MODEL PREDIKSI HARGA KAMAR HOTEL DENGAN SUPPORT VECTOR REGRESSION

• JAJA MIHARJA
• 14002277

---

ABSTRAK

ABSTRAK

 

 

 

Nama                           : Jaja Miharja

NIM                            : 14002277

Program Studi             : Ilmu Komputer

Jenjang                        : Strata Dua (S2)

Konsentrasi                 : Data Mining

Judul Tesis                  : “Model Prediksi Harga Kamar Hotel Dengan Support

                                        Vector Regression”

 

 

Penelitian ini melanjutkan penelitian sebelumnya tentang harga kamar hotel menggunakan model advanced algoritma yang dilakukan oleh Karathanasopoulos and Shehhi (2020), yang menggunakan beberapa model  advanced forecasting dari machine learning dan Artificial Inteligence (AI) pada sektor Hospitality. Pada penelitian ini mengusulkan model regresi prediksi dari Support Vector Regression (SMOReg) yang menggunakan tiga kernel SMOreg antara lain Polykernel, Pearson VII Kernel (PUK) dan Radial Basic Function (RBF). Data penelitian diperoleh dari Zuri Express Mangga Dua. Dalam penelitian ini akan difokuskan untuk menguji apakah model Support Vector Regression dengan tiga kenel tersebut dapat diterapkan dalam prediksi harga kamar hotel, dengan menggunakan beberapa parameter yang telah ditentukan. Hasil yang terbaik yang didapatkan pada penelitian ini mencapai error rate MAPE sebesar 0,1177, hasil tersebut dicapai dengan nilai parameter sebagai berikut, Jumlah iterasi : 100, Complexity : 1.0, Omega : 1.0, Sigma : 1.0.

 

Kata kunci: Prediksi Harga Hotel, Algoritma Regresi, Support Vector Regression

KATA KUNCI

Vector Regression

---

DAFTAR PUSTAKA

DAFTAR REFERENSI

 

 

[1]      M. Al Shehhi and A. Karathanasopoulos, “Forecasting hotel room prices in selected GCC cities using deep learning,” J. Hosp. Tour. Manag., vol. 42, no. May 2019, pp. 40–50, 2020, doi: 10.1016/j.jhtm.2019.11.003.

[2]      D. Dredge and T. Jamal, “Progress in tourism planning and policy: A post-structural perspective on knowledge production,” Tour. Manag., vol. 51, pp. 285–297, 2015, doi: 10.1016/j.tourman.2015.06.002.

[3]      A. F. P. Legohérel, E. Poutier, “Revenue management for hospitality & tourism,” J. Revenue Pricing Manag., vol. 13, no. 1, pp. 74–76, 2014, doi: 10.1057/rpm.2013.40.

[4]      A. Macvicar and J. Rodger, “( ~ P e r g a m o n,” pp. 325–332, 1996.

[5]      A. Rodríguez-Algeciras and P. Talón-Ballestero, “An empirical analysis of the effectiveness of hotel Revenue Management in five-star hotels in Barcelona, Spain,” J. Hosp. Tour. Manag., vol. 32, pp. 24–34, 2017, doi: 10.1016/j.jhtm.2017.04.004.

[6]      M. Al Shehhi and A. Karathanasopoulos, “Forecasting Hotel Prices in Selected Middle East and North Africa Region (MENA) Cities with New Forecasting Tools,” Theor. Econ. Lett., vol. 08, no. 09, pp. 1623–1638, 2018, doi: 10.4236/tel.2018.89104.

[7]      M. Ak, “A novel approach to model selection in tourism demand modeling,” vol. 48, pp. 64–72, 2015, doi: 10.1016/j.tourman.2014.11.004.

[8]      C. Zhang, “Current Issues in Tourism Analysing Chinese citizens ’ intentions of outbound travel?: a machine learning approach,” no. January 2015, pp. 37–41, doi: 10.1080/13683500.2013.768606.

[9]      R. S. Paul Phillips, Krystin Zigan, Maria Manuela Santos Silva, “The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis,” Tour. Manag., vol. 50, pp. 130–141, 2015, doi: 10.1016/j.tourman.2015.01.028.

[10]    J. S. Douglas C Pattie, “Using a neural network to forecast visitor behavior,” Ann. Tour. Res., vol. 23, pp. 151–164, 1996, doi: 10.1016/0160-7383(95)00052-6.

[11]    R. Law, “Room occupancy rate forecasting: A Neural network approach.,” Int. J. Contemp. Hosp. Manag., vol. 10, pp. 234–239, 1998.

[12]    J. C. Zhenshan Yang, “Do regional factors matter? Determinants of hotel industry performance in China,” Tour. Manag., vol. 52, pp. 242–253, 2016, doi: 10.1016/j.tourman.2015.06.024.

[13]    J. B.-G. Santiago Melián-González, “Santiago Melián-González, Jacques Bulchand-Gidumal, A model that connects information technology and hotel performance,” Tour. Manag., vol. 53, pp. 30–37, 2016, doi: 10.1016/j.tourman.2015.09.005.

[14]    K. O. Jason F. Cohen, “The impacts of complementary information technology resources on the service-profit chain and competitive performance of South African hospitality firms,” Int. J. Hosp. Manag., vol. 34, pp. 245–254, 2013, doi: 10.1016/j.ijhm.2013.04.005.

[15]    T.-P. Turban, E., E. Aronson, J., & Liang, “Decision Support and Business Intelligence Systems,” Decis. Support Syst. Bus. Intell., vol. 7/E, 2007, doi: 10.1017/CBO9781107415324.004.

[16]    M. North, Data Mining for the Messes. Athens, GA: USA: Global Text Project., 2016.

[17]    D. T. Larose, “Discovering Knowledge in Data: An Introduction to Data Mining,” Discov. Knowl. Data An Introd. to Data Min., 2005, doi: 10.1002/0471687545.

[18]    H. Hoffer, J. A., Venkataraman, R., & Topi, Modern Database Management, 11th ed. ‎London, England: Pearson, 2010.

[19]    Z. Budi Santoso, Azminudin I. S. Azis, Machine Learning & reasoning Fuzzy Logic, Algoritma, Manual, Matlab & Rapid Miner, 1st ed. Yogyakarta: Deepublish, 2020.

[20]    ?????MIT ??, “Regression (Gaussian process for machine learning),” Gaussian Processes for Machine Learning, p. Chapter 2, 2006.

[21]    R. R. Pratama, “Analisis Model Machine Learning Terhadap Pengenalan Aktifitas Manusia,” vol. 19 No. 2, pp. 302–311, 2020.

[22]    and R. C. W. D. A. Mardhika, B. D. Setiawan, “Penerapan Algoritma Support Vector Regression Pada Peramalan Hasil Panen Padi Studi Kasus Kabupaten Malang,” vol. 3 No.10, 2019.

[23]    Joachim T, “Making Large-Scale SVM Learning Practical,” MIT Press, no. In B. Schoelkopf, C. J. C. Burges, and A. J. Smola (Eds), Advances in Kernel Methods – Support Vector Learning, pp. 169–184, 1999.

[24]    K. A. A. Abakar and C. Yu, “Performance of SVM based on PUK kernel in comparison to SVM based on RBF kernel in prediction of yarn tenacity,” Indian J. Fibre Text. Res., vol. 39, no. 1, pp. 55–59, 2014.

[25]    Arman Hakim Nasution, Perencanaan dan Pengendalian Produksi, Kedua. Surabaya: Prima Printing, 2008.

[26]    Vincent Gasperz, Production Planning And Inventory Control. Jakarta: PT. Gramedia Pustaka Utama, 2008.

[27]    S. Kalmegh, “Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News,” IJISET - Int. J. Innov. Sci. Eng. Technol., vol. 2, 2015.

[28]    T. M. Ratna Indrawati, Rudy Dwi Nyoto, “Rancang Bangun Aplikasi Jadwal Kegiatan Akademik Berbasis Android,” Sist. dan Teknol. Inf., vol. 1, no. 02, pp. 1–5, 2017.

[29]    and K. K. S. Pujiono, A. Amborowati, M. Suyanto, “Analisis kepuasan publik menggunakan weka dalam mewujudkan,” J. DASI, vol. 14, pp. 45–55, 2013.

[30]    A. A. Pranatha, “Analisis Perbandngan Lima Metode Klasifikasi Pada Dataset Sensus Penduduk,” J. Sist. Infomasi, vol. 4/2, pp. 127–134, 2012.

[31]    Nurmahaludin, “Analisis Perbandingan Metode Jaringan Syaraf Tiruan Dan Regresi Linear Berganda Pada Prakiraan Cuaca,” J. INTEKNA, vol. 2, 2014.

[32]    and P. C. P. Pai, K. Lin, C. Lin, “Expert Systems with Applications Time series forecasting by a seasonal support vector regression mode,” Expert Syst. Appl, vol. 37 no. 6, pp. 4261–4265, 2010.

[33]    Angipora MP, Dasar - Dasar Pemasaran. Jakarta: Raja Grafindo Persada, 2002.

[34]    Sugiyono, Metode Penelitian Kuantitatif, Kualitatif dan R&D. Bandung: Alfabeta, 2012.