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A Web-Based Hotel Management System with Midtrans Payment Integration and Email Notification Features M. Galvin Prihardi Putra; Irwan; Bradika Almandin Wisesa
KHARISMA Tech Vol 21 No 2 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i2.677

Abstract

This study aims to design and develop a web-based Hotel Management System as part of the digitalization of operational processes at Hotel Letco In, which previously relied on manual procedures. The manual workflow caused several issues, including limited access to room availability, a high risk of data entry errors, and inefficiencies in reservation and reporting processes. The developed system includes modules for user registration and authentication, online room booking, room and facility management, payment integration using Midtrans, and automated email notifications through SMTP. In addition, the system provides a PDF-based transaction receipt feature to support administrative documentation. Functional testing using the Black Box method showed that all features operated according to requirements and supported more efficient hotel operations. Overall, the system improves reservation efficiency, enhances data accuracy, and provides a faster and more structured service experience for both users and hotel administrators.
SISTEM KLASIFIKASI SAMPAH ORGANIK, ANORGANIK, DAN BAHAN BERBAHAYA BERACUN (B3) Bradika Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Rahmat Lionza
KHARISMA Tech Vol 21 No 2 (2026): KHARISMATech Journal
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/kharismatech.v21i2.695

Abstract

Penelitian ini mengembangkan sistem klasifikasi sampah real-time untuk sampah organik, anorganik, dan bahan berbahaya beracun (B3) di Jalan Timah Sungailiat, Bangka Belitung, Indonesia menggunakan YOLOv11 untuk mengatasi pencemaran lingkungan akibat pengelolaan sampah yang tidak tepat. Dataset khusus sebanyak 5.000 gambar yang seimbang pada kondisi siang, senja, dan malam dikumpulkan untuk melatih YOLOv11 sehingga mampu menangani variasi pencahayaan. Metodologi meliputi pra-pemrosesan (ubah ukuran 640×640 piksel, normalisasi, augmentasi data), pelatihan YOLOv11 pada dataset seimbang, serta evaluasi menggunakan metrik mean Average Precision (mAP@0.5), presisi, recall, dan F1-score. Sistem mencapai mAP@0.5 sebesar 70%, presisi 69%, recall 70%, dan F1-score 0,70 dengan kecepatan 43 frame per detik (FPS) serta 102 GFLOPs sehingga dapat dijalankan pada GPU kelas menengah. Meskipun akurasi masih moderat karena variasi pencahayaan dan oklusi, kerangka kerja ini menawarkan solusi hemat biaya dan skalabel untuk pengelolaan sampah kota pintar, mengurangi tenaga sorting manual serta mendukung inisiatif daur ulang. Perbaikan mendatang akan fokus pada peningkatan performa malam hari dan penanganan.