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All Journal International Journal of Electrical and Computer Engineering Lontar Komputer: Jurnal Ilmiah Teknologi Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Chemistry Indonesian Journal of Chemical Research Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Enterprise and Development (JED) Jurnal Ilmiah Profesi Pendidikan jurnal ilmu keolahragaan Charity : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Pengabdian Mandiri eProceedings of Engineering Jurnal Pengabdian Masyarakat Bangsa
Nurul Ikhsan
Universitas Telkom
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Journal : International Journal of Electrical and Computer Engineering

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Movie recommender chatbot based on Dialogflow Zinke Abdurahman Baizal; Nurul Ikhsan; Ichwanul Muslim Karo Karo; Reinaldo Kenneth Darmawan; Roby Dwi Hartanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp936-947

Abstract

Currently, the online movie streaming business is growing rapidly, such as Netflix, Disney+, Amazon Prime Video, HBO, and Apple TV. The recommender system helps customers in getting information about movies that are in accordance with their wishes. Meanwhile, the development of messaging platform technology has made it easier for many people to communicate instantly. Utilizing a messaging platform to build a recommender system for movies, provides special benefits because people often access the messaging platform all the time. In the Indonesian language, there are many slang terms that the system must recognize. In this study, we build a chatbot on a messaging platform which users can interact with the system in natural language (in Indonesian language) and get recommendations. We use rule-based and maximum likelihood as a method in natural language processing (NLP), and content-based filtering for the recommendation process. The recommender system interaction is built through a conversation mechanism that will form a conversational recommender system. The interaction is based on a chatbot which is built using Dialogflow and implemented on the telegram. We use the accuracy of recommendations and user satisfaction to evaluate the system performance. The results obtained from the user study indicate that the NLP approach provides a positive experience for users. In addition, the system also produces an accuracy value of 83%.
Co-Authors ., Agustinus ., Safruddim A. M. Imran Abdulloh Azzam Adi Maulana, Adi Azrul, Muh. Zulfahmi Danang Triantoro Murdiansyah Detty Agustin Riscal Devi Pratami Dian Budhi Santoso Fajrin, Muh. Favian Dewanta Fikri Auzi Al Haq Fitriyani Fitriyani Fitriyani Fitriyani Fitriyani, Firtriyani Ghazi Ahmad Fadhlullah Haerany Sirajuddin Hasanela, Nurani Hidayah, Bahrul Husain, Ratna Ichwanul Muslim Karo Karo Ika Arum Puspita Irma Palupi Iryanto Iryanto Isman Kurniawan Iswadi, Muhamad Khalid Izzatul Ummah Jamal Rauf Husain Khoiril Anam, Khoiril Lila Setiyani Litasari Widyastuti, Litasari Mahmud Imrona Maman Suryaman Maya Rosalinda Meinarni Thamrin Meutia Farida Muchamad Fajar Alif Muchlis Insani Muhammad Arief Muhammad Arief Naila Al Mahmuda Nugroho Susanto Nuridin Widya Pranoto Pachri, Hendra Perdana Aditya Natayuda Prasetiyo, Rahayu Putu Harry Gunawan Rafiqa Humaira Rahmat Gunawan Ramadhani, Siti Rayhan M Auliarahman Reina Wardhani Reinaldo Kenneth Darmawan Reza Rendian Septiawan Risma Fitriyani Rizky Alfiansyah Roby Dwi Hartanto Rohaya Langkoke Sekewael, Serly J Shaufiah . Siti Julaikha Sobirin Sobirin Sultan . Syafrianto, Donald Tugiono, Tugiono Umar Ali Ahmad Yogi Nugraha ZK Abdurahman Baizal