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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Zad Al-Ummah: Jurnal Pengabdian Masyarakat
Alamsyah, Aditya
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics Social Sciences Other

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Journal : Jurnal Penelitian Pendidikan IPA (JPPIPA)

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Development Edge Device Monitoring System Stunting and Malnutrition in Golden age 0–5 years Integrated with AI Widanti, Nurdina; Handini, Wike; Yanto, Nur Witdi; Alamsyah, Aditya
Jurnal Penelitian Pendidikan IPA Vol 9 No SpecialIssue (2023): UNRAM journals and research based on science education, science applic
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9iSpecialIssue.6397

Abstract

Golden age is the best period for a child's growth, monitoring of growth and development must be carried out regularly. Growth and development disorders in children are stunting and malnutrition. This incident also prompted the government through the Ministry of Health to create a special program towards a golden Indonesia 2045 to monitor stunting and disease, especially in children. The prevalence of stunting decreased to 21.6% from 24.4% in 2022. Early detection of stunting and malnutrition, where the research object is children aged 0-5 years. This prototype was built using a load cell sensor, a study of the use of optical sensors and ultrasonic sensors to measure body height, and a MAX sensor to detect children's anemia. The integration of this tool combines IoT and AI. The results obtained to validate the use of load cells have a reading error of 0.01% with an accuracy of 99%. Comparison using optical sensors and ultrasonic sensors. Optical sensors have result average error of 0.01, accuracy 98.99%, ultrasonic sensors error was 0. 15 with 85% accuracy. To measure malnutrition, the anemia parameters were processed using the Dense Neural Network (DNN) model with 256 neurons showing an accuracy of 98.03%.
Design Smart Farming in Rice Field for Monitoring Soil Fertility and Pest Rate Using Internet of Things Widanti, Nurdina; Alamsyah, Aditya; Albus, Actor; Ikhsan, Ahmad Nur; Lestari, Sri Wiji; Handini, Wike; Raharjo, Sasmito Adi
Jurnal Penelitian Pendidikan IPA Vol 10 No 8 (2024): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i8.8288

Abstract

Rice fields in Indonesia have a strategic role in providing food for the Indonesian population. The Central Statistics Agency (BPS) noted that domestic rice consumption also continues to increase, 98.35% of households in Indonesia consume rice. There are many influencing factors for production rice such as pest, climate change. The aim to optimize rice production by monitoring soil moisture and soil pH and adding protection features to detect rat pests. This tool was built using an Internet of Things-based system integration method, where the system output can be monitored on the blynk and email applications for the reading history of rat pests if they are caught on camera. The results obtained from the system are Soil moisture sensor readings have a system accuracy of 99% with an error value of 0.01. And the pH sensor reading has an accuracy of 99% with an error of 0.015. The most optimal PIR sensor reading is 1 meter and this data is sent simultaneously with the camera sensor via email. Monitoring data on rice agricultural land by adding rat pest protection features, as well as historical data can be captured wellcan provide a strong basis for the development of more effective and sustainable.
Co-Authors Abdul Hakim Albus, Actor Hadi, Lalu Nuzul Ilham Hikam, Ainul Ikhsan, Ahmad Nur Muhammad Irfan Raharjo, Sasmito Adi Sri Wiji Lestari widanti, Nurdina Wike Handini Yanto, Nur Witdi