Banyak hal dalam dunia ini yang merupakan implementasi dari graph coloring, karena model-modelnya sangat bermanfaat untuk aplikasi yang luas, seperti  pendaftaran dan proses penjadwalan data instruktur berbasis web ini, Tujuan dari penelitian ini adalah untuk menunjang proses pelatihan virtual dengan menghasilkan jadwal pelatihan secara otomatis dalam seminggu. Terdapat jenis program pelatihan yang berdurasi 1 jam, 2 jam dan 3 jam. Sebelumnya instruktur harus mendaftarkan diri terlebih dahulu dan menentukan berapa jam sesi pelatihannya serta ketersediaan jadwal yang kosong. Penerapan graph coloring dapat membantu merumuskan jadwal instruktur agar tidak berbenturan dengan jadwal instruktur lainnya. Dari hasil penelitian ini yaitu Setiap instruktur yang mengajar dalam seminggu dipilih oleh sistem dengan jumlah slot waktu total dalam seminggu adalah 8x6=48jam. Jika jumlah total jam mengajar para pendaftar mencapai 60, 61 atau 62 jam maka sistem akan menutup secara otomatis.
The following credit card records were used in this study of 284.807 transactions made by credit card holders in Europe for two days from the Kaggle dataset. This is a very poor data set, having 492 transactions, an imbalance of only 0.172% of the 284.807 transactions. The purpose of this study is to obtain the best model and then simulate it by electronically detecting unauthorized financial transactions in bank payment systems. The dataset for this study is unbalanced class data with 99.80% for the major class and 0.2% for the minor class. This type of class-imbalanced data problem is solved by applying method a combination of minority oversampling techniques using Synthetic Minority Oversampling Technique (SMOTE). To determine the most appropriate and accurate classification in solving class balance problems, comparisons were made with the Random Forest Classifier (RFC), Logistic Regression (LGR), and Gradient Boosting Classifier (GBC) algorithms. The test results in this study are the Random Forest Classifier (RFC) algorithm is better than other algorithms because it has the highest accuracy the percentage of data-train is 100% and data-test is 99.99% and the evaluation of the AUC score as a result of algorithm testing is 0.9999.
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