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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
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

 1 
Conversational Recommender Systems Based on Criticism for Tourist Attractions using TF-IDF Rayhan M Auliarahman; Z K Abdurahman Baizal; Nurul Ikhsan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3245

Abstract

Tourist attractions are one of the attractions of tourist interest. There are many types of tourist attractions in an area, but this becomes a problem in itself because tourists will find it hard to find or determine a tourist attractions that suit their tastes. Many researches on recommendation systems based on criticism have been carried out with the aim of obtaining user preferences. However, only a few studies have conducted a critique-based recommendation system using the Conversational Recommender System (CRS). With this research, we will discuss a recommendation system based on criticism using natural language or CRS for tourist attractions in Bandung. In this study, we add assistance from the system to help users choose preferences or what can be called System-suggested Critiques (SC), users more easily determine preferences for the system. We use the Term Frequency-Inverse Document Frequency (TF-IDF) to determine critiques submitted to users. Based on the results of an evaluation involving 88 respondents who were asked to fill out a questionnaire after trying the system built, it was found that users were quite satisfied with the system we built. And obtained 62.06% system accuracy which proves that the system performance is quite satisfactory.
Conversational Recommender Systems Based on Mobile Chatbot for Culinary Ghazi Ahmad Fadhlullah; Z K Abdurahman Baizal; Nurul Ikhsan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3242

Abstract

Culinary places are one of the tourists attractions in a place that makes many new culinary places appear. Various types of new foods and drinks are present along with the addition of culinary places. However, this can be a problem when tourists visit a new destination and look for a culinary place that suits their tastes. In the previous research on the recommendation system for culinary places, users only gave their preferences at the beginning of the recommendation process and ignored the operating hours of the recommended culinary places. Therefore, we developed a recommendation system for culinary places by utilizing the context of time from users. We use the Conversational Recommender System on the chatbot platform with the Personalized PageRank algorithm to generate recommendations. In addition, we also use the explanation facility to get an explanation of the recommended items. We use questionnaires and the accuracy of recommendation results to measure user satisfaction and system performance. The evaluation results with a questionnaire involving 81 respondents concluded that users are pretty satisfied with the system built. However, testing with accuracy yields a value of 40%, proving that the system performance is low
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