Digital Society Ecosystem Impact on Creative Industry

Industry 4.0 phenomenon has emerged since many technological breakthroughs developed in the past decades. Human well-being behavior are basically influenced by the digital technology. The current customers incline the need for customized products. This situation drive the production paradigm shift from the mass production to the individual production. This paradigm shift force companies to own more resources. Companies’ collaboration is a way to win the competition. Industrial revolution era bring the fact that dominant economic activity is coming from a strong business ecosystem. The major impact of digitalization is faced by the creative industries, an industry priority and a \u27laboratory\u27 for studying economic transformation and modern society. This paper will review the digitalization in industry 4.0 era, business ecosystem and society shift, and the digitalization impact on creative industry. Keywords Industry 4.0; business ecosystem; society shift; creative industr

Service Quality Analysis of Online Travel Agencies (Ota) Using Multiclass Classification

The simplicity provided by Online Travel Agencies (OTA) does not always make customers feel satisfied. Sometimes the customers get some problems with the company services. This finally led customers to give their opinion on social media. Large numbers of data in social media are capable to be an information source for the company to get customer insight. This study aims to determine the quality of OTA services based on customer opinions on social media Twitter. The method used in this study is a multiclass classification with Naïve Bayes Classifier model. Furthermore, each opinion is classified into positive and negative sentiment groups. Multiclass classification results show that Traveloka's service quality is not good enough because six of the seven dimensions of service quality tend to have a negative sentiment. While the quality of Tiket.com and Pegipegi services can be assumed to be quite good because three of the seven dimensions of service quality get the more positive sentiment

Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia

Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively

Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia

Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively