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All Journal Information Technology Education Journal Journal of Embedded Systems, Security and Intelligent Systems
Ikram Anas
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Computer Science & IT Education

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Prototype Smart Life Jacket Berbasis Teknologi Long Range Wireless Data Telemetry Ikram Anas; Muh. Yusuf Mappeasse; Muliadi; Satria Gunawan Zain; Jumadi M Parenreng
Information Technology Education Journal Volume 2, Issue 1, Januari 2023
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1051.992 KB) | DOI: 10.59562/intec.v2i1.265

Abstract

Penelitian ini bertujuan untuk membuat sebuah prototype pelampung cerdas serta mengetahui hasil dari pengujiannya. Jenis penelitian yang digunakan dalam penelitian ini adalah Research dan Development (R&D) yaitu sebuah proses atau langkah-langkah untuk mengembangkan suatu produk baru atau menyempurnakan produk yang telah ada. Menggunakan prosedur pengembangan PPE (Pre Production, Production dan Post Production). Penelitian ini dilakukan untuk membuat protype pelampung yang dapat mengirimkan koordinat sekaligus untuk mengetahui hasil pengujian functionality pada prototype serta untuk mengetahi jarak jangkau pada prototype, akurasi gps serta penggunaan daya yang dibutuhkan. Hasil dari penelitian ini adalah didapatkan sebuah solusi untuk melakukan evakuasi korban kecelakaan laut. Produk yang dihasilkan adalah sebuah pelampung dengan perangkat yang dapat dideteksi titik koordinatnya apabila pelampung dikenakan oleh korban. Titik koordinat didapatkan dari GPS yang terpasang pada pelampung dan dikirim menggunakan modul LoRa. Titik koordinat pelampung dapat terdeteksi dari jarak rata-rata 1,05 kilometer dengan toleransi akurasi GPS sebesar 5,33 meter.
SVM Parameter Optimization with PSO for Sensor-Based Water Quality Classification and Monitoring Dashboard Andi Muharram; Hasmiah Husayn; Ibnu Farhan Hasrul; Ibnu Hajar; Ibnu Mundzir Hasanuddin; Ikram Anas
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/te7wgc92

Abstract

Water quality monitoring is essential for environmental sustainability and public health protection. Conventional laboratory-based testing is often time-consuming and unsuitable for real-time monitoring systems. The development of sensor-based Internet of Things (IoT) technology enables continuous acquisition of water quality parameters such as pH, temperature, turbidity, and Total Dissolved Solids (TDS). However, accurate classification of water quality from multi-parameter sensor data remains a challenge due to non-linear data characteristics and the sensitivity of machine learning models to parameter selection. This study aims to optimize the parameters of Support Vector Machine (SVM) using Particle Swarm Optimization (PSO) for sensor-based water quality classification and to integrate the optimized model into a real-time monitoring dashboard. A quantitative experimental approach was employed by comparing the performance of standard SVM and PSO-optimized SVM models. The dataset consisted of sensor measurements collected over 30 days and was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that parameter optimization significantly improves classification performance and enhances the model’s ability to detect critical water quality conditions. The optimized SVM model was successfully integrated into a web-based dashboard capable of real-time monitoring and classification. This study demonstrates that combining metaheuristic optimization with machine learning provides an effective and practical solution for intelligent water quality monitoring systems
Co-Authors Andi Muharram Hasmiah Husayn Ibnu Farhan Hasrul Ibnu Hajar Ibnu Mundzir Hasanuddin Jumadi Mabe Parenreng Muh. Yusuf Mappeasse Muliadi Muliadi Satria Gunawan Zain