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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Teknologi Terpadu
Muhammad Alfin
Universitas Negeri Medan
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Sistem Informasi Kalkulator Kesehatan dan Kebugaran Tubuh Berbasis Web Rizky Wahyudi; John Bush Henrydunan; Muhammad Alfin; Debi Yandra Niska
Jurnal Teknologi Terpadu Vol 11 No 2 (2025): Desember, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v11i2.1678

Abstract

Health and fitness play a crucial role in improving the quality of life. This study aims to develop a web-based health calculator system capable of automatically, in real time, calculating health indicators such as ideal body weight, Body Mass Index (BMI), basic and daily calorie needs, and body fat levels. This system was built using the Waterfall method with HTML, CSS, PHP, and JavaScript technologies. To assess accuracy, testing was conducted on five user datasets by comparing the system's calculations with manual calculations using standard formulas. The difference between the two was calculated as the average relative deviation for each indicator: 0.13% (BMI), 0.84% (ideal body weight), 0.27% (basic calories), 0.27% (daily calories), and 0.73% (body fat levels). Since all deviations were <1%, the system's overall accuracy was estimated to exceed 99%. The test results can be further visualized graphically to clarify the comparison of values. The system also demonstrated ease of use and responsive performance. In conclusion, this system can be used as an accurate and practical educational tool to support self-monitoring of health.
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

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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;
Co-Authors Dedy Kiswanto John Bush Henrydunan Muhammad Budi Akbar Najwa Latifah Hasibuan Niska, Debi Yandra Rizky Wahyudi