Novi Ari Wardani
Universitas Putra Bangsa

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Rancangan Bangun User Interface pada Pembuatan Website Jual Beli Baju pada Toko Tuku Fashion dengan Menggunakan Metode Waterfall Nur Azizah; Novi Ari Wardani; Lolanda Hamim Annisa
Technology and Informatics Insight Journal Vol. 5 No. 1 (2026): TIIJ
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/3g8g4m37

Abstract

Kemajuan teknologi informasi telah mendorong transformasi dalam sektor perdagangan, termasuk penjualan pakaian secara online. Website dirancang untuk mempermudah proses penjualan, meningkatkan efisiensi operasional, serta memberikan pengalaman belanja yang lebih nyaman bagi pelanggan. Penelitian ini bertujuan untuk merancang dan mengembangkan website penjualan pakaian berbasis web yang efisien, mudah digunakan, dan mendukung kebutuhan pelanggan serta pebisnis.Metodologi pengembangan sistem menggunakan pendekatan waterfall yang meliputi identifikasi masalah, tinjauan literatur, pengumpulan data, analisis kebutuhan, perancangan sistem, dan implementasi. Sistem yang dirancang mencakup fitur utama seperti katalog produk, keranjang belanja, sistem pembayaran, dan pengelolaan data pelanggan. Untuk memvisualisasikan alur kerja dan struktur sistem, digunakan diagram UML, termasuk use case, activity diagram, dan sequence diagram.Hasil penelitian menunjukkan bahwa sistem ini mampu meningkatkan efisiensi operasional dan memperluas jangkauan pemasaran produk. Dengan adanya platform ini, konsumen dapat dengan mudah melakukan pemesanan tanpa harus datang langsung ke toko, sementara pelaku usaha dapat mengelola transaksi dan inventaris dengan lebih baik. Sistem ini diharapkan menjadi solusi efektif dalam mendukung aktivitas bisnis di era digital.   Advances in information technology have driven transformation in the trade sector, including online clothing sales. The web-based information system is designed to simplify the sales process, increase operational efficiency, and provide a more comfortable shopping experience for customers. This research aims to design and develop a web-based clothing sales information system that is efficient, easy to use, and supports the needs of customers and business people. The system development methodology uses a waterfall approach which includes problem identification, literature review, data collection, needs analysis, system design, and implementation. The system design includes main features such as product catalogs, shopping carts, payment systems, and customer data management. To visualize the workflow and system structure, UML diagrams are used, including use cases, activity diagrams, and sequence diagrams. The research results show that this system is able to increase operational efficiency and expand product marketing reach. With this platform, consumers can easily place orders without having to come directly to the store, while business people can manage transactions and inventory better. This system is expected to be an effective solution in supporting business activities in the digital era.
Klasifikasi Curah Hujan Harian Menggunakan Convolutional Neural Network (CNN) 1D Laili Meifa Ayuningtias; Muhammad Nizar Asagaf; Novi Ari Wardani; Anggit Gusti Nugraheni
Technology and Informatics Insight Journal Vol. 5 No. 2 (2026): TIIJ
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/cnskqe82

Abstract

Daily rainfall is an important climatic element for agriculture, water resource management, and hydrometeorological disaster mitigation. The temporal variability of rainfall makes its classification a challenging task. This study aims to develop a daily rainfall classification model using a one-dimensional Convolutional Neural Network (1D CNN) based on rainfall (RR) data from the Climatology Station of D.I. Yogyakarta for the period from July 1, 2025, to June 30, 2026, comprising 364 observations. The classification uses two classes, namely No Rain (RR = 0 mm) and Rain (RR > 0 mm), with a 30-day sliding window. The 1D CNN architecture consists of two convolutional blocks, batch normalization, ReLU, max pooling, global average pooling, a dense layer, L2 regularization, and dropout. Evaluation on 67 test samples yielded an accuracy of 74.63%, balanced accuracy of 66.84%, macro F1-score of 0.671, and ROC-AUC of 0.777. However, five-fold TimeSeriesSplit resulted in a balanced accuracy of 50.00%. These results indicate that the model performance is not yet stable due to the limited one-year dataset and the use of a single predictor variable.