Penerapan Finite State Automata pada Desain Vending Machine Masker dan Hand Sanitizer

The COVID-19 pandemic that has occurred for almost 2 years has hit this country caused by a mutation by the SARS-CoV virus, making changes in people's attitudes and behavior to become more concerned about cleanliness and health. In this case, the use of masks and hand sanitizers is very basic and a primary need during this pandemic. Vending Machine is a form of technological development that is used to sell or provide various kinds of products. Finite State Automata (FSA) is applied to vending machines for masks and hand sanitizers. FSA is a mathematical model that can accept input and output from the same state. The method used in this study consists of four stages, the first is knowledge of the FSA, the second is the design of the system diagram in this case the researcher uses the JFLAP application in making the FSA diagram, the third stage is the FSA test by describing the transition table and for the test it is still using JFLAP and the last stage is the VM design design process, in this case the researcher tries to design a VM using a display that is easy to use by buyers and designs the payment system with 2 methods, namely cash and digital money. The conclusion obtained from this study is that the application of the FSA concept to VM masks and hand sanitizers can make transactions of eight products, namely five types of mask products and three types of hand sanitizer products. this time trying to sell two different types of products

Analisis Algoritma KNN Berbasis Feature Selection untuk Memprediksi Nasabah Pengguna Deposito melalui Pemasaran Langsung

Sebuah bank menggunakan teknik pemasaran langsung dalam menargetkan segmen nasabah dengan cara menghubungi nasabah tersebut untuk memenuhi tujuan tertentu. Setelah menghubungi nasabah, bank mendapatkan informasi apakah nasabah tersebut sudah berlangganan produk yang ditawarkan oleh bank atau belum. Salah satu produk yang ditawarkan oleh bank antara lain yaitu deposito. Dari banyaknya informasi nasabah yang dikumpulkan, bank mampu menawarkan produk dan layanan kepada nasabah. Kemampuan tersebut dapat menggunakan teknologi data mining, seperti tujuan dibuatnya penelitian ini yaitu memprediksi nasabah yang berlangganan deposito dengan algoritma K-Nearest Neighbor (KNN) dan feature selection yang diproses menggunakan tools Anaconda dan bahasa pemrograman python. Dari hasil penelitian yang diperoleh, akurasi dari penggunaan algoritma K-Nearest Neighbor (KNN) sebesar 74,37% dengan nilai K=9, sedangkan akurasi algoritma klasifikasi K-Nearest Neighbor dengan menggunakan feature selection sebesar 89,72% dengan nilai K=3, sehingga didapat selisih peningkatan akurasi sebesar 15,35%