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All Journal Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Rekayasa elektrika
Andriano, Niko
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Hybrid Deep Learning Models Using LSTM with Random Forest for Radio Frequency-Based Human Activity Recognition in Line-of-Sight and Non-Line-of-Sight Environments Andriano, Niko; Pribadi, Feddy Setio
Jurnal Rekayasa Elektrika Vol 21, No 2 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v21i2.44828

Abstract

Human activity recognition (HAR) has become an important field of study because of its wide range of applications in healthcare, security, and smart living systems. Radio frequency (RF)-based HAR offers a non-invasive and privacy-preserving alternative to traditional vision-based systems. This study proposes a hybrid deep learning model combining long short-term memory (LSTM) networks with Random Forest classifiers for RF-based HAR, aiming to improve recognition accuracy across diverse environments. The model was evaluated using channel state information (CSI) and received signal strength indicator (RSSI) features under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. synthetic minority over-sampling technique (SMOTE) was integrated to balance the dataset and K-fold cross-validation was employed to assess robustness. The dataset included data from 8 subjects performing 10 different activities. The model achieved high classification accuracy, with 99.40% in Environment 1 (LOS), 97.58% in Environment 2 (LOS), and 98.30% in Environment 3 (NLOS), demonstrating the models adaptability and effectiveness. The results highlight the potential of the hybrid LSTM with random forest approach for scalable and reliable RF-based HAR systems that can be integrated into real-world Internet-of-Things (IoT) applications.
Peningkatan Produksi Budidaya Ikan Bandeng Melalui Implementasi Sistem Monitoring Kualitas Air Berbasis IoT di Desa Kadilangu Rochim, Febry Putra; Sulistyawan, Vera Noviana; Saputro, Bagaskoro; Arlinto, Arlinto; Andriano, Niko; Maghribi, Mohammad Maulana; Kladius, Rafik
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2025): Desember
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v5i2.762

Abstract

Desa Kadilangu di Kabupaten Pati, Jawa Tengah, dikenal sebagai salah satu pusat budidaya ikan bandeng. Namun, permasalahan kualitas air akibat limbah industri tapioka menjadi tantangan bagi para petani tambak, yang menyebabkan penurunan hasil panen dan kematian ikan. Kualitas air tambak sangat penting dalam budidaya ikan bandeng, namun pengecekan kualitas air masih dilakukan secara tradisional tanpa bantuan teknologi. Oleh karena itu, pengabdian ini bertujuan untuk meningkatkan produksi ikan bandeng melalui penerapan sistem monitoring kualitas air berbasis Internet of Things (IoT). Sistem ini menggunakan sensor untuk mengukur parameter penting seperti suhu, pH, dan kekeruhan air yang kemudian ditampilkan secara real-time melalui aplikasi web dan smartphone. Metode yang digunakan dalam pengabdian ini meliputi survei lapangan, studi pustaka, pengembangan sistem berbasis IoT, pelatihan kepada petani tambak, serta evaluasi penggunaan sistem. Hasil pengabdian menunjukkan bahwa sistem monitoring kualitas air mampu memberikan informasi yang akurat dan real-time, sehingga petani tambak dapat dengan cepat mengambil tindakan preventif. Hal ini berdampak pada peningkatan produksi ikan bandeng yang meningkat 10% - 12,5% dari hasil panen musim sebelumnya dan efisiensi pengelolaan tambak. Sistem ini menjadi solusi tepat guna dalam membantu petani tambak menjaga kualitas air secara lebih efektif.
Co-Authors Arlinto Arlinto, Arlinto Feddy Setio Pribadi Kladius, Rafik Maghribi, Mohammad Maulana Rochim, Febry Putra Saputro, Bagaskoro Sulistyawan, Vera Noviana