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All Journal Journal of Social Work and Empowerment Galaksi BIOS: Jurnal Informatika dan Sains
I Made Subrata Sandhiyasa
Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Social Sciences Other

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Artificial Intelligence-Assisted IoT Model for Water Level Monitoring and Prediction Systems: A Review and Analysis I Gede Iwan Sudipa; I Dewa Gede Agung Pandawana; I Made Subrata Sandhiyasa
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 02 (2024): BIOS: Jurnal Informatika dan Sains, October 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Flood disasters have become increasingly frequent and severe due to climate change and urban expansion. Traditional water level monitoring systems often lack real-time data processing and predictive capabilities. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) presents a promising solution for enhancing water level monitoring and flood prediction systems. This paper provides a comprehensive review and analysis of AI-assisted IoT models for water level monitoring and prediction. It examines system architectures, sensor networks, and the application of AI algorithms such as Fuzzy Logic and Long Short-Term Memory (LSTM) networks. The study highlights the benefits of combining real-time IoT data with AI-based predictive models to improve the accuracy and responsiveness of flood early warning systems. Challenges related to data quality, sensor network infrastructure, and model optimization are also discussed. This review aims to inform future research and development in intelligent disaster mitigation systems.
Pengenalan Aplikasi Pengolah Kata Microsoft Word pada Sekolah Dasar Ketut Jaya Atmaja; I Made Subrata Sandhiyasa; I Putu Yoga Endrawan; I Gede Ega Ariesta; Agustinus Sanaka Luan
Journal of Social Work and Empowerment Vol 5 No 2 (2026): Journal of Social Work and Empowerment - (Januari-Februari 2026)
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/jswe.v5i2.1105

Abstract

Dalam era digital saat ini, keterampilan menggunakan teknologi informasi menjadi sangat penting, termasuk bagi anak-anak sekolah dasar. Microsoft Word merupakan salah satu aplikasi pengolah kata yang paling umum digunakan dan memiliki berbagai fitur yang dapat membantu dalam proses belajar mengajar. SDN 2 Sembung Gede merupakan salah satu sekolah dasar negeri yang ada pada Desa Sembung Gede, Kecamatan Kerambitan, Kabupaten Tabanan. Sekolah ini terdiri dari 26 siswa. Oleh karena itu, pengenalan Microsoft Word kepada siswa SD akan memberikan mereka dasar yang kuat untuk keterampilan teknologi di masa depan. Dengan pelaksanaan kegiatan ini, diharapkan siswa SDN 2 Sembung Gede dapat memiliki kemampuan dasar dalam menggunakan Microsoft Word sehingga dapat menunjang proses belajar mereka sehari-hari. Kegiatan ini juga diharapkan dapat memotivasi siswa untuk lebih tertarik dalam mempelajari teknologi informasi.
Detection of Disease in Platycerium Ornamental Plant Leaves Using Yolo 12 I Made Subrata Sandhiyasa; Made Landiva; I Gede Sudiantara; I Putu Noven Hartawan
Jurnal Galaksi Vol. 3 No. 1 (2026): Galaksi - May 2026
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v3i1.120

Abstract

Platycerium is an epiphytic ornamental plant with high aesthetic and economic value, thus requiring proper care. Identifying Platycerium leaf diseases based on visual symptoms often requires precision and experience, thus necessitating an image-based automated approach. This study aims to develop a Platycerium leaf disease detection model using the deep learning-based YOLO method. The model was developed using Kaggle Notebook with P100 GPU support. The dataset used consisted of three disease classes, namely Bacterial Leaf Spot, Fern Scale, and Rizoctonia Blight. Model training was carried out with variations in the number of epochs of 50, 75, and 100 epochs, and evaluated using the Precision, Recall, and Mean Average Precision (mAP) metrics. The results showed that training with 50 epochs gave the best results with a mAP50 value of 0.953 and mAP50–95 of 0.577. Testing using test data and data outside the dataset showed that the model was able to detect Platycerium leaf disease in test images by displaying bounding boxes and class labels. Based on these results, the YOLO model developed can be used as an image-based approach for detecting Platycerium leaf disease and can be further developed.
Co-Authors Agustinus Sanaka Luan I Dewa Gede Agung Pandawana I Gede Ega Ariesta I Gede Iwan Sudipa I Gede Sudiantara I Putu Noven Hartawan I Putu Yoga Endrawan Ketut Jaya Atmaja Made Landiva