Implementation of Chatbot on University Website Using RASA Framework

Fauzia, Lia; Hadiprakoso, Raden Budiarto; Girinoto,

doi:10.1109/isriti54043.2021.9702821

Cited by 8 publications

(3 citation statements)

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“…On the other hand, conversations and decision-making are handled by Rasa Core [2]. Various researchers have developed chatbots using the Rasa framework, such as in ( [23], [24]). Rasa's most recent release is called Rasa X, which is a tool that supports the creation, enhancement, and implementation of AI assistants driven by the Rasa framework.…”

Section: Rasa Open Sourcementioning

confidence: 99%

The Chatbot Systems for Mathematics Education using Knowledge Graphs and Named Entity Recognition

Salsabila

2024

Arch. Sci.

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The development of chatbot systems for mathematics courses in elementary school has gained significant attention due to their potential to enhance the learning experience. This study proposes a novel approach that combines knowledge graph (KG) and Named Entity Recognition (NER) methods using Neo4j and SpaCy within the Rasa Open-Source v3.0 platform as chatbot frameworks. The knowledge graphs represent mathematical concepts and their relationships, enabling the chatbot to provide accurate and relevant responses to user queries. The NER SpaCy is employed to identify and extract mathematical entities from user inputs, ensuring a precise understanding of the context. The integrations of Neo4j and NER using SpaCy with Rasa Open-Source v3.0 facilitate efficient information retrieval and improve the conversational abilities of the chatbot. Experimental results demonstrate the effectiveness of the proposed approach, showcasing its potential as an educational tool for fifth-grade students in elementary schools.

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Section: Rasa Open Sourcementioning

confidence: 99%

The Chatbot Systems for Mathematics Education using Knowledge Graphs and Named Entity Recognition

Salsabila

2024

Arch. Sci.

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“…Student learning and experimentation with chatbots rose after Covid-19 [155]. This chatbot system gives parents student growth information [170] to improve school-parent relations [157].…”

Section: Chatbot For Educationmentioning

confidence: 99%

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Suryanto,

Wibawa,

Hariyono

et al. 2023

IJRCS

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Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.

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“…Berdasarkan permasalahan tersebut, solusi yang dapat ditawarkan adalah penerapan chatbot pada Helpdesk TIK. Chatbot merupakan sebuah sistem dialog yang dibuat dengan kecerdasan buatan untuk dapat meniru dan melakukan percakapan dengan manusia (Fauzia et al, 2021). Chatbot dapat beroperasi 24 jam non-stop, sehingga dapat merespon kapan saja civitas academica UB membutuhkan bantuan.…”

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Klasifikasi Intensi dengan Metode Ling Short-Term Memory pada Chatbot Bahasa Indonesia

Al Farisi,

Perdana,

Adikara

2024

JTIIK

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E-government merupakan sebuah konsep pemerintahan yang menyelenggarakan layanan publik secara digital yang didukung oleh teknologi informasi, sehingga menjadi lebih prima. Salah satu bentuk layanan publik digital yang umum ada di berbagai sektor adalah helpdesk. Helpdesk memungkinkan pengguna dapat bertanya atau melaporkan sesuatu untuk kemudian dijawab oleh staf. Permasalahan yang ada adalah ketersediaan staf yang hanya dapat menjawab pada jam kerja, sedangkan pengguna bisa jadi menemukan permasalahan yang urgent di luar jam kerja. Oleh karena itu, solusi yang ditawarkan adalah penerapan chatbot, sehingga dapat melayani kapan pun, sekaligus meringankan kerja dari staf. Penelitian ini mencoba untuk merancang salah satu komponen pada chatbot, yaitu model klasifikasi intensi, dengan metode Long Short-Term Memory. Data yang digunakan merupakan 501 teks riwayat pertanyaan dari database Helpdesk TIK UB yang termasuk ke dalam 7 kelas intensi yang ditentukan. Data akan melalui beberapa tahap prapemrosesan sebelum kemudian dilakukan pemodelan dan beberapa pengujian. Tahap pengujian meliputi pemilihan embedding yang digunakan, pemilihan teknik augmentasi data, dan penyetelan hyperparameter. Hasil dari keseluruhan pengujian, didapatkan model terbaik yang mampu menghasilkan akurasi sempurna untuk data latih dan data uji, serta loss 0,004 untuk data latih dan 0,044 untuk data uji. Abstract E-government is a government concept that organizes digital public services supported by information technology, so that they become more excellent. One form of digital public service that is common in various sectors is the helpdesk. Helpdesk allows users to ask questions or report something to be answered by staff. The problem that exists is the availability of staff who can only answer during working hours, while users may find urgent problems outside of working hours. Therefore, the solution offered is the implementation of a chatbot, so that it can serve at any time, while easing the work of staff. This study attempts to design one of the components of the chatbot, namely the intention classification model, using the Long Short-Term Memory method. The data used are 501 question history texts from the Helpdesk TIK UB database which fall into the 7 specified intensity classes. The data will go through several pre-processing stages before then being modeled and tested. The testing phase includes selecting the embedding used, data augmentation techniques selection, and hyperparameter tuning. The best model is obtained which can produce perfect accuracy for training data and test data, as well as a loss of 0.004 for training data and 0.044 for test data.

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Implementation of Chatbot on University Website Using RASA Framework

Cited by 8 publications

References 10 publications

The Chatbot Systems for Mathematics Education using Knowledge Graphs and Named Entity Recognition

The Chatbot Systems for Mathematics Education using Knowledge Graphs and Named Entity Recognition

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Klasifikasi Intensi dengan Metode Ling Short-Term Memory pada Chatbot Bahasa Indonesia

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