RAMADHAN, KHAIRUNNISA (2026) KLASIFIKASI LAPORAN DARURAT PADA SISTEM DORMITORY BERBASIS WEBSITE MENGGUNAKAN ALGORITMA NAIVE BAYES DAN CHATBOT NLP. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Dormitory management in a university environment plays a crucial role in supporting student comfort, safety, and operational efficiency. However, traditional dormitory management systems often face several challenges, including administrative delays, ineffective communication, and slow responses to emergency situations. This study aims to design and implement a web-based Dormitory Management System integrated with a Natural Language Processing (NLP)-based chatbot and an Emergency Alert System (EAS) utilizing the Naive Bayes algorithm. The NLP chatbot is designed to provide automated information services to dormitory residents on a 24/7 basis, while the Emergency Alert System is responsible for receiving and classifying emergency reports quickly and accurately. The Naive Bayes algorithm is applied to classify emergency types, such as fire, medical, and security incidents, based on textual input from reports. This research is conducted as a case study at Universitas Mercu Buana. The results of this study are expected to improve dormitory management efficiency, enhance internal communication, and support faster and more accurate decision-making in emergency handling. Keywords: Sistem Dormitory, Asrama Management, Chatbot with NLP, Emergency Alert System, Naive Bayes, Text Classification. Pengelolaan asrama di lingkungan universitas memiliki peran penting dalam mendukung kenyamanan, keamanan, dan efektivitas aktivitas mahasiswa. Namun, sistem manajemen asrama yang masih bersifat manual sering menghadapi berbagai permasalahan, seperti keterlambatan administrasi, kurang efektifnya komunikasi, serta lambannya penanganan kondisi darurat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Dormitory Berbasis Website yang terintegrasi dengan Chatbot berbasis Natural Language Processing (NLP) dan Emergency Alert System (EAS) menggunakan Algoritma Naive Bayes. Chatbot NLP digunakan untuk memberikan layanan informasi otomatis kepada penghuni asrama selama 24 jam, sementara Emergency Alert System berfungsi untuk menerima dan mengklasifikasikan laporan darurat secara cepat dan tepat. Algoritma Naive Bayes diterapkan untuk mengklasifikasikan jenis kondisi darurat, seperti kebakaran, medis, dan keamanan, berdasarkan input teks laporan. Studi kasus penelitian ini dilakukan di Universitas Mercu Buana. Hasil dari penelitian ini diharapkan dapat meningkatkan efisiensi manajemen asrama, mempercepat komunikasi internal, serta mendukung pengambilan keputusan yang lebih cepat dan akurat dalam penanganan keadaan darurat. Kata kunci: Sistem Dormitory, Manajemen Asrama, Chatbot NLP, Emergency Alert System, Naive Bayes, Klasifikasi Teks.
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