Analisis Klasifikasi Jenis Layanan Ojek Online Menggunakan Algoritma Naive Bayes pada Dataset Dummy Ojol Transactions - Muhammad Fauzan Fadhilah; Hasbi Firmansyah; Wahyu Asriyani (Jejak digital: Jurnal Ilmiah Multidisiplin, 2025)
The rapid growth of online motorcycle taxi (ojol) services as part of the digital economy ecosystem has generated a substantial volume of diverse transaction data. This data holds significant potential for supporting analytical processes such as identifying service usage patterns, predicting user needs, and enabling data-driven decision-making. One essential analytical task is the classification of service types based on available transaction attributes. To address this need, this study conducts a classification analysis of online ride-hailing service types using the Naive Bayes algorithm on the “Ojol Transactions” dummy dataset. The research consists of several stages, including data preprocessing, handling of missing values, target attribute determination, model training, and performance evaluation using RapidMiner. The Naive Bayes algorithm was selected due to its efficiency in classifying structured data and its compatibility with simple probabilistic assumptions. The evaluation results show that the classification model achieved an accuracy of 100%, indicating that all test data were correctly classified. These findings demonstrate the strong potential of the Naive Bayes algorithm for analyzing ride-hailing transaction classifications, particularly when applied to datasets with clearly separated classes.