tegas Ramadhan Tri Sapta Warman Zai Tua Halomoan Harahap Tua Halomoan Harahap, Tua Halomoan Vega S. Meliala, Ruth Amelia Vincentius Manurung, Enriko Vivielda Farmawaty Tambunan Waruwu, Stefen Agus Windy Aulia Yesy Simanjuntak Yohana Lorinez S. Yohanes Gerardus Haga Zai Yuda Advis Ambrosius Sitohang YUSRIDA JELIANTI SIHITE SIHITE Zahira Putri Julia Daulay Zainal Azis Zidane Al-Kautsar, Muhammad Zulfahrizan, Atta Zulfi, M. Fikri