Rekomendasi Fitur yang Mempengaruhi Harga Sewa Menggunakan Pendekatan Machine Learning

Penulis

Bambang Wisnuadhi, Irwan Setiawan

Abstrak

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.

 

Abstract

The 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.


Teks Lengkap:

PDF

Referensi


BATCH, ANDREA and NIKLAS ELMQVIST. 2018. “The Interactive Visualization Gap in Initial Exploratory Data Analysis”. IEEE Transactions on Visualization and Computer Graphics 24: 278–87. .

BONDAREV, N. V. 2019. “Classification and Prediction of Sodium and Potassium Coronates Stability in Aqueous-Organic Media by Exploratory Data Analysis Methods”. Russian Journal of General Chemistry 89: 281–91. .

CAMACHO, JOSÉ, RAFAEL A. RODRÍGUEZ-GÓMEZ AND EDOARDO SACCENTI. 2017. “Group-Wise Principal Component Analysis for Exploratory Data Analysis”. Journal of Computational and Graphical Statistics 26: 501–12. .

FARISHA ISA, NINA and NOR ADILA ROSLI FAIRUZ HAKIM IRINA MOHD AKHIR. 2017. “Impact of Web and Digital Experience on the Stickiness of Third Party Hotel Website”. Malaysia Journal of Tourism. Vol. 9.

GARCÍA, SALVADOR, JULIÁN LUENGO and FRANCISCO HERRERA. 2016. “Tutorial on Practical Tips of the Most Influential Data Preprocessing Algorithms in Data Mining”. Knowledge-Based Systems 98: 1–29. .

GUTTENTAG, DANIEL, STEPHEN SMITH, LUKE POTWARKA and MARK HAVITZ. 2018. “Why Tourists Choose Airbnb: A Motivation-Based Segmentation Study”. Journal of Travel Research 57: 342–59. [accessed 19 February 2020].

JEBB, ANDREW T., SCOTT PARRIGON and SANG EUN WOO. 2017. “Exploratory Data Analysis as a Foundation of Inductive Research”. Human Resource Management Review 27: 265–76. .

LI, LING and K. W. CHAU. 2017. “Measuring Price Differentials Between Large and Small Housing Units: The Case of Hong Kong”. In: . Proceedings of the 20th International Symposium on Advancement of Construction Management and Real Estate. Springer Singapore. 663–75. .

MORENO-IZQUIERDO, L., A. RUBIA-SERRANO, J. F. PERLES-RIBES, A. B. RAMÓN-RODRÍGUEZ and M. J. SUCH-DEVESA. 2020. “Determining Factors in the Choice of Prices of Tourist Rental Accommodation. New Evidence Using the Quantile Regression Approach”. Tourism Management Perspectives 33: 100632. .

NUZZO, REGINA L. 2019. “Histograms: A Useful Data Analysis Visualization”. PM and R. .

OSHODI, OLALEKAN SHAMSIDEEN, WELLINGTON DIDIBHUKU THWALA, TAWAKALITU BISOLA ODUBIYI, ROTIMI BOLUWATIFE ABIDOYE and CLINTON OHIS AIGBAVBOA. 2019. “Using Neural Network Model to Estimate the Rental Price of Residential Properties”. Journal of Financial Management of Property and Construction 24: 217–30. .

OSKAM, JEROEN and ALBERT BOSWIJK. 2016. “Airbnb: The Future of Networked Hospitality Businesses”. Journal of Tourism Futures 2: 22–42. .

PHAN, THE DANH. 2019. “Housing Price Prediction Using Machine Learning Algorithms: The Case of Melbourne City, Australia”. In: . Proceedings - International Conference on Machine Learning and Data Engineering, ICMLDE 2018. Institute of Electrical and Electronics Engineers Inc. 8–13. .

RAMÍREZ-GALLEGO, SERGIO, BARTOSZ KRAWCZYK, SALVADOR GARCÍA, MICHAŁ WOŹNIAK and FRANCISCO HERRERA. 2017. “A Survey on Data Preprocessing for Data Stream Mining: Current Status and Future Directions”. Neurocomputing 239: 39–57. .

SHAHHOSSEINI, MOHSEN, GUIPING HU and HIEU PHAM. 2020. “Optimizing Ensemble Weights for Machine Learning Models: A Case Study for Housing Price Prediction”. In: . INFORMS International Conference on Service Science. Springer, Cham. 87–97. [accessed 19 February 2020].

VARMA, AYUSH, ABHIJIT SARMA, SAGAR DOSHI and ROHINI NAIR. 2018. “House Price Prediction Using Machine Learning and Neural Networks”. In: . Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2018. Institute of Electrical and Electronics Engineers Inc. 1936–39. .

WANG, DAN and JUAN L. NICOLAU. 2017. “Price Determinants of Sharing Economy Based Accommodation Rental: A Study of Listings from 33 Cities on Airbnb.Com”. International Journal of Hospitality Management 62: 120–31. .

YANG, LINCHUAN, BO WANG, JIANGPING ZHOU and XU WANG. 2018. “Walking Accessibility and Property Prices”. Transportation Research Part D: Transport and Environment 62: 551–62. .

YANG, LINCHUAN, JIANGPING ZHOU, OLIVER F. SHYR and (DEREK) DA HUO. 2019. “Does Bus Accessibility Affect Property Prices?” Cities 84: 56–65. .

ZGRAGGEN, EMANUEL, ALEX GALAKATOS, ANDREW CROTTY, JEAN DANIEL FEKETE and TIM KRASKA. 2017. “How Progressive Visualizations Affect Exploratory Analysis”. IEEE Transactions on Visualization and Computer Graphics 23: 1977–87. .




DOI: http://dx.doi.org/10.25126/jtiik.2021843305