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Assignment
Klasifikasi
Data Mining
Soil Suitability Classification for Crop Cultivation
A data mining project that classifies soil suitability for crops such as corn, rice, and chili across different regions. The project uses Naive Bayes and Random Forest algorithms and follows the CRISP-DM methodology to analyze whether the soil conditions in specific areas are suitable for cultivation.
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About this project
Soil Suitability Classification for Crop Cultivation is a data mining project developed during my 4th semester assignment to analyze whether soil conditions in certain regions are suitable for planting crops such as corn, rice, and chili. The goal of the project is to help identify land suitability for agricultural purposes based on soil characteristics and environmental factors.
The project applies two classification algorithms, Naive Bayes and Random Forest, to predict whether soil in specific areas (Region A, B, and C) is suitable or unsuitable for crop cultivation. The analysis process follows the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, including stages such as Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment.
Through this structured approach, the project demonstrates how data mining techniques can support agricultural decision-making by providing insights into soil suitability for different types of crops.