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 worl...

Impact of COVID-19 pandemic on tourism in Indonesia

Background: In this paper, we examine how social media influencers can influence visit intention, especially in the case of Raffi Ahmad and Nagita Slavina, a top influencer who by 2 September 2021 had reached 21.3 M subscribers on YouTube and 54.9 m followers on Instagram with an engagement rate of 0.42%. The focus of this study is Generation Y or Millennials (born 1981–1996) and Generation Z (born 1997–2012). Design/methodology/approach: Snowball sampling was performed to arrive at a representative group of Millennials. Data analysis was performed using hierarchical likelihood via structural equation modeling. Findings: The study results are helpful for a comprehensive understanding of factors affecting visit intention. Effects of the study results summary, tourists from Generations Y and Z are thriving within the internet of things and the digital age, an era in which information can be accessed via various forms of technology across multiple platforms. Practical implications: We discuss and identify the relative importance of each factor through the use of logistics with variational approximation and structural equation models using hierarchical likelihood. Originality: The technique we use is an integrated and extended version of the structural equation model with hierarchical likelihood estimation and features selection using logistics variational approximation.

/pdf/the-impact-of-social-media-influencers-raffi-ahmad-and-2hhfhxef.pdf

The Impact of Social Media Influencers Raffi Ahmad and Nagita Slavina on Tourism Visit Intentions across Millennials and Zoomers Using a Hierarchical Likelihood Structural Equation Model

Air quality conditions are now more severe in the Jakarta area that is among the world’s top eight worst cities according to the 2022 Air Quality Index (AQI) report. In particular, the data from the Meteorological, Climatological, and Geophysical Agency (BMKG) of the Republic of Indonesia, the latest outcomes in air quality conditions in Jakarta and surrounding areas, says that PM2.5 concentrations have increased and peaked at 148μ g/m3 in 2022. While a classification system for this pollution is necessary and critical, the observation of PM2.5 concentrations measured through the BMKG Kemayoran station, Jakarta, turns out to be identified as an unbalanced data class. Thus, in this work, we perform boosting algorithm supervised learning to handle such an unbalanced classification toward PM2.5 concentration levels by observing meteorological patterns in Jakarta during 1 January 2015 to 7 July 2022. The boosting algorithms considered in this research include Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). Our simulations have proven that boosting classification can significantly reduce bias in combination with variance reduction with unbalanced within-class coefficients, with the classification of PM2.5 class values: good 62%, moderate 34%, and unhealthy 59%, respectively. 

Boosting Algorithm to handle Unbalanced Classification of PM2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia using AdaBoost, XGBoost, CatBoost, and LightGBM

The fast transformation of the urban centers, pushed by the impacts of climatic changes and the dramatic events of the COVID-19 Pandemic, will profoundly influence our daily mobility. This resulted scenario is expected to favor adopting cleaner and flexible modal solutions centered on bicycles and scooters, especially as last-mile options. However, as the use of bicycles has rapidly increased, cyclists have been subject to adverse conditions that may affect their health and safety when cycling in urban areas. Therefore, whereas cities should implement mechanisms to monitor and evaluate adverse conditions in cycling paths, cyclists should have some effective mechanism to visualize the indirect quality of cycling paths, eventually supporting choosing more appropriate routes. Therefore, this article proposes a comprehensive multi-parameter system based on multiple independent subsystems, covering all phases of data collecting, formatting, transmission, and processing related to the monitoring, evaluating, and visualizing the quality of cycling paths in the perspective of adverse conditions that affect cyclist. The formal interactions of all modules are carefully described, as well as implementation and deployment details. Additionally, a case study is considered for a large city in Brazil, demonstrating how the proposed system can be adopted in a real scenario.

https://www.mdpi.com/2624-6511/4/3/56/pdf

iBikeSafe: A Multi-Parameter System for Monitoring, Evaluation and Visualization of Cycling Paths in Smart Cities Targeted at Cycling Adverse Conditions

Design/methodology/approach: In the present digital era, technology infrastructure plays an important role in the development of digital literacy in various sectors that can provide various important information on a large scale. Purpose: The use of information and communication technology (ICT) in Indonesia in the last five years has shown a massive development of ICT indicators. The population using the internet also experienced an increase during the period 2016–2020, as indicated by the increasing percentage of the population accessing the internet in 2016 from around 25.37 percent to 53.73 percent in 2020. This study led to a review of the level of ICT vulnerability in eastern Indonesia through a machine learning-based cluster analysis approach. Implications: Data were collected in this study from Badan Pusat Statistik (BPS) through SUSENAS to obtain an overview of the socioeconomic level and SAKERNAS to capture the employment side. This study uses 15 variables based on aspects of business vulnerability covering 174 districts/cities. Practical implications: Cluster analysis using Fuzzy C Means (FCM) was used to obtain a profile of ICT level vulnerability in eastern Indonesia by selecting the best model. The best model is obtained by selecting the validation value such as Silhouette Index, Partition Entropy, Partition Coefficient, and Modified Partition Coefficient. Social implication: For some areas with a very high level of vulnerability, special attention is needed for the central or local government to support the improvement of information technology through careful planning. Socio-economic and occupational aspects have been reflected in this very vulnerable cluster, and the impact of the increase in ICT will provide a positive value for community development. Originality/value: From the modelling results, the best cluster model is two clusters, which are categorized as high vulnerability and low vulnerability. For each cluster member who has a similarity or proximity to each other, there will be one cluster member.

/pdf/revisiting-cluster-vulnerabilities-towards-information-and-1jhqlh4m.pdf

Revisiting Cluster Vulnerabilities towards Information and Communication Technologies in the Eastern Island of Indonesia Using Fuzzy C Means

Big Data is an umbrella term for explosion in the quantity and diversity of high frequency digital data and it is not usually coming from traditional sources The speed and frequency by which data is produced and collected — by an increasing number of sources — is responsible for today's data deluge: the amount of available digital data is projected to increase by an annual 40% “Big Data for Development” is a concept that refers to the identification of sources of Big Data relevant to policy and planning of development programmes It differs from both “traditional” development data and what the private sector and mainstream media call Big Data Potential applicability of “Big Data for Development” at the most general level, when it is properly analysed, these new data can provide snapshots of the well-being of populations at high frequency, high degrees of granularity, and from a wide range of angles, narrowing both time and knowledge gaps This research discussed several possible implementations of Big Data to the official statistics in Indonesia Furthermore, three case studies would be discussed: (1) predicting inter-city commuting patterns using twitter, (2) developing a statistical model to nowcast food prices using crowdsourcing, and (3) Mobile Position Data for Tourism Statistics The results show similar trend between crowdsourcing approach and BPS Survey for all commodities, between the twitter approach and the commuter survey 2014 For the MPD approach for tourist statistics, the number of visits based on the roaming and the visits based on the immigration are similar The study reveals potential implementations of Bigdata in complementing official statistics for government policy in Indonesia

Big data for government policy: Potential implementations of bigdata for official statistics in Indonesia

Tourism data is one of the strategic data in Indonesia. In addition, tourism is one of the ten priority programs of national development planning in Indonesia. BPS-Statistics Indonesia has collected data related to tourism demand in Indonesia, but these data have different time period. Several data can be provided monthly, while the other data can be provided annually. However, accurate and real time tourism data are needed for effective policy making. In this era, all of information about tourism destination or accommodation can be gotten easily through internet, especially information from Google search engine, such as information about tourism places, flights, hotels, and ticket for tourism attractions. Since 2004, Google has provided the information of user behavior through Google Trends tool. This paper aims to analyze and compare the patterns of tourism demand in Indonesia from Google Trends data with tourism statistics from BPS-Statistics Indonesia. In order to understand tourism demand in Indonesia, we used Google Trends data on a set of queries related to tourism. This paper shows that the search intensity of related queries provides the pattern of predicted tourism demand in Indonesia. We evaluated the prediction result by comparing several time series models. Furthermore, we compared and correlated the Google Trends data with official data. The result shows that Google Trends data and tourism statistics have similar pattern when there were disasters. The result also shows that Google Trends data has correlation with official data and produced accurate prediction of tourism demand in Indonesia. Therefore, Google Trends data can be used to predict and understand the pattern of tourism demand in Indonesia.

Predicting Tourism Demand in Indonesia Using Google Trends Data

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Big data for government policy: Potential implementations of bigdata for official statistics in Indonesia

Predicting Tourism Demand in Indonesia Using Google Trends Data