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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Abdi Jurnal Publikasi
Ali Nurohman Fadilah
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Computer Science & IT Social Sciences Other

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MENGENAL DUNIA INTERNET OF THINGS (IOT) DAN ARTIFICIAL INTELLIGENCE (AI): MENYIAPKAN GENERASI MUDA SMK KEMALA BHAYANGKARI DELOG UNTUK REVOLUSI TEKNOLOGI Ali Nurohman Fadilah; Arief Wardana; Aziz Saputra; Fajrin; Imam Maulana; Muhammad Sulthan Al Jazeera; Raden Muhammad; Rahmat Iqsan; Ranu Ramadhan; Rio Hadi Chandra
Abdi Jurnal Publikasi Vol. 3 No. 2 (2024): November
Publisher : Abdi Jurnal Publikasi

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Abstract

Pengabdian kepada masyarakat ini bertujuan untuk memperkenalkan teknologi Internet of Things (IoT) dan Artificial Intelligence (AI) kepada siswa SMK Kemala Bhayangkari Delog. Kegiatan dilakukan dalam bentuk workshop interaktif yang mencakup pemaparan materi, diskusi, dan kuis. Program ini berhasil meningkatkan pemahaman siswa tentang IoT dan AI, menumbuhkan minat belajar, serta memberi wawasan aplikasi praktis teknologi ini dalam kehidupan sehari-hari maupun sektor industri. Rekomendasi dari kegiatan ini meliputi pelatihan berkelanjutan, peningkatan fasilitas pembelajaran, dan kolaborasi dengan dunia industri untuk mendukung kesiapan siswa menghadapi Revolusi Industri 4.0.
IMPLEMENTASI ALGORITMA RANDOM FOREST UNTUK MEMPREDIKSI KETINGGIAN AIR SEBAGAI SISTEM PERINGATAN DINI BANJIR TERINTEGRASI WEB Ali Nurohman Fadilah; Fitri Yanti
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 03 (2026): Volume 11 No. 03, September 2026 Processed
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i03.62263

Abstract

Floods are among the most frequent disasters in regions with high rainfall, including Bogor Regency, creating a need for a system capable of predicting rising water levels early enough for preventive measures to be taken. This study applies the Random Forest Regression algorithm to predict the water level (TMA) one hour ahead using historical hydrological data from the Batu Beulah Water Level Monitoring Station, Bogor, comprising water level, rainfall, and discharge derived from a rating curve, and integrates the resulting model into a Next.js-based web application. Unlike locally run machine learning programs accessible only to technical users, the developed system separates the prediction service (FastAPI) from the web interface so that the output can be used by two actors, namely the station administrator and the general public. The dataset consists of 7,817 hourly records collected throughout 2024 with 31 input features, split chronologically into training, validation, and testing sets with a 70:15:15 ratio to avoid data leakage. The model was trained with 300 decision trees and evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²), and compared against a persistence baseline. Testing results show an MAE of 0.0957 m, an RMSE of 0.2137 m, and an R² of 0.7918, with RMSE and R² outperforming the baseline. Current water level, calculated discharge, and the three-hour rolling mean of water level were the most influential features. Black Box and White Box testing confirmed that the system's core functions, including login, data management, prediction execution, and the display of Safe/Alert/Danger warning status, operate as intended.
Co-Authors Arief Wardana Aziz Saputra Fajrin Fitri Yanti Imam Maulana Muhammad Sulthan Al Jazeera Raden Muhammad Rahmat Iqsan Ranu Ramadhan Rio Hadi Chandra