Nowadays, computer networks are widely used to exchange valuable and confidential data information between servers to computers or cellular devices. Access to user control and use of software or hardware as a firewall often experience security problems. Unauthorized access to information through computer networks continues to occur and tends to increase. This study examines the attack detection mechanism by using three data mining algorithms based on particle swarm optimization (PSO), namely PSO-K Nearest Neighbor, PSO-Random Forest, and PSO-Decision Tree in the Canadian Institute for Cybersecurity Dataset (CICIDS2017). The initial experiment showed that the approach using the PSO-RF method was able to produce the highest accuracy of attack detection. Accuracy values generated using the PSO-RF algorithm with a combination of the number of trees and maximal depth = 20 in the CICIDS2017 dataset are intact higher than other proposed algorithms. The highest accuracy of attack detection in the CICIDS2017 dataset is intact, which is 99.76%. In the CICIDS2017 dataset 50% Benign and 50% Attack it turns out that the PSO-RF algorithm with a combination of the number of trees and maximal depth = 20 also gets the highest accuracy value of 99.67%.

Precision marketing is the companys ability to offer products specifically made to customers. This decision can give the company the ability to attract customers to always buy continuously. This study presents a trend model for accurately predicting monthly supply quantities / The method used in the first stage is the RFM (Recency, Frequency, Monetary) method for selecting attributes to group customers into different groups. The output of the first stage is clustered using the K-Means Algorithm. The output of clustering is then classified using the Decision Tree and compared with the K Nearest Neighbor method. The dataset that is processed is sales data from Syifamart As-Syifa Boarding School in Subang with 351,158 rows of data. The clustering process produces 4 optimal clusters. The four clusters are then classified using the Decision Tree algorithm to determine the potential and non-potential characteristics of each customer.

Aplikasi e-Library Fakultas Teknik adalah sebuah aplikasi yang digunakan untuk membantu bagian Perpustakaan melakukan pengelolaan data dan transaksi pada Perpustakaan Fakultas Teknik Universitas Muhammadiyah Tangerang. Aplikasi ini digunakan oleh petugas agar lebih mudah dalam melakukan pencatatan peminjaman dan pengembalian buku dalam memantau buku yang tersedia dan dipinjam. Aplikasi ini dibuat dengan menggunakan metode pengembangan sistem Software Development Life Cycle (SDLC) dengan model Rapid Application Development (RAD). Pembangunan aplikasi ini berupa aplikasi Android yang dapat diakses oleh anggota perpustakaan dan terhubung dengan aplikasi Website yang diakses oleh petugas perpustakaan. Aplikasi angroid dibangun menggunakan Bahasa pemrograman PHP dengan Framework Codeigniter dan aplikasi Android menggunakan Bahasa pemrograman Dart dengan Framework Flutter. Sedangkan dalam metode pengujian sistem menggunakan pengujian blackbox testing.Kata kunci: Perpustakaan, Aplikasi e-Library, Android, RAD