<![CDATA[Determining the ripeness level of avocado fruit is an important factor in distribution, marketing, and consumption processes. Conventional ripeness assessment is often subjective and dependent on human experience, which can lead to inconsistent results. This study aims to develop an avocado ripeness prediction system using the Logistic Regression algorithm based on physical and visual fruit characteristics. The dataset consists of 1,250 avocado samples with features including firmness, color attributes, tapping sound, weight, and fruit size. Data preprocessing involved cleaning, normalization of numerical features using StandardScaler, and categorical feature transformation using one-hot encoding. The experimental results show that the proposed model achieved an accuracy of approximately 77% in classifying avocado ripeness into ripe and unripe categories, indicating that Logistic Regression is a lightweight and efficient approach for numerical-based ripeness prediction systems.]]>
Copyrights © 2026
