Classification of Weather Conditions in Medan City: Application of the C4.5 Decision Tree Algorithm as a Prediction Accuracy Solution

Safrizal; Lili Tanti; Ramadani; Rabiana Saragih; Suci Sanjaya

doi:10.59934/jaiea.v5i2.2128

Authors

Safrizal Politeknik Gihon
Lili Tanti Universitas Potensi Utama
Ramadani Universitas Muhammadiyah Asahan
Rabiana Saragih Universitas Muhammadiyah Asahan
Suci Sanjaya Universitas Muhammadiyah Asahan

DOI:

https://doi.org/10.59934/jaiea.v5i2.2128

Keywords:

Weather, Decision trees, Classification, C4.5, Algorithm

Abstract

The Accurate weather predictions play an important role in supporting community activities and economic sectors, especially in cities with varied weather patterns like Medan. Erratic weather can have a significant impact on the transportation sector, agriculture and daily community activities. This research aims to develop a weather condition classification model using the Decision Tree C4.5 algorithm, which is known to be reliable in handling numerical and categorical data. The historical data used includes temperature, humidity, wind speed and rainfall, which are analyzed using the CRISP-DM approach, including the stages of business understanding, data preparation, modeling and evaluation. This model classifies weather into four categories: sunny, cloudy, drizzly, and rainy. Based on test results, the model succeeded in achieving an accuracy level of 100%, showing its reliability in predicting the weather in Medan. Apart from providing accurate weather information, this model is also able to support data-based decision making, such as transportation planning and risk mitigation in the agricultural sector. These findings show the great potential of using the C4.5 algorithm in analyzing and predicting weather in Indonesia, as well as being a practical solution for facing complex weather challenges. This research provides an important contribution in supporting the development of data-based weather prediction technology that can be implemented widely for the benefit of society and the industrial sector

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