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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer E-Dimas: Jurnal Pengabdian kepada Masyarakat JAST : Jurnal Aplikasi Sains dan Teknologi JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Difusi Industrial Research Workshop and National Seminar Journal of Applied Islamic Economics and Finance Jurnal AbdiMU
Wisnuadhi, Bambang
Politeknik Negeri Bandung
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Journal : Jurnal Teknologi Informasi dan Ilmu Komputer

 1 
Rekomendasi Fitur yang Mempengaruhi Harga Sewa Menggunakan Pendekatan Machine Learning Bambang Wisnuadhi; Irwan Setiawan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 4: Agustus 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021843305

Abstract

Perkembangan Teknologi Infromasi, internet, dan perangkat bergerak telah mengubah perilaku konsumen dalam menjalankan aktivitasnya. Hal ini direspon oleh industri dengan menyediakan berbagai aplikasi berbasis web dan perangkat bergerak dalam interaksinya dengan pelanggan. Salah satu industri yang beradaptasi dengan perubahan teknologi dan perilaku konsumen ini adalah industri pariwisata dan perhotelan. Kebutuhan konsumen yang sebelumnya menggunakan akomodasi wisata tradisional seperti hotel, berubah menjadi lebih memilih rumah-rumah penduduk disekitar tempat wisata sebagai tempat penginapan sementara wisatawan. Perubahan ini berdampak kepada semakin banyaknya properti pribadi yang disewakan sehingga menyebabkan persaingan harga sewa. Harga sewa merupakan salah satu faktor penting yang dipertimbangkan calon penyewa dalam menentukan properti yang akan disewanya. Hal ini tentunya membuat para pemiliki properti harus memikirkan strategi penentuan harga sewa agar propertinya laku dipasaran. Penelitian ini bertujuan untuk mendapatkan fitur apa saja yang dapat mempengaruhi penentuan harga sewa properti berdasarkan data pengguna Airbnb di Berlin. Data penelitian diambil dari dataset yang disediakan oleh InsideAirbnb berupa file dengan format CSV. Penelitian dilakukan menggunakan teknik machine learning dengan pendekatan algoritma XGBoost. Terdapat lima tahapan pengerjaan dalam penelitian ini, yaitu data understanding, data pre-processing, exploratory data analysis, pemodelan, dan insights. Hasil yang didapatkan dari penelitian ini adalah room type private room, room type entire home/apt, dan cancellation policy super strict 60 days merupakan tiga fitur tertinggi yang mempengaruhi penentuan harga sewa. Luas properti menempati urutan keempat berdasarkan rekomendasi algoritma yang diterapkan. AbstractThe development of information technology, the internet, and mobile devices has changed the behavior of consumers in carrying out their activities. The industry responded by providing various web-based and mobile applications in their interactions with customers. The tourism and hospitality industry is adapting to changes in technology and consumer behavior. The needs of consumers who previously used traditional tourist accommodations such as hotels have changed to prefer residents' houses around tourist attractions as their temporary lodging. This change has an impact on the increasing number of private properties being leased, causing competition in rental prices. It is undeniable that the rental price is one of the essential factors that prospective tenants consider in making choices. This certainly makes property owners, who will rent out their properties, have to think about rental pricing strategies. This study aims to obtain any features that affect pricing based on Airbnb user data in Berlin. The study was conducted using machine learning techniques with the XGBoost algorithm approach. There are five stages of work in this study, namely understanding data, pre-processing data, exploratory data analysis, modeling, and insights. The results obtained from this study are room type private room, room type entire home / apt, and cancellation policy type super strict 60 are the three highest features that affect price determination. Property size ranks fourth based on algorithmic recommendations.
Co-Authors Aldy Akbarrizky Ani Rahmani Ardhian Ekawijana Asri Maspupah Cholid Fauzi Dimas Sumitra Danisworo Dimas Sumitra Danisworo Dimas Sumitra Danisworo Gelar, Trisna Ghifari Munawar Hasbi Assidiki Mauluddi Haya Utami Lutfi Ine Mayasari Irwan Setiawan Joe Lian Min Lukmannul Hakim Firdaus Lukmanul Hakim Firdaus Muhammad Afif Muzakki Muhammad Azra Sabila Muhammad Rizqi Sholahuddin Mumuh Kustino Muharram Niko Sumanda Sibarani Rahil Jumiyani Said Al Fakhri Santi Sundari Sari, Aprianti Nanda Siti Dwi Setiarini Wendi Wirasta Wira Kusuma Wardana Wulan, Sri Ratna Zikri Ariachandra [1] S. Peltonen, L. Mezzalira, and D. Taibi, “Motivations, benefits, and issues for adopting Micro-Frontends: A Multivocal Literature Review,” Inf Softw Technol, vol. 136, Aug. 2021, doi: 10.1016/j.infsof.2021.106571. [2] Y. Prajwal, J. Viren Parekh,