Classification of Subsidized Organic Fertilizer Stock Rations using Based of Website Naive Bayes Method (Farmers' Groups in Sumberagung Village) - Fakiki, Achmad Rizki Nur; Rohman, M. Ghofar; Zamroni, M. Rosidi (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2025)
The distribution of subsidized organic fertilizer to farmers often encountered challenges, such as unequal allocation, mismatches between the amount requested and the amount distributed, and a manual and subjective selection process. Therefore, this research aimed to develop a web-based classification system to assist in the fair and automated determination of fertilizer allocation. The system was built using the Naive Bayes classification method, with input parameters including fertilizer type, land area, and the amount of fertilizer requested. The training dataset was derived from historical application data that had been evaluated by the farmers’ group leader, with decisions categorized as accepted or rejected, and classification results labeled as “Insufficient”, “Sufficient”, or “Excessive”. The system was developed using PHP as the programming language and MySQL as the database, and was tested using the black-box testing method to evaluate the functional accuracy of each feature. The test results indicated that the system operated as intended and successfully classified fertilizer application data. Additionally, the system provided visual representation of classification results in bar chart format, facilitating administrators in analyzing distribution trends. With this system, the fertilizer distribution process was expected to become more transparent, efficient, and objective.