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Produksi,  order , penjualan, dan pengiriman adalah serangkaian  event  yang saling terkait dalam industri manufaktur. Selanjutnya hasil dari  event  tersebut dicatat dalam  event log .  Complex Event Processing  adalah metode yang digunakan untuk menganalisis apakah terdapat pola kombinasi peristiwa tertentu (peluang/ancaman) yang terjadi pada sebuah sistem, sehingga dapat ditangani secara cepat dan tepat. Jaringan saraf tiruan adalah metode yang digunakan untuk mengklasifikasi data peningkatan proses produksi. Hasil pencatatan rangkaian proses yang menyebabkan peningkatan produksi digunakan sebagai data latih untuk mendapatkan fungsi aktivasi dari jaringan saraf tiruan. Penjumlahan hasil catatan  event log  dimasukkan ke  input  jaringan saraf tiruan untuk perhitungan nilai aktivasi. Ketika nilai aktivasi lebih dari batas yang ditentukan, maka sistem mengeluarkan sinyal untuk meningkatkan produksi, jika tidak, sistem tetap memantau kejadian. Hasil percobaan menunjukkan bahwa akurasi dari metode ini adalah 77% dari 39 rangkaian aliran event. Kata kunci :  complex event processing, event, jaringan saraf tiruan, prediksi peningkatan produksi, proses. Productions, orders, sales, and shipments are series of interrelated events within manufacturing industry. Further these events were recorded in the event log. Complex event processing is a method that used to analyze whether there are patterns of combinations of certain events (opportunities / threats) that occur in a system, so it can be addressed quickly and appropriately. Artificial neural network is a method that we used to classify production increase activities. The series of events that cause the increase of the production used as a dataset to train the weight of neural network which result activation value. An aggregate stream of events inserted into the neural network input to compute the value of activation. When the value is over a certain threshold (the activation value results from training process), the system will issue a signal to increase production, otherwise system will keep monitor the events. Experiment result shows that the accuracy of this method is 77% for 39 series of event streams. Keywords:  complex event processing, event, neural networks, process, production increase prediction.

Prediction of Increasing Production Activities using Combination of Query Aggregation on Complex Events Processing and Neural Network