Implementation of the K-Medoids Algorithm for Data Clustering of Covid 19 Cases in West Java

The Covid 19 pandemic has hit Indonesia for almost 15 months since March 2020. The virus has spread to all provinces in Indonesia. Various efforts were made to be able to reduce or prevent the spread of the coronavirus, including the implementation of the PSBB in various areas including in West Java province. In this study, the objective of this research is to cluster the data on cases of Covid 19 in West Java which are recapitulated daily based on districts/cities that occurred on May 20, 2021. For the clustering process, the K-medoids algorithm is used which determines 3 clusters based on the variables used, namely discarded close contact, suspects discarded, probable completed, probable died, totally positive, positive recovered, and positive died. For data processing, a calculation analysis was carried out using the stages in the K-medoids algorithm and the Rapidminer application with high cluster mapping of 6 districts/cities, medium clusters there were 19 districts/cities, while low clusters had 2 districts/cities. The results of the analysis are expected to provide information about the distribution and mapping of clusters in West Java province.

Visualizing Artificial Intelligence Used in Education Over Two Decades

With the rapid development of computer science, use of artificial intelligence (AI) in education has caught much attention across the world although it is still a young field with many under-explored research elements. Through visualizing study with bibliometric evaluation and taxonomy of the literature using both VOSviewer and CiteSpace, this study provided references for readers in terms of cluster mapping on the basis of keywords, bibliographic coupling of countries, cluster mapping on the basis of co-citations, citation counts, bursts, betweenness centrality, and sigma. Researchers could also take the findings of this study into serious consideration when they set about researching effectiveness, efficiency, or usefulness of AI in education. Future research into use of AI in education will most likely need interdisciplinary cooperation between computer science, statistics, education, cognition, and robotics.

Spatial agglomeration and specialization in emerging markets

Purpose The paper aims to investigate the role of specialization and agglomeration forces on industry performance in an emerging market, namely, Thailand. In particular, the impact of clusters and the influence of complexity will be tackled. Design/methodology/approach The methodology used is based on the work of Delgado et al. (2014). Industries and clusters are assigned to a certain category according to their respective level of specialization and complexity. Performance measures are then computed for each category. Findings It was found that the agglomeration of similar industries and co-located and related industries increase the performance of firms in terms of gross output per employee and remuneration per employee. Moreover, the increase of performance induced by the complexity level of an industry was closely related to the level of specialization. Originality/value Building on a cluster mapping, this study brings new insight on the effect of specialization and agglomeration on performance in emerging markets. In fact, the paper show how performance can be enhanced in less sophisticated and developed economies.

Computational RSM Modeling of Neuromorphofunctional Relations of Dentate Nuclear Neurons and Dentatostriate Inter-Cluster Mapping with the Dentatostriate Neural Network Reconstruction: RLSR/PCR Regression and Canonical Correlation Analysis

Aim: The aim of this study is to find relational connections (interdependence) between the two most general categorical aspects of a neuron, i.e., between the form (morphology) and its function, using as a model for this task dentate nucleus neurons. Furthermore, the configuration of the dentatostriate nucleotopic inter-cluster mapping of the dentatostriate neural network is investigated in order to determine mutual, inter-neuronal, neuromorphofunctional remote influence, i.e. the neuromorphofunctional relations at the level of a neural network.Materials and methods: (Semi) virtual dentate and neostriate adult human neuronal samples were used. Neuromorphological parameters of each neuron have been directly measured, i.e. experimentally determined, whereas the corresponding neurofunctional parameters have been theoretically obtained. The neuromorphological parameters determine the following properties of a neuron: neuron shape, compartmental length and size/ surface, dendritic branching, complexity and organization of neuronal morphology. The group of neurofunctional parameters determines functional aspects of action potential (AV/AP), as well as neurofunctional properties of the perikaryodendritic compartment of a neuron. Data analysis is performed using response surface (RSM) modeling, along with partial least-squares (PLSR) and principal component regression analysis (PCR), accompanied by canonical and Pearson correlation analysis. A stepwise algorithm formulates the complete data analysis.Results: Obtained RSM models represent response-predictor relations, where a neuromorphological/functional response parameter is expressed as a function in terms of parameters of other category (morphology/function). Additionally, RSM modeling is also used to decipher the symmetry of the dentatostriate inter-cluster neural network by the corresponding inter-cluster inter-nuclear mapping, using so-called integral parameters/variables, obtained on a computational, theoretical manner. The obtained network is a fully connected, symmetric, Hopfield neural network.Conclusion: Neuronal morphology and function are definitely interrelated and depend on each other. By intensity, however, this interconnectedness can be treated as mild to moderate. It is determined by elementary neuromorphofunctional relations, observed at the macroscopic, phenomenological level, i.e. only through measured parameters as their observable and explicit manifestation without considering the microscopic, molecular causality of them. These relations are the strongest when acting upon a single neuron and their mutual remote influence on each other weakens in neural circuits and networks up to 10% of deterministic relational interconnection strength observed at the level of single neuron relations.

COMPARISON OF EFFECTIVENESS OF CLUSTER MAPPING, FLOW CHARTING, AND DOUBLE/ TRIPLE ENTRY ON TEACHING WRITING SKILL

The study was aimed to examine the effectiveness of three writing techniques, namely Cluster Mapping, Flow Charting, and Double/ Triple Entry in improving the students’ writing skill. The research technique used was descriptive comparative technique using a quantitative approach. It compared the equations and differences as phenomena to find what factors/ situations that can cause the occurrence of a particular event. The data collection techniques were Documentation study by taking the students’ writing product and interview by giving the question and answer while face to face between the interviewer with the respondent using a tool called interview guide. The try out was conducted to measure validity and reliability. The two ways ANOVA was conducted to test the hypotheses, two –way analysis of variance with F-test at the 5% (0.05) level of significance. The subject of this study was the fourth semester English students with a total of 22 people. The English students’ writing skill using three techniques was tobe the object under the study. The findings of the study were the average writing score of students using Cluster Mapping was 56.5, the average score of students’ writing using Flow Charting was 49.6, and the average score of students’ writing using Double/ Triple Entry was 63.0. It can be concluded that those three techniques of writing skill are effective to use but the most effective is the Double/ Triple Entry technique. Keywords: cluster mapping, flow charting, double/triple entry, writing skill Keywords: Cluster Mapping, Flow Charting, Double/ Triple Entry, Writing Skill

Characterization of Fire Regime Descriptors in Botswana Using Remotely Sensed Data

Despite its role and relevance in environmental management at all scales the use of fire has been contentious. The absence of information on fire parameters compounds the situation. This study derives fire parameter information for Botswana by analyzing MODIS fire data for (2001-2012), using conditional statements, and cluster mapping in ArcGIS. The study also related the fire information to other variables to examine how they interact with fire. The results of the study indicates that over the 12 year period the burned area has exhibited an upward trend. It has also shown that most of the fire in the country occur over the late dry season when the fires are potentially destructive. A south-north transect of fire frequency is observed, accompanied by an inverse relationship between frequency and intensity. Of all the factors, rainfall (0.638) and biomass(NDVI) (0.355) were the most significant contributors to the fire activity. The study demonstrated the utility of the MODIS fire data in characterizing the fire regime of the country and thus contribute to the policy process.