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All Journal Jurnal Informatika dan Teknik Elektro Terapan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Komputasi dan Teknologi Informasi JETL (Journal Of Education, Teaching and Learning) MAJAMATH: Jurnal Matematika dan Pendidikan Matematika 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) 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 Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Dedy Kiswanto
Universitas Negeri Medan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering 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 Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Transportation Other

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Sistem Monitoring Kualitas Udara dan Peringatan Dini Berbasis IoT dengan Prediksi Polusi Menggunakan Random Forest Regression Syukron, Ananda Irya Shakila; Kiswanto, Dedy; Hafiz, Alvin; Harahap, Salsa Nabila
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10243

Abstract

Abstrak - Pencemaran udara merupakan ancaman serius terhadap kesehatan masyarakat dan kualitas lingkungan, sehingga diperlukan sistem pemantauan yang responsif, andal, dan proaktif. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem monitoring kualitas udara dan peringatan dini berbasis Internet of Things (IoT) dengan integrasi machine learning untuk prediksi tingkat polusi secara real-time. Metode penelitian mencakup pengembangan perangkat keras berbasis ESP32 yang terhubung dengan sensor DHT11 (suhu dan kelembaban) serta MQ-135 (konsentrasi gas), pengiriman data ke server VPS melalui protokol HTTP, pembuatan dashboard web untuk visualisasi dan notifikasi, serta pembangunan model prediksi menggunakan Random Forest Regression. Dataset dikumpulkan secara real-time dengan total 38.733 entri, kemudian diproses melalui preprocessing (pembersihan, imputasi missing value, normalisasi Min-Max) dan dibagi dengan rasio 80:20 untuk pelatihan dan pengujian. Hasil evaluasi menunjukkan bahwa model memiliki performa sangat baik dengan R² Score 0,8829 (latih) dan 0,8852 (uji), MAE 2,98, serta RMSE 5,13 pada data uji mengonfirmasi akurasi tinggi dan minimnya risiko overfitting. Analisis feature importance mengungkapkan bahwa konsentrasi gas (PPM) merupakan variabel paling dominan dalam prediksi AQI (skor 0,8497). Sistem peringatan dini juga terbukti efektif saat AQI melebihi ambang batas (misalnya ≥151), indikator LED berubah merah, alarm suara aktif berulang, dan insiden secara otomatis tercatat dalam log digital untuk manajemen respons yang terukur. Secara keseluruhan, integrasi IoT dan Random Forest Regression menghasilkan sistem monitoring yang tidak hanya informatif dan andal, tetapi juga proaktif dalam mitigasi risiko kesehatan akibat polusi udara.Kata kunci : IoT; Random Forest Regression; Kualitas Udara; Sistem Peringatan Dini; Abstract - Air pollution poses a serious threat to public health and environmental quality, requiring a responsive, reliable, and proactive monitoring system. This study aims to design and implement an Internet of Things (IoT)-based air quality monitoring and early warning system with machine learning integration for real-time pollution level prediction. The research methods included developing ESP32-based hardware connected to DHT11 (temperature and humidity) and MQ-135 (gas concentration) sensors, sending data to a VPS server via the HTTP protocol, creating a web dashboard for visualization and notification, and building a prediction model using Random Forest Regression. The dataset was collected in real-time with a total of 38,733 entries, then processed through preprocessing (cleaning, missing value imputation, Min-Max normalization) and divided with a ratio of 80:20 for training and testing. The evaluation results show that the model performs very well with an R² Score of 0.8829 (training) and 0.8852 (testing), MAE of 2.98, and RMSE of 5.13 on the test data, confirming high accuracy and minimal risk of overfitting. Feature importance analysis revealed that gas concentration (PPM) was the most dominant variable in AQI prediction (score 0.8497). The early warning system also proved effective when the AQI exceeded the threshold (e.g., ≥151), with the LED indicator turning red, the audible alarm sounding repeatedly, and the incident being automatically recorded in a digital log for measurable response management. Overall, the integration of IoT and Random Forest Regression resulted in a monitoring system that is not only informative and reliable but also proactive in mitigating health risks due to air pollution.Keywords: IoT; Random Forest Regression; Air Quality Monitoring; Early Warning System;
Integrasi Sensor Ultrasonik dan Computer Vision (YOLO) Berbasis ESP32-CAM untuk Klasifikasi Objek pada Sistem Parkir Manik, Kristin Impana; Kiswanto, Dedy; Defi, Aqilah; Gaol, Anwar Shaleh Lbn
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10263

Abstract

Abstrak - Penelitian ini mengembangkan sistem smart parking berbasis Internet of Things (IoT) yang mengintegrasikan sensor ultrasonik dan metode computer vision berbasis YOLO menggunakan modul ESP32-CAM untuk mendeteksi jarak serta mengklasifikasikan objek manusia dan non-manusia secara real-time. Sensor ultrasonik HC-SR04 digunakan untuk membaca jarak objek di area depan dan belakang kendaraan, sementara ESP32-CAM berfungsi menangkap citra lingkungan yang selanjutnya diproses melalui server berbasis Flask. Informasi jarak dan hasil klasifikasi objek kemudian diolah untuk menentukan status kondisi parkir, yaitu aman, hati-hati, dan bahaya, yang ditampilkan pada dashboard pemantauan real-time serta diteruskan ke indikator fisik berupa lampu lalu lintas mini dan buzzer sebagai peringatan kepada pengguna. Pengujian fungsional menunjukkan bahwa sistem mampu bekerja secara stabil dalam membaca jarak, mendeteksi keberadaan objek, serta menampilkan status parkir secara konsisten pada berbagai skenario jarak dan kondisi lingkungan. Integrasi sensor ultrasonik dan computer vision pada sistem ini memberikan informasi yang lebih komprehensif dibandingkan penggunaan sensor tunggal, sehingga berpotensi meningkatkan keselamatan dan efektivitas pemantauan area parkir. Untuk penelitian selanjutnya, sistem ini berpotensi dikembangkan melalui optimalisasi model deteksi objek serta perluasan variasi kondisi pengujian guna memastikan kinerja sistem yang andal pada lingkungan parkir yang lebih kompleks.Kata kunci : ESP32CAM; Internet of Things; Sensor Ultrasonik; Sistem Parkir Pintar; YOLO; Abstract – This research develops an Internet of Things (IoT)-based smart parking system that integrates ultrasonic sensors and YOLO-based computer vision using the ESP32-CAM module to detect object distance and classify human and non-human objects in real time. The HC-SR04 ultrasonic sensors are used to measure object distance in the front and rear areas of the vehicle, while the ESP32-CAM captures environmental images that are processed through a Flask-based server. The distance information and object classification results are then utilized to determine parking condition status, namely safe, caution, and danger, which are displayed on a real-time monitoring dashboard and transmitted to physical indicators in the form of a mini traffic light and buzzer. Functional testing shows that the system operates stably in reading distances, detecting object presence, and consistently displaying parking status under various distance and environmental conditions. The integration of ultrasonic sensing and computer vision provides more comprehensive information than single-sensor approaches, indicating its potential to improve safety and effectiveness in parking area monitoring. For future research, this system has the potential to be further developed through the optimization of the object detection model and the expansion of testing condition variations to ensure reliable system performance in more complex parking environment.Keywords: ESP32CAM; Internet of Things; Ultrasonic Sensor; Smart Parking System; YOLO;
Sistem Smart Water Monitoring Berbasis IoT dan Machine Learning untuk Analisis Ketinggian, Gelombang, dan Suhu Air Hutagalung, Fhadillah Br; Kiswanto, Dedy; Silalahi, Feby Juliana; Harahap, Fatima Asro
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10285

Abstract

Abstrak - Pemantauan kondisi perairan secara berkelanjutan merupakan aspek penting dalam mendukung pengelolaan sumber daya air dan mitigasi potensi bencana. Penelitian ini bertujuan untuk merancang, mengimplementasikan, dan menguji sistem Smart Water Monitoring berbasis Internet of Things (IoT) dan Machine Learning untuk memantau parameter ketinggian air, gelombang, dan suhu air secara real-time. Sistem dikembangkan menggunakan sensor ultrasonik dan sensor suhu yang terintegrasi dengan mikrokontroler serta dikoneksikan ke platform berbasis web untuk visualisasi data. Data hasil pengukuran dikirimkan melalui jaringan internet dan disimpan dalam basis data sebagai bahan analisis lanjutan. Metode Machine Learning diterapkan untuk menganalisis pola data dan mendeteksi kondisi anomali berdasarkan perubahan parameter air yang signifikan. Pengujian sistem menunjukkan bahwa perangkat IoT mampu melakukan akuisisi dan transmisi data secara stabil, sementara model Machine Learning yang digunakan memberikan performa yang baik dalam mengidentifikasi kondisi tidak normal pada data perairan. Hasil penelitian ini menunjukkan bahwa integrasi IoT dan Machine Learning dapat menjadi solusi yang efektif dan efisien untuk sistem pemantauan kondisi air secara cerdas dan berkelanjutan.Kata kunci: Sistem Logging; Otentikasi Dua Faktor; Rate Limiter; Machine Learning; Deteksi Anomali; Abstract - The development of modern cyber threats requires network security systems to have adaptive and integrated detection capabilities. This research aims to develop and test a prototype web-based network logging system equipped with a multi-layered authentication mechanism and anomaly pattern analysis using Machine Learning (ML). The system was developed using the Flask (Python) framework and tested online. The system's security components include Google reCAPTCHA and Two-Factor Authentication (OTP) for access protection, as well as the implementation of a Rate Limiter to mitigate low-rate distributed (multi-IP) attacks. The collected activity log data was then used to train two classification models, namely Decision Tree and Random Forest, with the main feature being the frequency of activity per IP within 60 seconds. Test results show that the Rate Limiter system successfully limits low-volume attacks. Meanwhile, ML performance analysis proves the effectiveness of the proposed method, where Decision Tree achieves perfect accuracy of 100.0% and an F1-Score of 1.0 in classifying anomalous activities in structured log datasets. This implementation demonstrates that the integration of secure logging with Machine Learning provides a strong foundation for the development of intelligent and efficient real-time threat detection systems.Keywords: Logging System; Two-Factor Authentication; Rate Limiter; Machine Learning; Anomaly Detection;
Pengembangan Sistem Otomatisasi Pakan Ikan dan Monitoring Kualitas Lingkungan Berbasis IoT dan Machine Learning untuk Budidaya Ikan Berbasis Web Alfin, Muhammad; Kiswanto, Dedy; Akbar, Muhammad Budi; Hasibuan, Najwa Latifah
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

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;
Implementation of IoT and Machine Learning for Monitoring and Prediction of Tank Water Levels Wahyudi, Rizky; Kiswanto, Dedy; Aulia, Windy; Audy Priscilia, Selfi
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 Purba, Jogi; Kiswanto, Dedy; Henrydunan, John Bush; Dly, Revidamurti
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.
Co-Authors Abdi Azzaki G, Fikri Abid Syuja, Muhammad Adidtya Perdana, Adidtya Adventino Gulo, Steven Afiati Nasution, Nadrah Afiq Alghazali Lubis Afrrahman S. Effendi, Ali Agi Berutu, Iwan Ahmad Fahrezi, Bryan Akbar, Muhammad Budi Al-Kautsar, Muhammad Zidane Alfin, Muhammad Alvansyah, Oka Amanah, Fadilla Andreas Sinabariba, Ade Anggraini Yolandari, Nezza Ardani Achmad Ashillah, Salma Asro Harahap, Fatimah Audy Priscilia, Selfi Aulia Artika, Delvita Aulia, Windy Auzi, Sybil Azima Lubis, Fauzan Azis, Khildan Rifail Azis, Zainal Azzahra, Dita Putri Berutu, Iwan Agi Bonifasius Simbolon, Aldo Br Hutagalung, Fhadillah Citra Hasiana Rajagukguk, Gloria Davina, Sherly Dealva Arsyad, Thania Defi, Aqilah Defiyanti, Aqilah Dewi Lestari Dly, Revidamurti Drilanang, Mhd Ilyasyah Dwi Febrianti, Bunga Evanthe, Hansel Evanthe, Hansel Valent Farezi, Nazwar Fitra, Muhammad Rizki Andrian Gaol, Anwar Shaleh Lbn Hafika, Rizky Ananda Hafiz, Alvin Halawa, Sovantri Putra Paskah Hanafiah Hanafiah Harahap, Fatima Asro Harahap, Salsa Nabila Hasibuan, Muhammad Alby Savana Hasibuan, Najwa Latifah Hatoguan, Idris Putra Henrydunan, John Bush Heppy Ria Sibarani, Ronasip Hermawan Syahputra Hidayat, M Fauzan Human Sukma, Ayman Hutabarat, Felix John Pardamean Hutagalung, Fhadillah Br Ichwanul Muslim Karo Karo Insan Pratama Siagian, Raihan Jehian, Neysa Talitha Jibran Muzakki Khan, Adhevta Josua Pinem Juliana Silalahi, Feby Khoiriah, Najwatul Latifah Hasibuan, Najwa Lubis, Ardilla Syahfitri Lubis, Fauzan Azima M.Pd., Zulherman Malau, Mei Lammi Manik, Albert Ramadhan Manik, Kristin Impana Maulida Surbakti, Nurul Melly Br Bangun Muhammad Agus Syaputra Lubis Muslim Sinaga, Rizal Musyaafa, Muhammad Naufal Nababan, Sirus Daniel Nababan, Sirus Daniel Haholongan Nasution, Adzkia Nasution, Afifah Naila Nasution, Aurela Khoiri Nasution, Siti Ananda Nezza Anggraini Yolandari Noor, Muhammad Yazid Nurul Maulida Surbakti Panggabean, Suvriadi Parapat, Gerhard Hasangapon Pebiana Putri, Fahra Prana Walidin, Adamsyach Pratama, Ega 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 Rajagukguk, Gloria Citra Hasiana Ramadhani, Fanny Rifail Azis, Khildan Rizki Andrian Fitra, Muhammad S., Yohana Lorinez Safitri, Eli Safrida Napitupulu Sapta Warman Zai, Tri Sembiring, Febe Gracia Shaleh Lbn Gaol, Anwar Siagian, Raihan Insan Pratama silalahi, evelyn keisha Silalahi, Feby Juliana Simanullang, Paskah Abadi Sinaga, Rizal Muslim Singgam, Pritiy Siregar, Dean Siregar, Ririn Amelia BR Sitanggang, Yoseph Christian Sitepu, Ahmad Denil Sitepu, Keysa Shifa Adwitia Siti Mamduhah siti wulandari Situmorang, Romatua SM Sidabutar, Yusiva Sri Dewi Sukma, Ayman Human Suryaningsih, Embun Syahri, Alfin Syukron, Ananda Irya Shakila Talitha Jehian, Neysa Tambunan, Vivielda Farmawaty Tua Halomoan Harahap, Tua Halomoan Valentino, Bob Vincentius Manurung, Enriko Wahyudi, Rizky Wardhana, Riyan Waruwu, Stefen Agus Yusuf Al-Hafiz, Ahmad Zai, Tri Sapta Warman Zidane Al-Kautsar, Muhammad Zulfahrizan, Atta Zulfi, M. Fikri