2024-10-27 –, CLASS #2 - 4B
Language: Indonesian
Agricultural commodities such as shallots, red chili, and cayenne pepper are highly produced, serving not only the local community but also neighboring districts and cities. However, farmers often face income losses as their selling prices are dictated by collectors, deviating from the actual market prices. This discrepancy negatively impacts farmers' productivity. Our proposed research aims to address this issue by developing a predictive system for commodity selling prices using data mining techniques, specifically multiple linear regression. This method will analyze factors such as rainfall and total production to forecast prices, enabling us to provide farmers with accurate selling price recommendations for specific periods. By aligning farmers' selling prices with market trends, this system aims to minimize their losses and enhance productivity. This proposal outlines the development and implementation of this predictive system, highlighting its potential benefits for the agricultural sector.
In this talk, we will explore the development of a predictive system for agricultural commodity prices using data mining techniques, focusing on shallots, red chili, and cayenne pepper. Farmers often face significant income losses due to discrepancies between market prices and prices set by intermediaries. Our system aims to address this issue by providing accurate price recommendations, thereby aligning farmers' selling prices with market trends and enhancing their productivity.
We will delve into the methodology of our predictive system, which employs multiple linear regression to analyze key factors such as rainfall and total production. By identifying patterns in historical data, our system can forecast commodity prices for specific periods, offering valuable insights to farmers.
Join us to learn how data-driven solutions can revolutionize the agricultural sector, empower farmers with better price predictions, and ultimately lead to increased productivity and profitability. This talk is ideal for data scientists, agricultural researchers, and anyone interested in the application of machine learning in real-world scenarios.
Arsy Opraza Akma is currently a Curriculum Developer at Dicoding, write a high material content to help student learn programming. Currently, the course is enrolled by hundreds of thousands of students.
I’m a passionate software engineer. I participated in mentorship programs such as Bangkit Academy as Advisor Capstone Project. I’m long life learner, i love to learn about software engineering, and self development.
I actively engaged in several tech communities and regularly shares insights through GitHub, LinkedIn, and his Medium blog.