Penentuan Tingkat Diskon Online Travel Agent pada Hotel dengan Simulasi Monte Carlo ( Studi Kasus : Clay Hotel Jakarta )

Abstract

FAJAR ADITYA PRATAMA. Penentuan Tingkat diskon Online Travel Agent pada Hotel dengan Simulasi Monte Carlo ( Studi Kasus : Clay Hotel Jakarta ). Dibimbing oleh D. IWAN RISWANDI. Tingkat hunian hotel bintang tiga di Jakarta, termasuk Clay Hotel, rendah saat low season. Penetapan diskon di Online Travel Agent (OTA) seringkali berdasarkan perkiraan, sehingga kurang optimal. Penelitian ini bertujuan menganalisis pendekatan aplikatif penentuan diskon, membandingkan okupansi aktual dengan proyeksi maksimum Simulasi Monte Carlo, dan menerapkan tingkat diskon yang memaksimalkan okupansi tanpa merugikan operasional. Metode analisis meliputi regresi log-log untuk elastisitas harga dan simulasi Monte Carlo 1.000 iterasi pada empat skenario diskon. Hasilnya, elastisitas permintaan -1,603 (R²=10,9%) menandakan dominasi faktor non-harga. Diskon 15% adalah tingkat optimal, meningkatkan rata-rata okupansi menjadi 50% dan pendapatan hingga Rp2,88 miliar, sementara diskon lebih tinggi justru menurunkannya. Oleh karena itu, hotel perlu menyeimbangkan diskon dengan peningkatan layanan dan pemasaran digital. Kata kunci: diskon, elastisitas harga, okupansi hotel, Online Travel Agent, simulasi Monte Carlo

FAJAR ADITYA PRATAMA. Determining Online Travel Agent Tingkat diskons for Hotels Using Monte Carlo Simulation (Case Study: Clay Hotel Jakarta). Supervised by D. IWAN RISWANDI. The occupancy rates of three-star hotels in Jakarta, including Clay Hotel, are notably low during the low season. Determining Tingkat diskons through Online Travel Agents (OTAs) is often based on estimates, making it suboptimal for maximizing both occupancy and revenue. This study aims to analyze an applicable approach for setting Tingkat diskons, compare actual occupancy with maximum occupancy projections from a Monte Carlo Simulation, and implement a rational Tingkat diskon that maximizes occupancy without causing operational losses. The analysis employed log-log regression to estimate price elasticity and a Monte Carlo simulation with 1,000 iterations across four discount scenarios. The results revealed a price elasticity of demand of -1.603 with an R² of 10.9%, indicating that non-price factors are more influential on demand. The simulation showed that a 15% discount is the most optimal, raising the average occupancy rate to 50% and revenue to IDR 2.88 billion. Conversely, higher discounts led to a decline in revenue. Therefore, in addition to applying a 15% discount, the hotel needs to enhance service quality and digital marketing to optimize occupancy. Keywords: discount, hotel occupancy, Monte Carlo simulation, Online Travel Agent, price elasticity