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All Journal Scientific Journal of Informatics Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) JOIV : International Journal on Informatics Visualization Creative Information Technology Journal Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL ILMIAH INFORMATIKA JurTI (JURNAL TEKNOLOGI INFORMASI) Informatik : Jurnal Ilmu Komputer CSRID (Computer Science Research and Its Development Journal) Jurnal Teknologi Informasi dan Terapan (J-TIT) Informatika Indonesian Journal of Business Intelligence (IJUBI) IJIIS: International Journal of Informatics and Information Systems BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of Applied Data Sciences International Journal of Computer and Information System (IJCIS) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD International Journal of Robotics and Control Systems Jurnal Pengabdian Masyarakat IPTEK Jurnal Ilmiah Edutic : Pendidikan dan Informatika SAGA: Journal of Technology and Information Systems Bangun Desa : Jurnal Pengabdian Masyarakat Jurnal Medika: Medika Journal of Intelligent Systems and Information Technology Jurnal Sistem Informasi dan Teknologi Informasi Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi KOMPUTASI
Pujo Hari Saputro
Sam Ratulangi University
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Journal : Journal of Applied Data Sciences

 1 
Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets Saputro, Pujo Hari; Nanang, Herlino
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.20

Abstract

Online ordering is the latest breakthrough in the hospitality industry, but when it comes to booking cancellations, it has a negative impact on it. To reduce and anticipate an increase in the number of booking cancellations, we developed a booking cancellations prediction model using machine learning interpretable algorithms for hotels. Both models used Random Forest and the Extra Tree Classifier share the highest precision ratios, Random Forest on the other hand has the highest recall ratio, this model predicted 79% of actual positive observations. These results prove that it is possible to predict booking cancellations with high accuracy. These results can also help hotel owners or hotel managers to predict better predictions, improve cancellation regulations, and create new tactics in business.
Co-Authors A'yun, Riza Shinta Qurrota Aditiarno Manik Ahmad Sulaiman, Ahmad Airlangga, Gregorius Alamsyah - Allo, Yusi Meilany Kendek Amsy, M.R Mutawakkil Anas, Syukron Arbiati Faizah Arbiati Faizah Asep Rahman Augusta Jannatul Firdaus Batubara, Saimarrasoki Dhina Puspasari Wijaya, Dhina Puspasari Djoko Budiyanto, Djoko Dyah Listianing Tyas, Dyah Listianing Dyah ListianingTyas Fatonah, Umti Gutama, Deden Hardan Henki Bayu Seta Herlino Nanang Indana Lazulfa Izzatul Umami Julius Bata Kalambia, Audia Endondaya Kevin, Geraldy Lintang Patria Luqmanul Hakim M Fadhel Nurmidin Maddusa, Sri Seprianto Makin, See Jong Michael Aristian Musthofa Galih Pradana Nindy Irzavika Nugroho, Oskar Ika Adi Nur Arifin Nur Rachman Dzakiyullah Nurcahyo, Azriel Christian Olivia Maria Tumurang, Olivia Maria Paendong, Indah Prisilia Pamekas, Bondan Wahyu Payuk, Fajar Salinding Buntu Pontoh, Fransisca Joanet Reinhard Komansilan Rendy Syahputra Reza Augusta J.F Rizal Sengkey Rouzi, Kana Safrina Roy, Andi Rumui, Nelson Rusiyono, Ruwet Salassa, Norris Elden Sari, Susi Nopita Silvianah, Nita Siti Zubaidah Sitompul, Bernard Jumadi Dehotman Sugianto, Lai Ferry Tumurang, Olivia Maria Tumurang Viantina, Anisa Lia Victor Tarigan Wahyuni Fithratul Zalmi Wardiyana, Lisa Wardiyana, Lisa Widoyoningrum, Sri Wulandari, Narulita Zalmi, Wahyuni Fithratul