The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)最新文献

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Preface: The 5th Innovation and Analytics Conference & Exhibition (IACE 2021) 23-24 November 2021, Virtual Conference 第五届创新与分析会议与展览(IACE 2021) 2021年11月23日至24日，虚拟会议

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/12.0010101

引用次数: 0

Detecting chaos in time-series data of localized measles cases in the Philippines 菲律宾局部麻疹病例时间序列数据的混沌检测

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/5.0092845

M. Decena

引用次数: 0

Mathematical models of performance in licensure examination for teachers of Nueva Vizcaya State University 新比斯开州立大学教师执照考试成绩的数学模型

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/5.0092741

Jay Roy D. Velasco

引用次数: 0

Solving arbitrary complex fully fuzzy linear systems for a simple electric circuit 求解任意复杂的全模糊线性系统的简单电路

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/5.0093461

N. Ahmad, Neendha Cheah Soo Thape, Wan Suhana Wan Daud, H. Ibrahim

引用次数: 0

Evaluation of the online arabic treasure hunt as learning games activities using the technology acceptance model (TAM) 基于技术接受模型的在线阿拉伯语寻宝游戏学习活动评价

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/5.0092716

A. Mustapa, Salamiah Ab. Ghani, Maryam Abdul Rahman, Zulkifli Nawawi, Mohamad Azwan Kamarudin

The Covid 19 pandemic has shifted the teaching approach in higher education institutions from the traditional face-to-face method to online teaching. Teachers and students are far apart at home to complete the teaching and learning process. Out-of-class language activities are severely affected because they are usually performed face-to-face. This study evaluated the suitability and usability of an online Arabic Treasure Hunt conducted on the Microsoft Teams platform. This study is a quantitative study by using a survey approach questionnaire. Forty-six students are the respondents who participated in online treasure hunt activities. All questionnaire items are adapted from the Technology Acceptance Model to meet the study's requirements. The respondent's assessment is based on a five-point Likert scale: 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree. The study's primary instrument was a questionnaire placed in the 'Treasure World Adventure' channel in Microsoft Teams for participants to complete after the activity. First, the data tested the validity through the Cronbach's alpha method and then tested the study hypotheses. Data is analysed using IBM SPSS Statistics software. The study found a significant positive relationship between the independent variables and the behavioural intent variable. It reflects the acceptance of the technology used by students. The results also demonstrated a high level of variable reliability. Detailed findings and educational implications have been discussed © 2022 Author(s).

新冠肺炎疫情使高等教育机构的教学方式从传统的面对面教学转变为在线教学。老师和学生相隔很远，在家里完成教学过程。课外语言活动受到严重影响，因为它们通常是面对面进行的。本研究评估了在Microsoft Teams平台上进行的在线阿拉伯语寻宝活动的适用性和可用性。本研究采用问卷调查法进行定量研究。受访者中有46名学生参与了网络寻宝活动。所有问卷项目均采用技术接受模型，以满足本研究的要求。被调查者的评估是基于五点李克特量表:1 =非常不同意，2 =不同意，3 =中立，4 =同意，5 =非常同意。该研究的主要工具是在微软团队的“宝藏世界冒险”频道中放置一份问卷，供参与者在活动结束后完成。首先通过Cronbach’s alpha法对数据进行效度检验，然后对研究假设进行检验。数据分析使用IBM SPSS统计软件。研究发现，自变量和行为意图变量之间存在显著的正相关关系。它反映了学生对所使用的技术的接受程度。结果也证明了高水平的可变信度。详细的研究结果和教育意义已被讨论©2022作者。

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引用次数: 6

Climatic influences on dengue incidence in Baguio city, Philippines: A multiple linear regression approach 气候对菲律宾碧瑶市登革热发病率的影响:多元线性回归方法

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/5.0092781

Joseph Ludwin D. C. Marigmen, R. Addawe

引用次数: 0

Robust estimation of Weibull-Rayleigh parameters Weibull-Rayleigh参数的鲁棒估计

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/5.0094159

Ehab A. Mahmood, Ali Khaleel Dhaiban

引用次数: 0

Stratified sampling using cluster analysis 采用聚类分析分层抽样

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/5.0092740

N. Haron

引用次数: 0

A machine learning modeling prediction of enrollment among admitted college applicants at University of Santo Tomas 圣托马斯大学录取的大学申请者的机器学习建模预测

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/5.0100174

Arturo J. Patungan, Mari Loren M. Francia

引用次数: 0

Customer profiling and segmentation of starbucks Malaysia: Empirical investigation during CMCO 2.0 马来西亚星巴克的顾客分析和细分:CMCO 2.0期间的实证调查

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)

Pub Date : 1900-01-01 DOI: 10.1063/5.0092729

Z. Kamaruzzaman

The economic uncertainties due to Covid-19 pandemic has forced businesses to survive, maintain their long-term profitability and remain competitive through the unexpected market upsets. As businesses have to shut down, jobs are lost, people will suffer and economy will be shrinking. Thus, understanding the purchasing behavior of customer is a vital step not only in building but also in maintaining a business. Good customer management comes from good customer measurement. With the latest advent of customer analytics, businesses nowadays can thoroughly comprehend their consumers at all phases of the purchasing process, recognizing patterns in customer data, forecasting the actions that their customers will do, and then making decisions about how to enhance their business in order to attract new customers and retain existing ones. The objective of this paper is to perform customer profiling and customer segmentation of Starbucks Malaysia during the hit of Covid-19 pandemic in Malaysia. Dataset are collected during the second Conditional Movement Control Order (CMCO 2.0). Customer profiling tries to gain a deeper understanding of customers and describe their personalities types or personas, while customer segmentation is a powerful technique to understand the patterns that differentiate a customer. A customer segment is a grouping of customers that share certain characteristics. In this paper, k-means clustering algorithm is used to segment the customers of Starbucks Malaysia according to their income and spend data. From the clustering analysis, one can determine the optimal number of clusters and comprehend the underlying customer segments to identify the statistical patterns of Starbucks customers. This research will contribute both theoretically where this research can uplift the theoretical foundation of customer analytics and practically towards Starbucks Malaysia and other organization and marketing teams where they can understand their customers better and can increase their revenue with the improved marketing campaigns especially during this long-run Covid-19 crisis. © 2022 Author(s).

新冠肺炎大流行带来的经济不确定性迫使企业生存下来，保持长期盈利能力，并在意想不到的市场动荡中保持竞争力。由于企业不得不关闭，工作岗位减少，人们将受到影响，经济将萎缩。因此，了解客户的购买行为是至关重要的一步，不仅在建立和维持一个企业。良好的客户管理来自于良好的客户评估。随着客户分析的最新出现，如今的企业可以在购买过程的各个阶段彻底了解消费者，识别客户数据中的模式，预测客户将采取的行动，然后决定如何增强业务，以吸引新客户并保留现有客户。本文的目的是在马来西亚Covid-19大流行期间对马来西亚星巴克进行客户分析和客户细分。数据集是在第二个条件移动控制命令(CMCO 2.0)期间收集的。客户分析试图更深入地了解客户，并描述他们的个性类型或人物角色，而客户细分是一种强大的技术，可以了解区分客户的模式。客户细分是一组具有某些共同特征的客户。本文采用k-means聚类算法，根据马来西亚星巴克的顾客收入和消费数据，对他们进行细分。通过聚类分析，可以确定最优的聚类数量，了解潜在的顾客群体，从而确定星巴克顾客的统计模式。这项研究将在理论上做出贡献，因为这项研究可以提升客户分析的理论基础，并在实践中为星巴克马来西亚和其他组织和营销团队做出贡献，他们可以更好地了解他们的客户，并可以通过改进的营销活动增加收入，特别是在这场长期的Covid-19危机期间。©2022作者。

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The 5th Innovation and Analytics Conference &amp; Exhibition (IACE 2021)最新文献

The 5th Innovation and Analytics Conference & Exhibition (IACE 2021)最新文献