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This study aims to analyze the factors that influence students' interest in continuing their education to college using a machine learning approach. Data was collected through an online questionnaire completed by 727 students between July 27 and August 22, 2025, covering 23 variables consisting of respondent identity (gender, grade level, major) as well as internal and external factors such as parental support, learning motivation, and preferred type of college. The data preparation stage was carried out through column cleaning, deletion of empty data, encoding of categorical variables, and division of the dataset into 80% training data and 20% test data. The Naive Bayes algorithm of the CategoricalNB type was used because it was suitable for the categorical nature of the data. The evaluation results showed that the model was able to predict student interest with 96% accuracy. For the class of students interested in continuing their studies, the precision, recall, and F1-score values were above 0.95, while the performance in the class of students who were not interested was slightly lower due to the smaller amount of data. These findings show that Naive Bayes is proven to be effective and reliable in classifying students' interest in continuing their studies and can be the basis for decision-making in designing more targeted educational strategies.

This study aims to analyze and compare the performance of the Naive Bayes, K-Nearest Neighbors (KNN), and Decision Tree algorithms in predicting the purchase intention of e-commerce visitors using the Online Shoppers Purchasing Intention Dataset, which consists of 12,330 records and 18 variables, with the Revenue variable serving as the classification target. The preprocessing stage involved transforming categorical and boolean variables into numerical form, standardizing features using StandardScaler, and splitting the dataset into 80 percent training data and 20 percent testing data. Model evaluation was conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics, and was further strengthened by 10-fold cross-validation to obtain more stable results. The findings indicate that KNN achieved the highest accuracy of 0.866180, while Naive Bayes produced the highest recall value of 0.690998 and the highest ROC-AUC value of 0.821696. Meanwhile, Decision Tree demonstrated relatively balanced performance with an accuracy of 0.857259 and an F1-score of 0.571776, whereas the cross-validation results identified KNN as the model with the highest average accuracy of 0.8770. These findings suggest that the selection of a classification model for purchase intention prediction cannot rely solely on a single evaluation metric, as each algorithm possesses different strengths. Therefore, a comparative approach among algorithms can help determine the most suitable model for supporting consumer behavior analysis on e-commerce platforms.

Neuro Associtive Conditioning Kepolisian Republik Indonesia (NAC POLRI) mempunyai tujuan untuk mengembalikan semangat dan teladan yang telah diwariskan oleh para pejuang dan para pahlawan Indonesia melalui training Pendidikan Karakter.  Kemerosotan bangsa Indonesia, khususnya para generasi penerus bangsa menjadikan bangsa Indonesia terpuruk.  Generasi muda menjadi loyo seperti tak bertenaga di alam Indonesia yang subur dan gemah ripah lohjinawi.  Perkembangan Teknologi Informasi menjembatani Rencana dan kiprah NAC POLRI melalui media online berupa website.  Melalui website nacpolri.org diharapkan semua tujuan dan rencana untuk membuat kembali generasi muda memiliki karakter bangsa Indonesia akan tercapai.  Proses pembuatan website yang dibuat melalui rencana yang benar, develop web yang benar, cara mengisi artikel yang benar dan cara memelihara website yang benar menjadi nilai tambah website nacpolri.org.  Website nacpolri.org menjadi besar dan disukai pengunjung karena menggunakan cara Search Engine Optimization (SEO) dan selalu di evaluasi perkembangannya.  Sehingga tujuan menjadikan bangsa ini kembali memiliki Pendidikan Karakter yang kuat akan bisa diwujudkan.