Factors Influencing the Conversion to Organic Rice Farming

Heryadi, D. Yadi; Rofatin, Betty

doi:10.18551/rjoas.2018-09.59

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…And as of March 29, COVID-19 the fatalities cases in Indonesia reach the 102 deaths out of 1,155 confirmed cases, which indicates a high risk from COVID-19 in Indonesia. Since March 15, The President of Indonesian, Joko Widodo has advised the citizens to help government reducing the spread of the virus by doing some activities at home for two weeks, to reduce the sprad of coronavirus [9] [24].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics in Healthcare: COVID-19 Indonesia Clustering

2023

JPTCP

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The rapid growth of the Internet and Technology produced a massive amount of data that resulted a phenomenon called Big Data. To process such a complex kind of massive amount of data, an advanced approach and tool is needed that is able to quickly produce results. This approach to analyzing massive amount of data is known as Big Data Analytics. Big data analytics is widely used in various sectors, not to mention the health sector. In the healthcare sector, recently there has been a study that is often carried out in dealing with crisis situations, namely research on implementing big data analytics to provide technological solutions to help deal with pandemics. In this article, we analyze and visualize the data collected from Indonesia. The data analyzed starts from the first case of COVID-19 in Indonesia to present. The proposed solution is to classify the regional case data into a group that can represent the situation of the area. As a result, it is determined based on the data that there are three groups consisting of areas with low risk, moderate risk, and high risk. In addition, this article proposes combining big data analytics technology with cloud technology to facilitate the dissemination of information to citizens to increase awareness about the spread of the COVID-19 virus.

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Section: Introductionmentioning

confidence: 99%

Big Data Analytics in Healthcare: COVID-19 Indonesia Clustering

2023

JPTCP

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“…Customers will get dissatisfied with low quality, including those who appreciate the restaurant's food as well as other patrons. Customers who are dissatisfied will inform at least 15 other people (Heryadi et al, 2023). Because of this, initiatives to raise service and product standards will have a much greater impact on business continuity.…”

Section: Introductionmentioning

confidence: 99%

Analysis of The Influence of E-Service Quality and Brand Awareness on Customer Satisfaction of Marketplace Bukalapak

Rahayu,

D. Yadi Heryadi,

Yulia Novita

et al. 2023

jemsi

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This study aims to investigate how brand perception and the quality of an e-service impact customer satisfaction. This kind of research is quantitative. An online shopping sample was employed to gather the fundamental data for this investigation. The survey was distributed to 100 marketplace users in order to acquire data through unintentional sampling. The investigation's methodology is multiple regression analysis. According to the study's findings, customer satisfaction is significantly influenced by brand image, e-service quality, and brand image.

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“…Of the 2,000 respondents surveyed, 1,872 respondents, or as many as 89.10%, came from the provinces of the island of Java: West Java got the first 25.35%, DKI Jakarta 20.10% was the second most, East Java 18.30% was the third most, and Central Java 13.50% was the fourth most [18]. In an Islamic legal review of pay-later credit practices, it can be seen from the pillars and legal terms of buying and selling that they can be fulfilled because there is a sense of willingness on the part of the buyer and the benefits felt by pay-later users to meet urgent needs [19]. From a legal review of Islamic law, utilizing the pay later feature can increase the loyalty of customers who don't have money but are in dire need of goods.…”

Section: Introductionmentioning

confidence: 99%

Analysis of E-Commerce User Acceptance of Technology-Based Loan Application Features Using The UTAUT Model

K. Pramudito,

Rudin,

Br Ginting

et al. 2023

jidt

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The purpose of this study was to obtain and determine the variables that influence the interest of pay later loan users in DKI Jakarta using the modified UTAUT 2 model by adding one perceived security variable. This study used a quantitative method by distributing questionnaires online to 163 pay later users. Using the PLS-SEM analysis approach with SmartPLS 3.3, of the 11 hypotheses tested, five had a significant effect, and six had no significant effect. The relationship path that has the greatest influence is behavioral intention and use behavior, while the path that has the least influence is social influence on use behavior. The results of this study provide recommendations on performance extension by increasing the performance of the pay later feature. Effort expectancy by making it easy to activate the pay later and providing limit adjustments. Social influence creates attractive ads and expands paid information. Hedonic motivation by improving the appearance of a more attractive user interface and increasing the use of the system with user experience. Price value by providing low interest and appropriate limits. Habit by following the recommendations of all variables as a reference for future improvement steps. Facilitating conditions pay later cooperates with other merchants as a payment option to expand the network. Perceived security by implementing fingerprint locks and enhancing user information security and privacy. Behavioral intention follows all recommendations on the factors that influence intention. and use behavior following the recommendations on the factors that influence the intensity level of the user's use of the pay later.

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Factors Influencing the Conversion to Organic Rice Farming

Cited by 3 publications

References 5 publications

Big Data Analytics in Healthcare: COVID-19 Indonesia Clustering

Big Data Analytics in Healthcare: COVID-19 Indonesia Clustering

Analysis of The Influence of E-Service Quality and Brand Awareness on Customer Satisfaction of Marketplace Bukalapak

Analysis of E-Commerce User Acceptance of Technology-Based Loan Application Features Using The UTAUT Model

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