Article Per Year (5 Year)
p-Index From 2021 - 2026
3.916
P-Index
This Author published in this journals
All Journal Jurnal Infomedia Jurnal Sains dan Teknologi Reaksi Vocatech : Vocational Education and Technology Journal INFOKUM Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Teknologi TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Journal Of Artificial Intelligence And Software Engineering eProceeding of TIK Jurnal Teknologi Rekayasa Informasi dan Komputer
Simbolon, Zulfan Khairil
Polteknik Negeri Lhokseumawe
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
Sistem IoT dan Algoritma Naive Bayes untuk Monitoring Kualitas Air Laut secara Real-Time Myladi, Mulyadi; Murtaza, Haikal; Huzaifah, Atthariq; Simbolon, Zulfan Khairil
VOCATECH: Vocational Education and Technology Journal Vol 7, No 2 (2025): December
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v7i2.291

Abstract

AbstractThis study develops a real-time seawater quality monitoring system by integrating Internet of Things (IoT) technology and the Naive Bayes classification algorithm. The system utilizes an ESP32 microcontroller equipped with pH and turbidity sensors to collect seawater quality data from coastal areas. The sensor data are transmitted continuously to a Firebase Realtime Database and visualized through an Android-based mobile application developed using Flutter. The Naive Bayes model was trained using a public Kaggle dataset consisting of 5,569 water quality records classified into polluted and unpolluted categories, and then applied to classify real-time sensor data collected at Pantai Pusong, Aceh. Data preprocessing included cleaning, normalization, outlier handling, and class balancing. Experimental results show that the proposed system is capable of performing stable real-time data acquisition, transmission, and visualization. The Naive Bayes classification achieved an accuracy of 53%, with better performance in identifying unpolluted seawater conditions than polluted ones. Although the classification accuracy is moderate, the results demonstrate that the integration of IoT devices, cloud services, and probabilistic machine learning can provide a functional end-to-end solution for real-time seawater quality monitoring and early environmental awareness in coastal regions.Keywords:Internet of things; naive bayes; real-time monitoring; seawater quality; turbidityAbstrakPenelitian ini mengembangkan sistem monitoring kualitas air laut secara real-time dengan mengintegrasikan teknologi Internet of Things (IoT) dan algoritma klasifikasi Naive Bayes. Sistem menggunakan mikrokontroler ESP32 yang dilengkapi sensor pH dan turbidity untuk mengakuisisi data kualitas air laut di wilayah pesisir. Data sensor dikirimkan secara kontinu ke Firebase Realtime Database dan ditampilkan melalui aplikasi Android berbasis Flutter. Model Naive Bayes dilatih menggunakan dataset publik dari Kaggle yang terdiri dari 5.569 data kualitas air dengan dua kelas, yaitu air tercemar dan tidak tercemar, kemudian diterapkan untuk mengklasifikasikan data sensor real-time yang diperoleh dari Pantai Pusong, Aceh. Tahapan praproses data meliputi pembersihan data, normalisasi, penanganan outlier, dan penyeimbangan kelas. Hasil pengujian menunjukkan bahwa sistem mampu melakukan akuisisi, pengiriman, dan visualisasi data secara real-time dengan kinerja yang stabil. Evaluasi menggunakan confusion matrix menghasilkan tingkat akurasi klasifikasi sebesar 53%, dengan kemampuan yang lebih baik dalam mengidentifikasi kondisi air tidak tercemar dibandingkan air tercemar. Meskipun akurasi model masih terbatas, sistem yang dikembangkan menunjukkan keberhasilan integrasi IoT, layanan cloud, dan pembelajaran mesin sebagai solusi awal pemantauan kualitas air laut secara real-time di wilayah pesisir.Kata Kunci:Internet of things; kualitas air laut; naive bayes; pemantauan real-time; turbidity
Implementation of Edge Intelligence in an IoT-Based Automatic Plant Watering System Using a Classification Algorithm Mulyadi Mulyadi; Zulfan Khairil Simbolon; Hendrawaty Hendrawaty; Hasyimi Abdullah
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27263

Abstract

This study discusses the application of Edge Intelligence in an Internet of Things (IoT)-based automatic plant watering system by utilizing the Decision Tree algorithm to support the concept of precision agriculture. The system was developed using several environmental parameters, such as soil moisture, air temperature, humidity, light intensity, and plant age as input data to determine watering decisions. All data processing is carried out directly on the ESP32 microcontroller so that the system can work faster, reduce dependence on cloud computing, and increase network and power usage efficiency. The Decision Tree method is applied through Information Gain calculations to determine the attributes that most influence watering decisions. Based on the analysis results, air humidity obtained the highest Information Gain value of 0.8813 bits at a threshold of 57.5%, making it considered the most effective in the watering classification process. However, soil moisture was chosen as the root node because it has a higher level of agronomic relevance and is easier to implement in the plant monitoring system. Data processing is carried out locally using the ESP32 microcontroller, while the monitoring data is sent via the MQTT protocol to a smartphone application for real-time monitoring. Test results show that the system is capable of carrying out automatic watering accurately, increasing water efficiency, and adapting to changing environmental conditions adaptively. Thus, this system can be an effective, intelligent, and sustainable solution in supporting IoT-based plant cultivation management. The results show that the Decision Tree algorithm can determine the right watering decisions through Information Gain calculations. The system is also connected to the MQTT protocol that allows real-time monitoring of plant conditions via a smartphone application. Based on the test results, the system is capable of carrying out automatic watering effectively, saving water, and adapting to changing environmental conditions. Therefore, this system can be an innovative and sustainable solution to support the development of IoT-based smart agriculture.
Co-Authors Abdurrazaq Abdurrazaq, Abdurrazaq Alvira, Alvira Amirullah Amirullah -, Amirullah Amirullah Amirullah Anggraini, Anggy Arhami, Muhammad Ari Saputra, Ari Arista, Della Atiqah Aida Azhar Azhar Azhar Azhar Azis, M. Dian Novrizal Aziz, M.Dian Novrizal Baniah Baniah Della Arista Firdaus, Muhammad Akbar Firnanda, Ary Haimi Ardiansyah Haris, Mifzal Hasyimi Abdullah Hendrawati Hendrawati Hendrawaty Huzaeni Huzaeni, Huzaeni Huzaifah, Atthariq Huzeini, Huzeini Ihyaulumiddin, Azis Ismail Ismail Isra Adelya Izzati Juanda, Zacky Syifa Mahdi Mahdi, Mahdi Maiyurita Maiyurita Miranti Septriani Muhammad Farhan Muhammad Khadafi Muhammad Rizka, Muhammad Mulyadi MULYADI Mulyadi Mulyadi Mulyadi Mulyadi Murtaza, Haikal Myladi, Mulyadi Novita, Noni Novriza, Ferdiansyah Nurul Husna Phonna, Khairuna Pocut Nurul Alam Pratiwi, Mutiara Dina Prihatin, Nanang Qatrun Nada Rahmad Hidayat Reza Fauzan Rima Dhinta Dewi Astuti Rizal Syahyadi S Syafruddin Salahuddin Salahuddin Saputra, Herdian Shahara Mahfudhah Sila Abdullah Syakri Sila Abdullah Syakry Syakri, Sila Abdullah Syakri,S.T.,M.T, Sila Abdullah T. Zakia Maulani Teuku Mizan Sya'rani Denk Ulfa, Aida