Penerapan Algoritma Decision Tree Untuk Memprediksi Pengelolaan Inventaris Sarana Pembelajaran Kampus
DOI:
https://doi.org/10.47065/bulletincsr.v6i1.889Keywords:
Campus Inventory; Gain Ratio; Cross Validation; Decision Tree; Average AccuracyAbstract
UBSI as an educational institution that has learning support facilities must be able to manage campus inventory effectively. This study aims to determine the management of asset management that needs to be done, both in the form of routine maintenance and updating of goods. The UBSI Jatiwaringin branch campus only makes reports on the condition of inventory items, so it cannot determine whether the reported inventory data is updated or repaired, so far it is not known which items are prioritized based on their level of importance. The data will then be followed up by the main campus to check the inventory data report. The method used to determine inventory predictions is the Decision Tree Algorithm which has priority, location, condition, frequency, and prediction attributes. As targets in the decision tree are prediction attributes that have maintenance or renewal classes. Determination of inventory data predictions by calculating the entropy, gain, gain info, and gain ratio values ??of each attribute and resulting in the Priority attribute being the root node in the formed decision tree. This indicates that the priority attribute has a strong influence in determining whether an item is included in the maintenance or renewal class. Based on testing results using RapidMiner software with the K-Fold Cross Validation method, the Decision Tree algorithm can generate a decision model with an average accuracy of 86.67% in campus inventory management. The results of this study are expected to be useful for Jatiwaringin Campus administrators to conduct initial inspections without waiting for repairs from the main campus.
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