Pengembangan Chatbot Berbasis Website Untuk Customer Service Di Cluster Puri Surya Jaya Dengan Pendekatan Deep Learning

Putri, Naftali Salsabila Kanaya (2024) Pengembangan Chatbot Berbasis Website Untuk Customer Service Di Cluster Puri Surya Jaya Dengan Pendekatan Deep Learning. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Cluster Puri Surya Jaya, sebuah lingkungan perumahan yang tengah berkembang pesat dengan beragam jenis properti dan fasilitas untuk penghuninya Dengan pertumbuhan pesat jumlah penghuni, masalah kompleksitas administratif, pembayaran Iuran Pengelolaan Lingkungan (IPL), dan ketidakjelasan regulasi menjadi perhatian utama. Keluhan yang disampaikan langsung lewat group chat pun sering terlewat dan tidak sengaja terabaikan, menyebabkan ketidakoptimalan dalam menanggapi permasalahan. Penelitian ini bertujuan untuk mengatasi kompleksitas administratif dan meningkatkan komunikasi di lingkungan Cluster Puri Surya Jaya melalui pengembangan aplikasi website dan chatbot sebagai customer service. Dengan melibatkan proses akuisisi data dari 860 pertanyaan yang dikategorikan ke dalam 78 kategori informasi, dataset yang terstruktur ini kemudian dilakukan tahap preprocessing dengan metode Natural Language Processing (NLP) untuk selanjutnya dilakukan model training menggunakan arsitektur LSTM. Model ini berhasil mencapai akurasi rata-rata 91% pada data training dan 81% pada data testing setelah dilatih dengan batch size 32 selama 250 epochs. Hasil evaluasi menggunakan classification report menunjukkan presisi (precision) sebesar 96%, recall sebesar 91%, dan f1-score sebesar 92%, menunjukkan kemampuan chatbot dalam mengelola berbagai jenis pertanyaan dengan baik. Evaluasi akurasi chatbot dengan metode user validation menunjukkan bahwa secara keseluruhan chatbot mampu memberikan jawaban yang sesuai dalam 89% pertanyaan yang diajukan oleh user. Integrasi chatbot dengan website dilakukan untuk meningkatkan aksesibilitas dan responsivitas layanan, memungkinkan penghuni untuk mendapatkan informasi tentang IPL, regulasi, dan mengajukan keluhan dengan lebih efisien.
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Cluster Puri Surya Jaya is a rapidly growing residential environment with diverse types of properties and facilities for its residents. The rapid increase in population has brought about issues of administrative complexity, Environmental Management Fee (IPL) payments, and regulatory uncertainties as primary concerns. Complaints conveyed directly through group chats often get overlooked and unintentionally neglected, leading to suboptimal responses to issues. This research aims to address administrative complexities and enhance communication within Cluster Puri Surya Jaya through the development of a website application and a chatbot as customer service. Involving the acquisition process of data from 860 questions categorized into 78 information categories, this structured dataset underwent preprocessing using Natural Language Processing (NLP) methods before model training using LSTM architecture. The model achieved an average accuracy of 91% on training data and 81% on testing data after training with a batch size of 32 over 250 epochs. Evaluation using a classification report showed a precision of 96%, recall of 91%, and an f1-score of 92%, indicating the chatbot's capability to manage various types of questions effectively. User validation testing revealed that overall, the chatbot provided suitable answers in 89% of user inquiries. Integration of the chatbot with the website was aimed at improving service accessibility and responsiveness, allowing residents to obtain information on IPL, regulations, and submit complaints more efficiently.

Item Type: Thesis (Other)
Uncontrolled Keywords: Customer Service, Chatbot, Website, NLP, LSTM
Subjects: T Technology > T Technology (General)
T Technology > T Technology (General) > T57.5 Data Processing
Divisions: Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information Technology > 59201-(S1) Undergraduate Thesis
Depositing User: Naftali Salsabila Kanaya Putri
Date Deposited: 25 Jul 2024 04:25
Last Modified: 25 Jul 2024 04:25
URI: http://repository.its.ac.id/id/eprint/108793

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