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All Journal Jupiter E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer Jurnal Manajemen & Supervisi Pendidikan Al Qodiri : Jurnal Pendidikan, Sosial dan Keagamaan INOVTEK Polbeng - Seri Informatika Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknologi Informasi dan Komunikasi Journal of Food System and Agribusiness (JoFSA) Teknik: Jurnal Ilmu Teknik dan Informatika ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Applied Business, Taxation and Economics Reseach INFORMATIKA Dinamika NUSANTARA: Jurnal Pengabdian Kepada Masyarakat KREATIF: Jurnal Pengabdian Masyarakat Nusantara Jurnal Publikasi Ilmu Komputer dan Multimedia Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Community: Jurnal Pengabdian Pada Masyarakat Global Education Journal Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Social Science Academic SmartComp Journal of Technology Informatics and Engineering As-Sakinah Jurnal Hukum Keluarga Islam Jurnal Ilmiah Sistem Informasi International Journal of Graphic Design Social Sciences Journal (SSJ) TECHNOLOGIC KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Ahmad Zainudin
Universitas Sains Dan Teknologi Komputer
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Journal : Journal of Technology Informatics and Engineering

 1 
Leveraging Machine Learning for Personalized Recommendations in Mobile Tourism: A Study on Collaborative and Content-Based Filtering zainudin, ahmad; Siswanto, Edy
Journal of Technology Informatics and Engineering Vol 3 No 2 (2024): Agustus : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i2.190

Abstract

The rapid growth of the tourism industry, coupled with the increasing reliance on mobile technology, necessitates the development of intelligent applications that enhance user experience through personalized recommendations. This research investigates the application of machine learning techniques, specifically Collaborative Filtering and Content-Based Filtering, to create a mobile tourism application that addresses the challenges of providing relevant and tailored suggestions to users. The primary objective of this study is to improve user satisfaction and engagement by delivering precise recommendations based on individual preferences and behaviors. To achieve this, the research employs a mixed-methods approach, combining quantitative data analysis from user interactions and qualitative assessments of user feedback. The study utilizes datasets sourced from reputable platforms, including user ratings and demographic information, to train the recommendation algorithms effectively. The evaluation of the implemented system demonstrates a significant increase in recommendation accuracy, leading to enhanced user satisfaction and increased visitation rates to tourist attractions. The findings indicate that the integration of Collaborative Filtering and Content-Based Filtering not only optimizes the personalization of recommendations but also fosters user loyalty and engagement with the application. By leveraging machine learning techniques, tourism providers can better understand user preferences, ultimately leading to a competitive advantage in a crowded market. This study contributes to the ongoing discourse on the intersection of technology and tourism, offering valuable insights for future research and application development in this dynamic field.
Leveraging Machine Learning for Personalized Recommendations in Mobile Tourism: A Study on Collaborative and Content-Based Filtering zainudin, ahmad; Siswanto, Edy
Journal of Technology Informatics and Engineering Vol. 3 No. 2 (2024): Agustus : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i2.190

Abstract

The rapid growth of the tourism industry, coupled with the increasing reliance on mobile technology, necessitates the development of intelligent applications that enhance user experience through personalized recommendations. This research investigates the application of machine learning techniques, specifically Collaborative Filtering and Content-Based Filtering, to create a mobile tourism application that addresses the challenges of providing relevant and tailored suggestions to users. The primary objective of this study is to improve user satisfaction and engagement by delivering precise recommendations based on individual preferences and behaviors. To achieve this, the research employs a mixed-methods approach, combining quantitative data analysis from user interactions and qualitative assessments of user feedback. The study utilizes datasets sourced from reputable platforms, including user ratings and demographic information, to train the recommendation algorithms effectively. The evaluation of the implemented system demonstrates a significant increase in recommendation accuracy, leading to enhanced user satisfaction and increased visitation rates to tourist attractions. The findings indicate that the integration of Collaborative Filtering and Content-Based Filtering not only optimizes the personalization of recommendations but also fosters user loyalty and engagement with the application. By leveraging machine learning techniques, tourism providers can better understand user preferences, ultimately leading to a competitive advantage in a crowded market. This study contributes to the ongoing discourse on the intersection of technology and tourism, offering valuable insights for future research and application development in this dynamic field.
Co-Authors Aan Tya Ervina Agus Priyadi Agus Priyadi Agus Priyadi Agus Sumanto Agustinus Budi Santoso Al-Muqri, Fatima ALDO KRISDIANANTA Andik Prakasa Hadi Anisah, Ida Ayrelliano Reyvanda Nugroho basori, Chamdan Choirul Anam AM Diponegoro Danang Danang Dani Sasmoko Daniel Rudjiono Desy Intan Permatasari, Desy Intan Dewi Prehantini Wahyuningtyas Dewi Widyaningsih Dimas Pamungkas Edwin Zusrony Efendi, Nur Mahfud Eka Samsul Maarif Eko Rustanto Ester Dian Santos H.B.N.MS, Ayyub Haris Setyawan Hendhi Hermawan Hendy Briantoro Jogatama Purhita, Edy Kurnia Minarsih Kusumajaya, Robby Andika Lutfil Hakim Arif Efendi Moh Muthohir Muhammad Khoiril Anaam Mukhlisin Mukhlisin Nasikh Nuris Dwi Setiawan Permatasari, Adinda Priyadi Priyadi Priyadi Priyadi Raden Mohamad Herdian Bhakti Rizki Sidik Wicaksono Rudjiono Rujiono Samsudin Santi Widiastuti Sarwo Nugroho Setiani, Tri Setiyo Adi Nugroho Siswanto, Edy Siti Sohiroh Sugiarto S Tamrin Fathoni Tantik Sumarlin . Wibowo, Mars Caroline Yuli Fitrianto