Sepak bola merupakan olahraga paling popular dan paling digemari di seluruh dunia. berdasarkan hasil survei yang dilakukan oleh Fédération Internationale de Football Association (FIFA) pada tahun 2001 menyatakan bahwa sepakbola adalah olahraga paling populer dimainkan hari ini. Survei ini menunjukkan bahwa lebih dari 240 juta orang memainkan olahraga sepak bola di lebih dari 200 negara di hampir setiap bagian dari dunia. Salah satu posisi dalam sepak bola adalah gelandang pemain tengah atau dalam Bahasa Inggris disebut dengan midfielder Posisi gelandang sepak bola terdiri dari beberapa tipe yaitu CMF (central midfielder), AMF (attacking midfielder), DMF (defensive midfielder), RMF ( right midfielder), LMF (left midfielder). Namun, dalam penelitian ini, hanya tiga tipe yang akan digunakan yaitu CMF, DMF, dan AMF. Pada penelitian ini akan diangkat permasalahan yaitu: bagaimana seorang pemain gelandang dapat diklasifikasi kedalam posisi atau tipe gelandang yang tepat? Pendekatan yang digunakan yaitu menggunakan pendekatan klasifikasi menggunakan algoritma Naïve Bayes bertujuan untuk melakukan klasifikasi tipe gelandang sepak bola. Dari empat skenario yang dilaksanakan didapatkan bahwa hasil akurasi pada masing-masing scenario adalah sebesar 80%, 80%, 82.5%, dan 80.182%.
Today's complex decision-making solutions for intelligent manufacturing depend on the ability to be able to model a manufacturing system realistically, valid and consistent data integrated easily and in a timely manner, able to solve problems efficiently with computational effort to obtain optimal production and product quality optimizations continuously. When an organization uses a data-driven approach, it means that it makes strategic decisions based on data collection, analysis, and interpretations or insights. The purpose of this research is to analyze the business intelligence approach in optimizing print machines by speed, material and time. in this research, using the N-Beats is a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers and Recurrent Neural Networks (RNN). The novelty of this research is increasing machine speed using new insights by combining two deep learning methods. Observing and retrieving raw data from the printing machine process with sensors data for use and ensuring the justification of the addition of new methods. The result is expected to be able to provide new insights that can increase engine speed, the data based decision making provides businesses with the capabilities to generate real time insights and predictions to optimize their performance and provide confidence in decision making that are fast, precise and better.
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