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All Journal Simetris Fokus Abdimas
Putri, Rosalinda Ellysa
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Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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SITAMPAN (Sistem Taman Pintar) untuk meningkatkan Produktivitas Sayuran Organik pada Kelompok Wanita Tani (KWT) di Desa Nglanjuk Putri, Rosalinda Ellysa; Ummara, Aulia; Sofani, Mohammad Taufik; Ikhsan, Ridhotu Nur; Ramadhany, Achmad Fajar; Permadi, Arjun; Setiawan, Agung; Reziqna, Ayu Rahma; Maulana, Aditya Rahman; Dewangga, Diaz Alegra Arif
Fokus ABDIMAS Vol 3, No 1: April 2024
Publisher : STIE Pelita Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34152/abdimas.3.1.5-10

Abstract

The 2023 Ormawa Capacity Strengthening Program by the Electrical Engineering Student Association team focused on the implementation of smart farming in Nglanjuk Village, Cepu District, Blora, Central Java. Although the village is rich in natural resources, the low quality of human resources and the lack of technology utilization are obstacles. The research noted that the Kelompok Wanita Tani (KWT) faced difficulties in manual watering, a problem that needed to be addressed immediately. Therefore, the team designed a solution by presenting the SiTampan (Sistem Taman Pintar) tool that can control the plant watering pump through a smartphone. This program aims to implement smart farming, increase the efficiency of time and energy, and create KWT Nglanjuk Village that implements technology-based smart agriculture. Keywords : Smart Farming, SiTampan, Kelompok Wanita Tani, Ormawa Capacity Building.
PREDIKSI KUALITAS UDARA BERDASARKAN PARAMETER CO DAN CO2 MENGGUNAKAN ARTIFICIAL NEURAL NETWORK Putri, Rosalinda Ellysa; Suprawikno, Suprawikno
SIMETRIS Vol 19 No 1 (2025): SIMETRIS
Publisher : Sekolah Tinggi Teknologi Ronggolawe Cepu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51901/simetris.v19i1.569

Abstract

This study aims to predict carbon monoxide (CO) and carbon dioxide (CO2) levels as indicators of air quality using the Artificial Neural Network (ANN) method with a 21-20-7 architecture. Data was collected over 28 days using the AQMSense device, with the first 21 days used for training and the remaining 7 days for testing. The results show that the ANN model effectively recognizes historical data patterns, indicated by a regression value close to 1. The prediction accuracy reached 89.96% for CO and 95.84% for CO2, demonstrating strong model performance. ANN proves to be an effective tool for daily air quality prediction and holds potential as a decision-support system for air pollution mitigation
Co-Authors agung setiawan Dewangga, Diaz Alegra Arif Ikhsan, Ridhotu Nur Maulana, Aditya Rahman Permadi, Arjun Ramadhany, Achmad Fajar Reziqna, Ayu Rahma Sofani, Mohammad Taufik Suprawikno . Ummara, Aulia