Binus University

Field research can be associated with both qualitative and quantitative research methods, depending on the problems faced and the goals to be achieved. The success of data collection in the field research depends on the determination of the appropriate sampling technique, to obtain accurate data, and reliably. In studies that have problems related to specific issues, requiring a non-probability sampling techniques one of which is the snowball sampling technique. This technique is useful for finding, identifying, selecting and taking samples in a network or chain of relationships. Phased implementation procedures performed through interviews and questionnaires. Snowball sampling technique has strengths and weaknesses in its application. Field research housing sector become the case study to explain this sampling technique.

The increasing availability of online information has triggered an intensive research in the area of automatic text summarization within the Natural Language Processing (NLP). Text summarization reduces the text by removing the less useful information which helps the reader to find the required information quickly. There are many kinds of algorithms that can be used to summarize the text. One of them is TF-IDF (TermFrequency-Inverse Document Frequency). This research aimed to produce an automatic text summarizer implemented with TF-IDF algorithm and to compare it with other various online source of automatic text summarizer. To evaluate the summary produced from each summarizer, The F-Measure as the standard comparison value had been used. The result of this research produces 67% of accuracy with three data samples which are higher compared to the other online summarizers.

Wire arc additive manufacturing (WAAM) is steadily increasing with significant research work are underway. The ability of WAAM to manufacture a large-scale product at a lower cost and shorter lead times has increased its development. The efficiency or potential of WAAM to become a new lead in the additive manufacturing industry is believed to be realized by a few factors. High efficiency and performance require a higher metal deposition rate, which implies higher heat input that negatively affects the manufacturing process. This paper review works for factors associated with heat input on the WAAM process. The factor affecting these properties were explained to identify the optimized WAAM process in terms of heat input. The paper focuses on the impact of heat input on the macrostructure, microstructure and mechanical properties of the parts deposited in the WAAM process. This review also discussed the effect of heat input used by eight different types of arc welding technologies, with appropriate use of wire materials. The study also highlights that heat input affected the microstructure of the WAAM, causing significant changes in grain structure, grain size and pore area percentage. Besides, it can be suggested that grain structure has a strong influence on the WAAM materials' mechanical properties.

This study reviews the most recent literature on UTAUT (Unified Theory of Acceptance, and Use of Technology) and UTAUT 2(Unified Theory of Acceptance, and Use of Technology) 2 by focusing on the findings and recommended future research. The papers, proceedings and dissertations included in the analysis were identified technology acceptance as the focus of their studies. This search was supplemented various websites which host scientific journals such as Emerald, Science Direct and Google Scholar. The initial search produced 65 papers. The studies examined works which employed UTAUT and UTAUT 2 by focusing on findings on the core constructs of UTAUT to predict Behavioral Intentions. The results confirmed previous studies that the four constructs of UTAUT contributed to Behavioral Intention even though PE seemed to be the most significant contributors. Findings also suggest UTAUT 2 has been more explanatory and list the suggestions for future works. The immediate implications are for researchers who wish to examine behavioral intentions, and managers who wish to ensure the acceptance and use of a new system or technology. This study bears a number of limitations. Number of papers examined is one of them. It would be more accurate to increase the number of paper examined. The other limitation is the ability to draw a statistical conclusion each research examined. This is due to a great variety of research topics, methods, constructs and contexts.

Following the global outbreak of COVID-19 in March 2020, individuals report psychological distress associated with the “new normal”—social distancing, financial hardships, and increased responsibilities while working from home. Given the interpersonal nature of stress and coping responses between romantic partners, based on the systemic transactional model this study posits that perceived partner dyadic coping may be an important moderator between experiences of COVID-19 psychological distress and relationship quality. To examine these associations, self-report data from 14,020 people across 27 countries were collected during the early phases of the COVID-19 pandemic (March–July, 2020). It was hypothesized that higher symptoms of psychological distress would be reported post-COVID-19 compared to pre-COVID-19 restrictions (Hypothesis 1), reports of post-COVID-19 psychological distress would be negatively associated with relationship quality (Hypothesis 2), and perceived partner DC would moderate these associations (Hypothesis 3). While hypotheses were generally supported, results also showed interesting between-country variability. Limitations and future directions are presented.

Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled and the wide availability of massive weather observation data have motivated many researches to explore hidden hierarchical pattern in the large volume of weather dataset for weather forecasting. The purposes of this research are to build a robust and adaptive statistical model for forecasting univariate weather variable in Indonesian airport area and to explore the effect of intermediate weather variable related to accuracy prediction using single layer Long Short Memory Model (LSTM) model and multi layers LSTM model. The proposed forecasting model is an extension of LSTM model by adding intermediate variable signal into LSTM memory block. The premise is that two highly related patterns in input dataset will rectify the input patterns so make it easier for the model to learn and recognize the pattern from the training dataset. In an effort to achieve a robust model for learning and recognizing weather pattern, this research will also explore various architectures such as single layer LSTM and Multiple Layer LSTM (4 layers LSTM). The dataset is weather variable data collected by Weather Underground at Hang Nadim Indonesia airport. This research used visibility as predicted data and temperature, pressure, humidity, dew point as intermediates data. The best model of LSTM in this experiment is multiple layers LSTM and the best intermediate data is pressure variable. Using the pressure variable this model has gained the validation accuracy 0.8060 and RMSE 0.0775.

Online food delivery service (OFDS) has been widely utilized during the new normal of the COVID-19 pandemic, especially in a developing country such as Indonesia. The purpose of this study was to determine factors influencing customer satisfaction and loyalty in OFDS during the new normal of the COVID-19 pandemic in Indonesia by utilizing the extended theory of planned behavior (TPB) approach. A total of 253 respondents voluntarily participated and answered 65 questions. Structural equation modeling (SEM) indicated that hedonic motivation (HM) was found to have the highest effect on customer satisfaction, followed by price (P), information quality (IQ), and promotion (PRO). Interestingly, this study found out that usability factors, such as navigational design (ND) and perceived ease of use (PEOU) were not significant to customer satisfaction and loyalty in OFDS during the new normal of COVID-19. This study can be the theoretical foundation that could be very beneficial for OFDS investors, IT engineers, and even academicians. Finally, this study can be applied and extended to determine factors influencing customer satisfaction and loyalty in OFDS during the new normal of COVID-19 in other countries.

Big data encompasses social networking websites including Twitter as popular micro-blogging social media platform for a political campaign. The explosive Twitter data as a respond of the political campaign can be used to predict the Presidential election as has been conducted to predict the political election in several countries such as US, UK, Spain, and French. The authors use tweets from President Candidates of Indonesia (Jokowi and Prabowo), and tweets from relevant hashtags for sentiment analysis gathered from March to July 2018 to predict Indonesian Presidential election result. The authors make an algorithm and method to count important data, top words and train the model and predict the polarity of the sentiment. The experimental result is produced by using R language and show that Jokowi leads the current election prediction. This prediction result is corresponding to four survey institutes in Indonesia that proved our method had produced reliable prediction results.

The spread of COVID-19 has caused it to be a pandemic. This has caused massive disruption to our daily lives, both directly and indirectly. We aim to utilize Machine Learning model in attempt to forecast the trend of the disease in Indonesia with finding out the approximation when normality will return. This study uses Facebook’s Prophet Forecasting Model and ARIMA Forecasting Model to compare their performance and accuracy on dataset containing the confirmed cases, deaths, and recovered numbers, obtained from the Kaggle website. The forecast models are then compared to the last 2 weeks of the actual data to measure their performance against each other. The result shows that Prophet generally outperforms ARIMA, despite it being further from the actual data the more days it forecasts.

Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled with the wide availability of massive weather observation data and the advent of information and computer technology have motivated many researches to explore hidden hierarchical pattern in the large volume of weather dataset for weather forecasting. This study investigates deep learning techniques for weather forecasting. In particular, this study will compare prediction performance of Recurrence Neural Network (RNN), Conditional Restricted Boltzmann Machine (CRBM), and Convolutional Network (CN) models. Those models are tested using weather dataset provided by BMKG (Indonesian Agency for Meteorology, Climatology, and Geophysics) which are collected from a number of weather stations in Aceh area from 1973 to 2009 and El-Nino Southern Oscilation (ENSO) data set provided by International Institution such as National Weather Service Center for Environmental Prediction Climate (NOAA). Forecasting accuracy of each model is evaluated using Frobenius norm. The result of this study expected to contribute to weather forecasting for wide application domains including flight navigation to agriculture and tourism.

During the COVID-19 (coronavirus disease 2019) pandemic, universities had to shift from face-to-face to emergency remote education. Students were forced to study online, with limited access to facilities and less contact with peers and teachers, while at the same time being exposed to more autonomy. This study examined how students adapted to emergency remote learning, specifically focusing on students’ resource-management strategies using an individual differences approach. One thousand eight hundred university students completed a questionnaire on their resource-management strategies and indicators of (un)successful adaptation to emergency remote learning. On average, students reported being less able to regulate their attention, effort, and time and less motivated compared to the situation before the crisis started; they also reported investing more time and effort in their self-study. Using a k -means cluster analysis, we identified four adaptation profiles and labeled them according to the reported changes in their resource-management strategies: the overwhelmed, the surrenderers, the maintainers, and the adapters. Both the overwhelmed and surrenderers appeared to be less able to regulate their effort, attention, and time and reported to be less motivated to study than before the crisis. In contrast, the adapters appreciated the increased level of autonomy and were better able to self-regulate their learning. The resource-management strategies of the maintainers remained relatively stable. Students’ responses to open-answer questions on their educational experience, coded using a thematic analysis, were consistent with the quantitative profiles. Implications about how to support students in adapting to online learning are discussed.

This study attempts to investigate whether Social Media Marketing Activity (SMMA) carried out by companies / brands have a positive impact on their brand equity, e-WOM distribution on social media and customers' purchase intention. The objective of this paper is to investigate the impact of SMMA towards customers' purchase intention. The researchers tried to survey the results of previous studies to give more benefits to the readers and researchers in this area of study. Research data was collected using an online questionnaire survey of 114 participants of Instagram users in Indonesia. The results of structural equation modelling supported the current model's validity and indicated a positive effect of SMMA towards brand equity. Moreover, brand equity had a positive impact on e-WOM; and e-WOM maintained a positive influence towards customers' purchase intention. Finally, SMMA also has showed a direct impact to customers' purchase intention.

Industry 4.0 brings a new challenge for incumbent firms to anticipate new business model offered by emerging entries. The digital transformation is required by incumbent to develop innovation on product and service business model based on customer experience orientation. To support this transformation, strong digital leader is important to assure the development of this transformation. The study on the role of digital leadership on business model innovation and customer experience has not been explored, significantly, Hence, this research aims at assessing the role of digital leadership, whether it directly or indirectly influences the customer experience orientation in developing business model innovation. This study was conducted through survey to 88 senior leader respondents from Indonesia telecommunication firms, in which Smart-PLS application was used to analyze the data. The result show that digital leadership had direct and indirect impacts on customer experience orientation in developing business model innovation. The practical implications of these findings are recommended for the senior leader of management of telecommunications industries in Indonesia to strengthen digital leadership capability in conjunction with the development of business model innovation and customer experience orientation. Further research can be explored by expanding the sample, industry, statistical application and longitudinal study.

This paper investigates the impact of social media marketing on brand image and brand trust toward the purchase intention of Indonesian Male's Skincare. The study proposes a model that shows the effect of skincare marketing strategies through social media for male millennials generations. A quantitative approach is used to collect the data to support the model using online surveys. The data samples are collected from 203 male respondents using non-probability sampling techniques with convenience sampling method. The results are analysed with PLS-SEM methodologies by Smart-PLS, considered to be applied when the research is exploratory. The research result shows that social media marketing had a significant impact on brand image and brand trust. Moreover, brand trust and brand image had significant impacts on purchase intention. With brand image and brand trust 56.1 percent explained the purchase intention, 53.6 percent social media marketing explained the brand image, and 65.4 percent of the social media marketing explained the brand trust as well.

In the recent years, electronic commerce (e-commerce) in Indonesia has growing rapidly. E-commerce became an opportunity for company to increase their selling. Electronic payment (e-payment) was developed to facilitate e-commerce transactions beetwen consumer and seller. In this study, we will investigate consumer's intention to use e-payment. The proposed research model was developed by extending the unified theory of acceptance and use of technology (UTAUT) with culture and perceived security into the model, in order to determine the significance factors that influence acceptance of e-payment technology. Through this model, researchers can have a more accurate explanation of the consumer behavior not only in terms of acceptance of the technology, but other factors considered influential on consumers such as culture and perceived security in the origin country. This model will be used to examine consumer's behaviour in Indonesia.

A* is a search algorithm that has long been used in the pathfinding research community. Its efficiency, simplicity, and modularity are often highlighted as its strengths compared to other tools. Due to its ubiquity and widespread usage, A* has become a common option for researchers attempting to solve pathfinding problems. However, the sheer amount of research done on the topic makes it difficult to know where to start looking. With this paper, we hope to create an accessible, up to date reference on the current state of the A* search algorithm for future pathfinding projects to consider. This paper examines A-Star’s current usage in the field of pathfinding, comparing A* to other search algorithms. It also analyzes potential future developments for A-Star’s development. A* cannot keep up with the demands of current pathfinding problems. Other algorithms can maintain the same performance while also demanding less overhead and this problem only grows worse as grid size increases. However, use of innovative modifications such as different heuristic types or secondary components to the algorithm allow A* to achieve very fast times with good accuracy when dealing with large maps, while only having slightly increased overhead costs for the modifications. While beginning to show its age, improved algorithms based on the classic A* algorithm are more than capable of keeping up with modern pathfinding demands. These derivative search algorithms such as HPA* is used to overcome the limitations of A*. HPA* can compete with and even surpass its competitors depending on the challenge faced.

The use of social networks is increasing rapidly. Various informations are shared widely through social media, i.e. Facebook. Information about users and what they expressed through status updates are such important assets for research in the field of behavioral learning and human personality. Similar researches have been conducted in this field and it grows continually till now. This study attempts to build a system that can predict a person’s personality based on Facebook user information. Personality model used in this research is Big Five Model Personality. While other previous researches used older machine learning algorithm in building their models, this research tries to implement some deep learning architectures to see the comparison by doing comprehensive analysis method through the accuracy result. The results succeeded to outperform the accuracy of previous similar research with the average accuracy of 74.17%.

Research on the digital economy continues to develop but is limited to one country and/or field. From a bibliometric retrospective review, this study purposes to visually research mapping and research trends in the field of the digital economy on an international scale. This study used bibliometric techniques with secondary data from Scopus. Analyze and visualize data using the VOSViewer program and the analyze search results function on Scopus. This study analyzed 2,784 scientific documents published from 1984 to 2019. This study proposes a grouping of digital economy research themes: Information systems, Digitization, E-commerce, Education, Engineering, Marketing, Industrial revolutions, and Information technology, abbreviated as IDEEEMII research themes.

The outbreak of the COVID-19 pandemic has dramatically shaped higher education and seen the distinct rise of e-learning as a compulsory element of the modern educational landscape. Accordingly, this study highlights the factors which have influenced how students perceive their academic performance during this emergency changeover to e-learning. The empirical analysis is performed on a sample of 10,092 higher education students from 10 countries across 4 continents during the pandemic's first wave through an online survey. A structural equation model revealed the quality of e-learning was mainly derived from service quality, the teacher's active role in the process of online education, and the overall system quality, while the students' digital competencies and online interactions with their colleagues and teachers were considered to be slightly less important factors. The impact of e-learning quality on the students' performance was strongly mediated by their satisfaction with e-learning. In general, the model gave quite consistent results across countries, gender, study fields, and levels of study. The findings provide a basis for policy recommendations to support decision-makers incorporate e-learning issues in the current and any new similar circumstances.

Nowadays, customer awareness on environment friendly products is getting improved and there is an increase trend on green marketing strategy. The carried-out strategy has an objective of improving the customer care and purchasing intention on environment friendly products. Reviewing the issue of customer behavior, this study aims to review the correlation among green advertising, green brand image and customer green awareness on environment friendly products and their impacts to purchase intention. The study was conducted through a survey among 102 customers of Supermarket in Bandung City who have experience on friendly products. Data from the customers were obtained through a questionnaire, tabulated and processed by path analysis using SmartPLS. In order to emphasize research result, the research hypothesis test was conducted. Research finding explains that green advertising was assessed to be important by the customer and it can improve the customers' green awareness. On the other hand, it is stated that there was an impact of green awareness on improving customer purchasing intention on the environmentally friendly product. This study is useful for the supermarket in Indonesia particularly in understanding customer behavior to the environmentally friendly product. So, the implementation of the marketing strategy is more precise. Besides, this study can be an input for the Indonesian Government in implementing a regulation associated with the global warming issue through research on environmental friendly product.