Fake news or commonly known as a hoax has become one of the most visible cybercrime. Hoax news dissemination harms the social community, such as raising hatred towards something both individuals and groups. This paper is to classify amongst hoaxes and valid news utilizing Extreme Gradient Boosting (XGBoost) method in this research based on Indonesian news. The dataset used is Indonesian news about Indonesia itself and the world from 2015 to early 2020. The study used 500 news data including 250 valid news and 250 hoax news, divided into 80% training data and 20% test data. The result of this study shows that the machine learning model created using XGBoost has an accuracy value of 89%, with the precision value of 90% and recall value 80%.

AbstrakNotasi balok secara resmi dipakai sebagai standar notasi musik secara internasional, dan sering dijumpai pada partitur -partitur baik untuk alat musik maupun vokal. Di indonesia, penggunaan notasi angka lebih banyak digunakan dan dipahami, karena proses pembelajaran notasi balok yang tidak mudah, dan membutuhkan waktu untuk pengenalan tiap -tiap simbol dan pengertiannya. Teknologi pengenalan pola memungkinkan untuk mengenal pola dari notasi balok. Perangkat lunak yang dipakai untuk pengembangan sistem adalah Matlab, memanfaatkan jaringan syaraf tiruan dengan menerapkan metode backpropagation untuk mengenali pola notasi balok. Backpropagation tergolong metode supervised learning, dimana sistem akan diberikan pelatihan terlebih dahulu, dan kemudian sistem dapat memahami dan mengidentifikasi pola berdasarkan pengetahuan yang didapat. Hasil akhir menunjukkan bahwa sistem mampu mengenali pola dari notasi -notasi yang telah dipelajari sebelumnya dengan presentase tertinggi sebesar 91,20%. Kata Kunci: backpropagation, jaringan syaraf tiruan, notasi balok, pengenalan pola. AbstractThe beam notations is officially used as the standard of international music notation, and is often found in scores for both musical instruments and vocals. In Indonesia, the use of numerical notation is more widely used and understood, because the learning process of notation beams is not easy, and takes time for the introduction of each symbol and its meaning. The pattern recognition technology makes it possible to recognize the pattern of the beam notations. The software used for system development is Matlab, utilizing artificial neural network using backpropagation method to recognize the pattern of beam notation. Backpropagation is a supervised learning method, where the system will be given the training first, and then the system can understand and identify patterns based on the knowledge gained. The final result shows that the system is able to recognize patterns from notations that have been previously studied with the highest percentage of 91.20%.