Journal of Computational Innovation and Analytics (JCIA)

APPLICATION OF BIBLIOMETRIC ANALYSIS ON ROOT GROWTH ALGORITHM

Wed, 31 Jan 2024 00:00:00 +0800

The root growth algorithm has often been used to solve challenging optimization issues. It is one of the metaheuristic algorithms inspired by root growth in plant behaviors. An article reviewed and analyzed bibliographic data on metaheuristics but is not specific on the topic of root growth algorithm. Therefore, this article presents a bibliometric analysis based on the topic of the root growth algorithm. It reviews the publication from the Scopus database. Based on the search process done on 14 February 2023 using the keywords of root growth algorithm, this article managed to gather 1836 articles from 1976-2023. However, this article only focuses on Engineering, Computer Science, and Mathematics to ensure it relates to the area the researcher wants to explore. Moreover, articles in the year 2023 will also be excluded due to incomplete data. After the researcher limited the data to those areas, it was reduced to 837 articles. This article uses three types of software: Microsoft Excel, VOSviewer software, and Harzing’s Publish or Perish software to analyze the frequency, visualization mapping, and citation metrics analysis, respectively. The finding from the bibliometric analysis shows the researcher has noticed that the total publication keeps increasing rapidly from 2014 until 2022. The country that contributes the most to the root growth algorithm topic is China. Furthermore, the most productive author is Chen, H. and all the top 10 productive authors are from China except for Christofides, P. D., who comes from the United States. The top 10 sources of root growth algorithm research contributed over a quarter of the total articles (231 or 27.60%). The results of this study have significant implications for increasing the number of practices using the root growth algorithm in future research. Last but not least, bibliometric analysis on the topic of root growth algorithm is needed to identify influential publications, understand research trends, evaluate research impact, and identify potential research gaps in a concise manner, which helps researchers and readers stay informed, make informed decisions, and contribute to the advancement of knowledge in the field.

THE IMPLEMENTATION OF THE ALEXANDER-GOVERN TEST IN FACTORIAL DESIGN ANALYSIS

Nurul Syafiqah Ishak Latfi, Suhaida Abdullah — Wed, 31 Jan 2024 00:00:00 +0800

This study proposed to evaluate the performance of the Alexander-Govern test (AG test), Analysis of Variance (ANOVA), and t-test by analyzing the Type I error rate. The AG test is regarded as a reliable
control Type I error rate. This technique is insensitive in the presence of heteroscedasticity under a normal distribution. Simulation research was carried out using Statistical Analysis Software (SAS) to assess the effectiveness of the tests that are based on the rate of Type I error. By creating the conditions that could highlight the strengths and weaknesses of each test, three variables are being manipulated: sample size, variance heterogeneity, and type of pairings. The performance of the AG tests is convincing when it is able to control the Type I error rates better compared to ANOVA under all conditions of heterogeneous variances. Meanwhile, the ANOVA performs best only when the variances are homogenous. A real data experiment was applied to validate the result. In the battery life design experiment, the p-value using the AG test and ANOVA are computed and compared. The AG test provides valid results when it can test the main effect and the interaction effect, as well as the ANOVA. With good performance in the simulation study, the AG test can be considered a good alternative
to the ANOVA when the assumptions of the homogeneity of variances are violated in the case of factorial design.

EXPLORING THE EFFICACY OF DIGITAL MEME MARKETING CAMPAIGNS IN GENERATING LEADS WITHIN THE GENERATION Z DEMOGRAPHIC

Ronald R. Fernandez, Chin Uy, Ronaldo A. Manalo — Wed, 31 Jan 2024 00:00:00 +0800

Quite recently, marketers have used memes because they entice customers’ attention. It serves as a means to have a dialogue between customers and companies. Some companies would even hire people to browse the internet for relevant memes. The emergence of Generation Z, who are digital natives, has brought significant changes to how businesses interact with their target audience. One potential strategy is to use digital memes as a marketing tool to engage them. The study, using 271 respondents, investigates the use of the aforementioned tool to determine its appropriateness in lead generation based on the Attention, Interest, Desire, Action (AIDA) model. Using the software Analysis of Moment Structures (AMOS), a descriptive exploratory design using Structural Equation Modeling (SEM) was used to investigate the relationship between the different variables in the study. The findings show that attention impacts interest, interest influences desire, and desire significantly affects action at a significance level of p < 0.01. The study revealed that Digital memes are an effective tool that can be utilized to generate leads. Businesses can use digital memes to engage with the Generation Z segment. However, prudence in creating memes must align the message with corporate values and be relevant to the target audience.

SOLVING BIPOLAR FULLY FUZZY SYLVESTER MATRIX EQUATIONS WITH NEGATIVE FUZZY NUMBERS

Nazihah Ahmad, Neendha Cheah Soo Thape — Wed, 31 Jan 2024 00:00:00 +0800

Sylvester matrix equations play a crucial role in control theory for controller design. Bipolar Fully Fuzzy Sylvester Matrix Equations (FFSME), incorporating both positive and negative components, are employed in controller design to address uncertainties that may affect a system’s performance and stability. However, there is not much existing research on combining bipolar fuzzy numbers and FFSME,
and most of them mainly deal with positive coefficients. Thus, this paper presents a method that enables solving the negative coefficient of bipolar FFSME in the form of Left-Right (LR) triangular fuzzy
numbers using an Associated Bipolar Linear System (ABLS). To obtain the ABLS, bipolar FFSME is transformed into a bipolar Fully Fuzzy Linear System (FFLS) using the Kronecker product and Vecoperator. Subsequently, the solution is derived through the inverse method, and the equation of the ABLS is rearranged as a bipolar fuzzy matrix. Additionally, this paper provides two numerical examples to illustrate the applicability of the constructed method.

FORECASTING RAINFALL VOLUME IN SELANGOR WITH A COMBINED ARIMA MODEL

Huey Yin Tee, Rosnalini Mansor — Wed, 31 Jan 2024 00:00:00 +0800

Flash flood is the most hazardous type of flooding, mainly caused by extensive rainfall. It also can cause significant harm to a community’s economy, ecology, and society without warning at an irrational pace. Therefore, this study was conducted to detect the time series element within the rainfall data, select the optimal model, and make predictions about the volume of rainfall in Selangor. A variety of univariate time series models were utilized, including the naïve model, decomposition model, Autoregressive Integrated Moving Average (ARIMA) model, exponential models, and combined models. Historical monthly rainfall data collected from Petaling station and Subang station from 2018 to 2022 were used to estimate the parameters of the models, and the model was evaluated for the smallest error of measurements. Previous research mostly focused on complex methodologies for forecasting rainfall. However, this research aimed to identify a simple tool for fast prediction of rainfall. The results showed that the combination of the ARIMA (2,0,3) model from Petaling Station and the ARIMA (4,0,4) model from Subang station were able to capture the trends and seasons in the time series with the lowest error of measurement on short-term predictions of rainfall volume. Furthermore, the study delves into the concept of combined time series models, which are blended using weighted performance measures to enhance prediction accuracy further. The research acknowledges certain limitations of univariate time series models, notably their inability to account for intricate interactions among environmental variables and potential long-term trends, such as those stemming from climate change. Overall, the study explores the potential of combining models to refine predictions for forecasting rainfall volume in Klang Valley.