Informatics Publishing Limited

Las Johansen B. Caluza ¹

Affiliations
1 Leyte Normal University, PH

Abstract

This study explored and theorized the significant effects of typhoons relative to ICT products and services in the economy of a country that is most visited by this kind of catastrophe. Anent to this, the availability of information in the cloud such as economic indicator relative to ICT from the World Bank Organization (WBO) website and the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) was exploited through data mining, specifically cluster analysis to in-depth the data analysis and generate a theory based on facts through dendrogram, amalgamation steps, and observation by cluster centroids. As a result based on the generated data using the cluster analysis, having three (3) clusters with twelve observations, it was speculated that there was a rise of ICT products and services even if the country where most visited by typhoons. This implies that ICT investment in the Philippines will continuously gain since it was not affected by this catastrophe. Hence, this negates some speculations that the ICT economy will be affected by this catastrophe. Finally, it was theorized that a country that is most visited by typhoons does not affect the economic growth relative to ICT products and services.

Keywords

Climate Change, Cluster Analysis, Economy, Information and Communication Technology, Typhoon.

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Las Johansen B. Caluza ¹

Affiliations
1 Leyte Normal University, PH

Abstract

Objectives: Food has been a major concern in the Philippines regarding production and utilization. The main purpose of this research is to develop a model to venture the future of food production and consumption. Methods/Analysis: This study explored using data mining technique using regression and cluster analysis to establish a new knowledge from a data set ranging from 1990 to 2013, where the data taken and extracted into a software readable formatfrom the Philippine Government repository like the PhilFSIS and PAGASA. This research utilized software such as MiniTab 13.20 and Weka 3.7to facilitate model generation. Findings: The data generated were analyzed and found out that only poultry products were not affected by the typhoons. Thisresult is considered to be unique in nature considering that the Philippines is the most visited strong typhoons in the world, and can affect the poultry production. In the model generated, a significant finding also reveals that ischolar_main crops, poultry products, and hog production increase if there is an increase of typhoons that enter the PAR. Finally, a theory was created based on the interpretation of the generated data from the data mining processes. Novelty/Improvement: These results only suggest that the authorities should look into the risk of declining of the production of other commodities and create a more climate resilient plan of production to sustain the future needs of the consumers. Further study must be conducted to establish the validity of the result of this study.

Keywords

Cluster Analysis, Climate Change, Food Security, Regression, Typhoon, Theory Development.

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Las Johansen B. Caluza ¹

Affiliations
1 Information Technology and Computer Education Department, Leyte Normal University, 6500 Leyte, PH

Abstract

Objectives: The advent of information technology causes a significant impact on pedagogy and using ICT technologies in school-related activities. With this argument, determining the teacher’s ICT competence is expected to yield a better or positive impact on the learners and the performance of the teachers itself, however, no model was developed in predicting teachers ICT competence. In line with this, it is empirical to study and develop a model that will help the university in predicting teacher’s ICT competence. Methods: Data mining approach utilizing J48 algorithm was applied in this paper to create a model suitable for the actual teachers’ characteristics in the University. Moreover, Cross-validation technique was used to validate the dataset to have an optimum and acceptable model and generating the Receiving Operating Characteristics Curve (ROC) Area under ROC Curve technique. Findings: Decision tree model and decision rule for classification were created. Additionally, there were 92.78% correctly classified with an AUC weighted mean of 92.4%. Also, the model has very high acceptability and accuracy in predicting Teacher’s ICT Competence. However, it also revealed that many teachers still need more exposure in utilizing ICT technologies in pedagogy and any school-related activities. Application/Improvements: The result of this study can be a basis for developing software that will automatically categorize or classify the Teacher’s ICT competence. For more improvement of this paper and the model, it is suggested to add additional parameters to have more factors involved in predicting Teacher’s ICT competence.

Keywords

Blended Learning, Decision Tree, ICT Competency, ICT Domain, ICT Pedagogy, J48 Algorithm, Machine Learning

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