Profile Matching in Python to Identify Tourist Destinations for The Development of National Tourism

Abstract

Tourism has emerged as a key contributor to state revenue and has played a crucial role in national economic recovery following the Covid-19 pandemic, establishing itself as a new engine for the country’s economic growth. In response to this, the government—through the Ministry of Tourism and Creative Economy—is actively promoting development in the tourism sector to enhance numerous tourist attractions in Indonesia, which are renowned for their beauty and recognized globally. However, the Ministry faces several challenges, including the need for technological advancements and the expansion of infrastructure. Addressing these needs will require substantial financial investment to optimize all tourism sites. This study aims to identify 22 tourist attractions across Indonesia using the Profile Matching method in Python programming, based on seven established criteria. The uniqueness about this study is that this research implemented  a simple Python script and the tourist attractions are spread across all regions of Indonesia. The results of the study highlight three tourist destinations that warrant government attention for optimization: Lake Maninjau, Rantepao, and Seminyak. To facilitate improvement, several areas require focus, including upgrading access roads, enhancing basic facilities, fostering community participation, developing human resources, and leveraging technology. The analysis using Python demonstrates that the software performs efficiently, processing 22 data points with seven criteria in a mere 0.06 seconds.