Publikasi Ilmiah

The rapid development of Big Data as result of increasing interactivity with online systems between humans (e.g., online shopping, marketplace) and machine (internet of things, mobile phone, etc.) has led to a measurement revolution. This massive data if being mined and analyzed correctly can provide valuable alternative data sources for official statistics, especially price statistics. Several studies for using diverse Big Data as new sources of price statistics in Indonesia have been initiated. This article would provide a comprehensive review of experiences in exploiting various Big Data sources for price statistics, followed by the current development and the near future plans. The development of system and IT infrastructure is also discussed. Based on this experience, limitations, challenges, and advances for each approach would be presented

Environmental data such as pollutants, temperature, and humidity are data that have a role in the agricultural sector in predicting rainfall conditions. In fact, pollutant data is common to be used as a proxy to see the density of industry and transportation. With this need, it is necessary to have automated data from outside websites that are able to provide data faster than satellite confirmation. Data sourced from IQair, can be used as a benchmark or confirmative data for weather and environmental statistics in Indonesia. Data is taken by scraping method on the website. Scraping is done on the API available on the website. Scraping is divided into 2 stages, the first is to determine the location in Indonesia, the second is to collect statistics such as temperature, humidity, and pollutant data (AQI). The module used in python is the scrapy module, where the crawling is effective starting from May 2020. The data is recorded every three hours for all regions of Indonesia and directly displayed by the Power BI-based dashboard. We also illustrated that AQI data can be used as a proxy for socio-economic activity and also as an indicator in monitoring green growth in Indonesia. 

Data and information regarding air transportation is very crucial for all aspects, such as economy, people mobility and tourism. Currently, the Air Transportation Statistics is based on administrative data of different institutions. it is necessary to have a new data source regarding air transportation activities that can be used as an alternative reference for air transportation data which is faster and more granular. This research aims to study a new approach to produce air transportation statistics, especially air transportation statistics in Indonesia from Big Data that can be used as comparative or as complementary data for official air transportation statistics. This research using a website scraping method from a site that provides monitoring and tracking services for all flights in the world. The flight data was collected daily from the 15 busiest airports in Indonesia for both departure and arrival flights using the API provided by the website. The Scrapy module in the Python programming language is implemented. The data was collected daily from March 15, 2020, to August 31, 2020. The results of the flight data set contain information about the flight code, aircraft code, airline name, departure airport, departure city, arrival airport, arrival city, date/time of departure and arrival, and flight status. The result shows that it is feasible to use Big Data as a comparative or as a complementary of official statistics, especially in air transportation statistics. By using the web scraping technique, the indicator that usually requires more time and cost can be done in real-time and less cost. This new approach is expected to improve the quality of official air transportation statistics. 

Ekonomi sangat berpengaruh untuk memajukan kehidupan di suatu negara, namun diakibatkan oleh pandemi virus Covid-19 kondisi ekonomi negara menjadi tidak stabil. Beberapa sektor seperti pariwisata, penurunan pertumbuhan ekonomi seperti PDRB. Berdasarkan data Badan Pusat Statistik (BPS) Provinsi Bali PDRB menurun secara signifikan pada tahun 2020 triwulan keempat yaitu hingga-12, 21%. Penelitian ini menggunakan metode analisis hubungan atau korelasi antara data PDRB dan TPK hotel dengan data tayangan iklan penjualan dan penyewaan properti di Provinsi Bali, dimana data didapatkan dengan menggunakan Big Data dengan metode web scraping salah satu situs web properti di Indonesia (rumah123.com). Secara hubungan antara PDRB dan tayangan iklan didapatkan hasil -0.64 dan -0.67 menunjukkan harga jual dan sewa sektor properti mengalami tekanan dan berdampak pada pemulihan pasar properti dan tayangan iklan menaik, sedangkan pada TPK dihasilkan -0.53 menunjukkan pendapatan pada sektor pariwisata sudah cukup naik maka iklan penjualan akan menurun, karena pariwisata adalah sumber pendapatan yang cukup potensial di Bali.

This study aims to investigate the different impacts of the COVID-19 pandemic on Indonesia’s tourism industry, as this and its supporting sectors are the most affected by the COVID-19 pandemic worldwide, by clustering the provinces based on the room occupancy rate (ROR) to understand provinces specific impacts. Several Big Data sources are employed to study the different impacts of the pandemic on two tourists’ most popular provinces, i.e. Bali and Yogyakarta. A number of tourism indicators such as number of international arrivals, ROR from the BPS statistics Indonesia, combined with data from Google mobility index, Google trend, flight tracker and reviews from Tripadvisor and Booking.com are presented. Provinces are clustered by ROR category using the dynamic time warping method. The COVID-19 pandemic has obviously impacted the tourism industry and its supporting sectors in across Indonesia. However, this study explains the different patterns of the impact in different provinces. Furthermore, this study proves that Big Data sources are shown to be a good proxy to infer the impact of the pandemic on tourism.