Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Vertex
Elvi Sunita
Universitas Bina Sarana Informatika, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Unsupervised Machine Learning Based DSS for Land Profiling and Disease Risk Mitigation in Smart Farming Embun Fajar Wati; Elvi Sunita; Andi Diah Kuswanto
Vertex Vol. 15 No. 2 (2026): June: Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/na9y0b02

Abstract

Decision Support Systems (DSS) in smart farming require methodologies capable of representing the holistic complexity of agricultural ecosystems. This study proposes a DSS framework based on unsupervised machine learning, specifically K-Means clustering, to automatically segment land profiles using IoT sensor records. The dataset consists of 500 global sensor data points covering seven essential environmental variables: soil moisture, pH, temperature, rainfall, humidity, sunlight duration, and the NDVI index. Through Principal Component Analysis (PCA) for dimensionality reduction and Silhouette Score evaluation, the system successfully identified and mapped seven land profiles with distinct microclimatic characteristics. Cross-tabulation analysis further demonstrates the principal novelty of this DSS, namely its ability to classify land into "Safe Zones" (Clusters 0, 3, and 4), which are characterized by Mild disease status and are suitable for Soybean, Cotton, and Maize, as well as "High-Risk Zones" (Clusters 1, 2, 5, and 6), which consistently correspond to Severe disease status. These findings indicate that a DSS based on environmental clustering is substantially more effective for crop selection recommendations and disease prevention than conventional predictive approaches. Ultimately, this framework provides farmers with actionable insights to optimize productivity and minimize agricultural risk
Co-Authors Andi Diah Kuswanto Embun Fajar Wati