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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 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 Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK)
Dedy Kiswanto
Universitas Negeri Medan
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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.
RANCANG BANGUN MODEL KONTROL AKSES DINAMIS BERBASIS KONTEKS PADA ARSITEKTUR ZERO TRUST Vega S. Meliala, Ruth Amelia; Kiswanto, Dedy; Harahap, Salsa Nabila; Dly, Revidamurti
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.500

Abstract

The rapid development of digital systems and interconnected environments has created new challenges in securing data. Traditional perimeter-based security models are no longer adequate to protect sensitive information from internal and external threats. This study proposes the design and implementation of a Context-Based Dynamic Access Control Model within the Zero Trust Architecture (ZTA) framework. The proposed system integrates contextual authentication, adaptive risk evaluation, and a dynamic policy engine to implement more granular access control in multi-user web applications. The prototype was developed using Node.js, Express.js, and MySQL, featuring multi-factor authentication, contextual verification via OTP, session management, and security notifications.The test results indicate that the system is capable of detecting changes in access context, enforcing re-authentication, and recording all user activities for auditing and anomaly detection purposes. The integration of contextual authentication, adaptive access control, and Zero Trust principles has been proven to enhance data protection and user accountability without reducing system usability..
IMPLEMENTASI SISTEM LOGIN WEB BERBASIS ZTA DENGAN INTEGRASI OTP BREVO DAN CAPTCHA Gulo, Steven Adventino; Kiswanto, Dedy; Arifin, Muhammad Hidayatul; Aulia, Windy
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.501

Abstract

Keamanan autentikasi pada sistem web merupakan komponen penting dalam menjaga kerahasiaan dan integritas data pengguna dari berbagai ancaman siber seperti brute force, phishing, dan serangan bot. Penelitian ini mengimplementasikan sistem login web berbasis Zero Trust Architecture (ZTA) yang diintegrasikan dengan One-Time Password (OTP) Brevo serta Google reCAPTCHA untuk memperkuat proses verifikasi identitas pengguna. Prinsip dasar “never trust, always verify” diterapkan agar setiap permintaan akses divalidasi secara menyeluruh tanpa adanya asumsi kepercayaan terhadap pengguna. Sistem dikembangkan menggunakan bahasa pemrograman web dengan dukungan basis data MySQL dan diuji melalui serangkaian uji fungsional, performa, serta keamanan. Hasil pengujian menunjukkan bahwa kombinasi ZTA, OTP Brevo, dan reCAPTCHA secara signifikan meningkatkan keamanan proses login dengan membatasi percobaan akses berulang, mencegah serangan otomatis dari bot, serta menekan potensi login ilegal. Selain itu, penerapan enkripsi kata sandi dan pembatasan waktu OTP terbukti meningkatkan keandalan autentikasi berlapis. Berdasarkan hasil percobaan, sistem yang dikembangkan dinilai lebih tangguh, adaptif, dan efisien dalam menghadapi ancaman siber modern tanpa mengurangi kenyamanan pengguna.
PENINGKATAN AKURASI DETEKSI DINI KEBAKARAN BERBASIS IOT MENGGUNAKAN ALGORITMA RANDOM FOREST Meliala, Ruth Amelia Vega S; Kiswanto, Dedy; Sianipar, Freyro Dobry; Lubis, Fauzan Azima
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.516

Abstract

Fire is one of the most frequent disasters and poses a significant risk to human safety, environmental sustainability, and property due to delayed early detection. This study aims to design and implement an early fire warning system based on the Internet of Things (IoT) enhanced with Machine Learning to improve detection accuracy and reliability. The system utilizes an ESP32 microcontroller as an edge node integrated with a DHT11 sensor for temperature and humidity, an MQ-2 sensor for gas and smoke concentration, and a flame sensor for fire detection. Multisensor data are transmitted in real time to a Flask-based server via the HTTP protocol and processed using a Random Forest classification model to determine environmental conditions as either safe or fire-hazardous. The classification results are displayed on a web-based dashboard and accompanied by automatic notifications delivered through a Telegram bot. Experimental results show that the proposed system achieves a detection accuracy of 94%, a low false positive rate, and a notification latency of less than 3 seconds, based on experiments conducted using a dataset of 3000 samples with an 80:20 split between training and testing data.The integration of IoT and Machine Learning demonstrates superior performance compared to conventional threshold-based methods, making the system a promising preventive solution for fire risk mitigation in residential and industrial environments.
Sistem Keamanan Pintu Berbasis Computer Vision dengan Biometric Face Recognition dan Physical Tampering Detection Hutabarat, Felix John Pardamean; Kiswanto, Dedy; Simanullang, Paskah Abadi; Amanah, Fadilla
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.v9i1.10270

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 Syahri, Alfin; Kiswanto, Dedy; Manik, Albert Ramadhan; Sitohang, Yuda Advis Ambrosius
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.v9i1.10267

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;
BOOTCAMP TEKNIK JARINGAN TELEKOMUNIKASI FIBER OPTIK UNTUK SISWA/I TKJ SMKS TRI SAKTI LUBUK PAKAM Dedy Kiswanto; Hermawan Syahputra; Suvriadi Panggabean; Sri Dewi; Nurul Maulida Surbakti
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2025): Volume 6 No. 2 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i2.43050

Abstract

Kegiatan bootcamp teknik teknik jaringan telekomunikasi fiber optik untuk siswa/siswi teknik komputer dan jaringan di SMKS Tri Sakti Lubuk Pakam bertujuan untuk meningkatkan kompetensi siswa/i jurusan Teknik Komputer dan Jaringan (TKJ) di SMKS Tri Sakti Lubuk Pakam terkait instalasi jaringan fiber optik. Kegiatan ini diadakan untuk menjawab kebutuhan industri yang terus berkembang, di mana instalasi fiber optik menjadi standar dalam jaringan telekomunikasi secara Global. Metode yang dilakukan meliputi training materi teori instalasi fiber optik oleh praktisi Industri, demonstrasi, dan pelatihan langsung instalasi fiber optik. Hasil dari kegiatan ini menunjukkan bahwa sebagian besar peserta mampu memahami prinsip dasar fiber optik, jenis kabel yang digunakan, dan teknik instalasi yang benar. Namun, masih terdapat beberapa peserta yang belum sepenuhnya memahami aspek-aspek teknis tertentu. Diakhir kegiatan dilakukan penyerahan alat instalasi fiber optik kepada sekolah dengan harapan dapat mendukung peningkatan kompetensi instalasi fiber optik lebih lanjut dan memastikan kesiapan siswa menghadapi dunia kerja pada bidang telekomunikasi fiber optik.
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 Syahri Alfin, Muhammad Alvansyah, Oka Alvin Hafiz Amanah, Fadilla Ananda Irya Shakila Syukron Andreas Sinabariba, Ade Anggraini Yolandari, Nezza Aqilah Defiyanti Ardani Achmad Arifin, Muhammad Hidayatul Ashillah, Salma Asro Harahap, Fatimah Audy Priscilia, Selfi Aulia Artika, Delvita Aulia, Windy Auzi, Sybil Azima Lubis, Fauzan Azis, Zainal Azzahra, Dita Putri 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 Gloria Citra Hasiana Rajagukguk Gulo, Steven Adventino Hafika, Rizky Ananda Hafiz, Alvin Hanafiah Hanafiah Harahap, Fatima Asro Harahap, Salsa Nabila Hasibuan, Najwa Latifah 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 Idris Putra Hatoguan Insan Pratama Siagian, Raihan Iwan Agi Berutu Jehian, Neysa Talitha Jibran Muzakki Khan, Adhevta 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 Maulida Surbakti, Nurul Meliala, Ruth Amelia Vega S Melly Br Bangun Mhd Ilyasyah Drilanang Muhammad Agus Syaputra Lubis Muhammad Alby Savana Hasibuan Muhammad Naufal Musyaafa Muhammad Zidane Al-Kautsar Muslim Sinaga, Rizal Nababan, Sirus Daniel Nababan, Sirus Daniel Haholongan 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 Ramadhani, Fanny Rangga Wahyu Pratama 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 Sembiring, Febe Gracia Shaleh Lbn Gaol, Anwar Siagian, Raihan Insan Pratama Sianipar, Freyro Dobry silalahi, evelyn keisha Silalahi, Feby Juliana Simanullang, Paskah Abadi Siregar, Dean Sitanggang, Yoseph Christian Sitepu, Ahmad Denil Sitepu, Keysa Shifa Adwitia Siti Mamduhah siti wulandari Sitohang, Yuda Advis Ambrosius Situmorang, Romatua SM Sidabutar, Yusiva Sovantri Putra Paskah Halawa Sri Dewi Stefen Agus Waruwu Sukma, Ayman Human Suryaningsih, Embun Suvriadi Panggabean Syahri, Alfin Syukron, Ananda Irya Shakila Talitha Jehian, Neysa Tri Sapta Warman Zai Tua Halomoan Harahap, Tua Halomoan Vega S. Meliala, Ruth Amelia Vincentius Manurung, Enriko Vivielda Farmawaty Tambunan Wahyudi, Rizky Waruwu, Stefen Agus Yohana Lorinez S. Yusuf Al-Hafiz, Ahmad Zidane Al-Kautsar, Muhammad Zulfahrizan, Atta Zulfi, M. Fikri