Organizing Map Research Articles

Implementasi Data Mining untuk mengelompokkan Pasien Rawat Jalan dan Rawat Inap Asuransi Kesehatan

Patient Visits Outpatient and inpatient insurance at Class C Hospitals is increasing from year to year. Increased visits to insurance patients will have an impact on the inpatient and outpatient health services provided. From the increase in patient visits, the data owned by the hospital is increasingly abundant. The data can be used to explore knowledge, find certain patterns.
 To explore knowledge about Inpatient and Outpatient Insurance patients, data mining clustering techniques are used with the Self Organizing Map (SOM) algorithm using R Studio tools. Clustering technique with the implementation of the Self Organizing Map (SOM) algorithm is a technique for grouping data based on certain characteristics which are then mapped into areas that resemble map shapes. The CRISP-DM method is used in this study to perform the stages of the data mining process.
 The results obtained from the implementation of clustering with the Self Organizing Map (SOM) algorithm are obtained 2 clusters representing dense areas and non-congested areas. Dense areas are represented by Internal Medicine Clinic, Surgery Clinic, Eye Clinic, Hemodialysis, Melati Room, Orchid Room, Bougenville Room, Flamboyan Room. Non-crowded areas are represented by General Clinics, Dental Clinics, Obstetrics and Gynecology Clinics, Children's Clinics, Mawar Room and Soka Room

Big social data and customer decision making in vegetarian restaurants: A combined machine learning method

Customers increasingly use various social media to share their opinion about restaurants service quality. Big data collected from social media provides a data platform to improve the service quality of restaurants through customers' online reviews, where online reviews are a trustworthy and reliable source that helps consumers to evaluate food quality. Developing methods for effective evaluation of customer-generated reviews of restaurant services is important. This study develops a new method through effective learning techniques for customer segmentation and their preferences prediction in vegetarian friendly restaurants. The method is developed through text mining (Latent Dirichlet Allocation), cluster analysis (Self Organizing Map) and predictive learning technique (Classification and Regression Trees) to reveal the customer’ satisfaction levels from the service quality in vegetarian friendly restaurants. Based on the obtained results of our experiments on the data vegetarian friendly restaurants in Bangkok, the models constructed by Classification and Regression Trees were able to give an accurate prediction of customers' preferences on the basis of restaurants' quality factors. The results showed that customers’ online reviews analysis can be an effective way for customers segmentation to predict their preferences and help the restaurant managers to set priority instructions for service quality improvements.

İnşaat Ekibi Üretkenlik Tahmininde K-En Yakın Komşu ve Genelleştirilmiş Sinir Ağının Performansının Karşılaştırılması

Construction crew productivity prediction is one of the most important issues that affect the realistic prediction of construction duration and cost. Use of different search algorithms like Feed Forward Neural Network, Ant Colony, Artificial Bee Colony, Particle Swarm Optimization, Radial Based Neural Networks and Self Organizing Maps for crew productivity prediction problem have been discussed in previous studies. However, the significant effect of the coherence between the nature of the data and the characteristics of the method used in prediction performance has generally been neglected. The aim of the current research thus has been to analyse the prediction performance of two contemporary learning algorithms; K- Nearest Neighbour (K-NN) and Generalized Neural Network (GRNN) when applied to three different crew (formwork, tiling and masonry) productivity related data sets with different distribution characteristics. Performance of both methods varied with the changing coefficient of variation values. K-NN outperformed GRNN for all data sets and both of the methods had their worst performance on the dataset with the highest variance.

Development of a benthic spatial ratio index as an indicator of small island deformation

The ability of coral reefs and seagrass beds to reduce wave energy positions these ecosystems as the most reliable natural protection for many small islands, especially for coral islands and atolls. With coral damage reaching 174 hectares per year, there must be changes in the spatial composition of coral reefs and seagrass beds around many small islands in the Spermonde Archipelago, changing the shape of the islands and reducing the quality of life of their inhabitants. This study aimed to map the spatial composition of benthic substrates (coral reefs, seagrass beds, and macroalgae) around small islands over time, and to relate changes in substrate composition to island shape change. The initial study was conducted at Barrangcaddi island from July to December 2018. Three field surveys collected spatial data on benthic substrates (coral, seagrass, macroalgae), bathymetry, waves, tides, and beach profiles. The satellite data used were Sentinel 2 images from 2015 to 2018. Satellite image processing included atmospheric correction using the DOS method, land masking, geomorphic segmentation, and classification using the maximum likelihood, Self Organizing Map and Segmentation methods. Thirteen benthic substrate classes were validated with thematic accuracy ranging from 86% to 95%. The land area of Barrangcaddi Island changed around 6 percent from 2015 to 2018. The benthic spatial ratio index for Barrangcaddi Island shows that the north side of the island was the most vulnerable to coastal abrasion, and the existing benthic substrate is no longer effective in reducing the wave energy that hits the island from this side.

Arctic sea ice melt onset favored by an atmospheric pressure pattern reminiscent of the North American-Eurasian Arctic pattern

The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively.

Corrosion of rebar in concrete. Part III: Artificial Neural Network analysis of chloride threshold data

Active corrosion of carbon steel in reinforced concrete occurs when the chloride ion concentration exceeds the chloride threshold (CT). CT is affected by many physical and chemical parameters including primary variables (pH, corrosion potential, breakdown potential, and temperature) and secondary variables (cemenet composition, concreete porosity, and water/cement ratio). A Kohonen-self organized map (KSOM) and regression artificial neural network (ANN) coupled methodology was developed to find the missing values of independent variables in the sparse database and for quantitatively evaluating the effects of these variables on CT values that are expressed in %TotalCl/cem (or %FreeCl/cem), and [Cl−]/[OH−].

Synoptic mode of Antarctic summer sea ice superimposed on interannual and decadal variability

In contrast to decreased Arctic sea ice extent, Antarctic sea ice extent shows a somewhat increased trend. There is a large interannual variability of Antarctic sea ice, especially in the Pacific sector of the Southern Ocean. The change and variability of Antarctic sea ice in synoptic timescales in the recent decades remain unclear. We identify synoptic modes of variability of Antarctic summer sea ice by applying the Self Organizing Map (SOM) technique to daily sea ice concentration data for the period 1979–2018. Nearly 40% of the variability is characterized by opposite changes between sea ice cover in the Bellingshausen, Amundsen and western Ross Seas and in the rest of the Antarctic seas, and another 30% by meridional asymmetry in the Weddell, Amundsen, and Ross Seas. Most of these spatial patterns may be explained by the dynamics and thermodynamic processes associated with anomalous atmospheric circulations related to the Southern Annular Mode (SAM) with a structure of strong zonal asymmetry. The interannual variability of the sea ice modes appears to have little connection to SAM, and only a weak relation to ENSO. The annual frequencies of SOM node occurrences also show a great decadal variability. Node 9 appears mainly prior to 1990; while node 1 occurs mainly after 1990. The decadal variability of nodes 1 and 9 is associated with the asymmetrical SAM, which results from two wavetrains excited over northern Australia and the southeastern Indian Ocean. These results further highlight the importance of understanding the role of southern mid-to-high latitude atmospheric intrinsic variability in predicting Antarctic summer sea ice variations from synoptic to decadal timescales.

Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM)

Abstract. Weerakoon SR, Somaratne S. 2021. Genetic diversity of weedy rice (Oryza sativa f. spontanea) populations in Sri Lanka: An application of Self Organizing Map (SOM). Asian J Agric 5: 35-43. Weedy rice (WR) (Oryza sativa f. spontanea) has become a major threat in rice cultivation. Discrimination of WR from cultivated rice is difficult since agro-morphology of WR and cultivated rice are overlapping. Molecular markers are useful and can be an informative tool for estimating genetic diversity and relationships in closely related WR eco-types. Self-Organizing Maps (SOM) is an interesting and promising classification tool employing an innovative and data-driven classification method based on unsupervised artificial neural networks. The present study focused on exploring the potential use of SOM to classify WR populations of different eco-climatic zones in Sri Lanka using agro-morphological and molecular data. Separate SOMs for each set of variables, agro-morphological and molecular data were developed. The best SOM was chosen based on the error performance. Findings of SOM analyses showed that certain morphological characters (seedling height, leaf blade width, leaf blade length, culm strength, panicle shattering, seed coat color and leaf angle) and certain molecular characters detected from SSR primers (RM 11, RM 21, RM 14, and RM 280) are important in separation of different WR eco-types satisfactorily. SOM clustering of cultivated, wild, and WR eco-types indicated specific patterns of grouping with respect to climatic conditions of the country. WR eco-types in dry zone and wet zone of the country are closely related to Oryza nivara and O. rufipogon respectively.

The role of interoperability dimensions in building information modelling

Building Information Modelling (BIM) is a paradigm shift from the traditional Architecture, Engineering, and Construction (AEC) industry practices to digital construction delivery processes. BIM provides the capabilities of cost reduction, increase quality, enhanced productivity, and on-time delivery. Despite the numerous advantages of BIM, its adoption in AEC is vulnerable to confrontation. Data interoperability is often cited as a significant barrier to BIM adoption; there is a limited focus on other dimensions of interoperability. In other research areas, such as information systems and electronic government, the evolution of interoperability construct is debated beyond data interoperability. These dimensions of interoperability have yet to be explored in the AEC industry. This research article aims to introduce the BIM Interoperability Adoption Model (BIAM). For this purpose, we investigate the interoperability in multiple dimensions according to the European Interoperability framework and develop BIM Interoperability Model with factors affecting each interoperability dimension. BIAM offers a comprehensive assessment of technical, organizational, semantic, and legal interoperability in intra –organizational AEC units, inter-organizational AEC, and BIM partner organizations and assists as a model in assessing the level of compliance and maturity interoperability. BIAM is proposed to work as a link between the organizational, semantic, legal, and technical factors to determine the interoperability issues in the early stages of BIM adoption. The BIAM model addresses interoperability factors and provides recommendations and specifications to help AEC firms to collaborate more effectively by improving interoperability activities, organizational mapping processes, and improving legal coordination. Furthermore, opportunities for future research related to BIM interoperability are discussed, and addressing these aspects of interoperability will make collaborative project delivery well informed, more structured, and effective.

Study on information system infrastructure in Probolinggo by using Self Organizing Map

The infrastructure of information systems becomes one of the main components that support government managements system. This study is addressed to achieve the infrastructure information mapping in District of Probolinggo. The proposed of the research is getting an infrastructure information map of 29 districts administered that become its reliability recommendation. The system information as website is developed for operational purpose. The Self Organizing Map (SOM) as one of neural networks is used. The clustering results by in field observation data show that the infrastructure information system of 29 districts administered are defined by 3 clusters, specifically 3 districts administered have good infrastructure, 20 have fair infrastructure and 6 districts administered have less infrastructure. Furthermore, the clustering by using SOM shows that have 1 good, 22 fair and 5 less, respectively. As a result, the accuracy of both clustering is less than 70 %. That means, SOM still need in field observation data to evaluate the recommendation.

Supporting of Waste Management in Indonesia Using Self Organizing Map for Clustering Analysis

The amount of waste in Indonesia is continuously increasing, along with the increasing population and welfare. The government already takes various efforts to control the amount of garbage; for example, they prohibit using disposable plastic in any form. Mapping waste generation and waste composition hopefully will help the government (or local government) make a more precise policy. Therefore, this research employed a modern clustering analysis that was Self Organizing Maps (SOM). SOM is an Artificial Neural Network (ANN) that uses unsupervised learning to reduce data dimensions into two dimensions. SOM produces low-dimensional visualizations of high-dimensional data. The data of waste generation and composition were obtained from the National Waste Management Information System in 2017-2018. The variables processed were the daily average of waste per square kilometer (ton/km2), the daily average of waste per person (kg/person), and the daily average of waste per sub-district area (tons/sub-district). The researchers built the 3x3 hexagonal topology. The districts of Brebes, Buleleng, Cilacap, and Jepara were grouped into areas with high waste generation compared to other districts. Meanwhile, based on the composition of garbage, Morowali, Sinjai, and Palangkaraya districts were the districts where the composition of food waste, plastic waste, and textile waste was high. Tableau was used to visualize the result into map.

Integrating risk assessment and business impact assessment in the public crisis management sector

Risk and Vulnerability Assessments (RVAs) are often conducted by public sector organizations to prevent or prepare for hazards and threats. In Sweden, practitioners increasingly adopt approaches that are based on Business Continuity Management (BCM), where Business Impact Assessments (BIA) are used to gain organizational understanding, in favor of more traditional risk assessments. Both processes can be valuable to an organization and they have synergies that can be exploited. In the present paper, a method integrating risk assessment and BIA is suggested. The method was developed through a three-year collaboration with the municipality of Malmö, Southern Sweden, where a design science approach was used to ensure scientific rigor and practical relevance. The suggested method, adapted primarily to municipal departments, was implemented and evaluated longitudinally in a number of iterative phases and provides results perceived as useful by end users. Compared to previous suggestions on how to integrate BIA and risk assessment, our method intentionally limits the workload and complexity of the method in favor of practical applicability in a context where persons responsible for crisis management are not necessarily experts on risk and business impact assessment methods. In addition, the method includes features that have not been included in previous integrations of BIA and risk assessment, such as capability assessments related to critical functions in the occurrence of undesirable events using BIA-type of organizational mapping as an input. Finally, the method also includes steps to facilitate sharing information between municipal departments concerning dependencies and capabilities.

Interdisciplinary Online Hackathons as an Approach to Combat the COVID-19 Pandemic: Case Study.

BackgroundThe COVID-19 outbreak has affected the lives of millions of people by causing a dramatic impact on many health care systems and the global economy. This devastating pandemic has brought together communities across the globe to work on this issue in an unprecedented manner.ObjectiveThis case study describes the steps and methods employed in the conduction of a remote online health hackathon centered on challenges posed by the COVID-19 pandemic. It aims to deliver a clear implementation road map for other organizations to follow.MethodsThis 4-day hackathon was conducted in April 2020, based on six COVID-19–related challenges defined by frontline clinicians and researchers from various disciplines. An online survey was structured to assess: (1) individual experience satisfaction, (2) level of interprofessional skills exchange, (3) maturity of the projects realized, and (4) overall quality of the event. At the end of the event, participants were invited to take part in an online survey with 17 (+5 optional) items, including multiple-choice and open-ended questions that assessed their experience regarding the remote nature of the event and their individual project, interprofessional skills exchange, and their confidence in working on a digital health project before and after the hackathon. Mentors, who guided the participants through the event, also provided feedback to the organizers through an online survey.ResultsA total of 48 participants and 52 mentors based in 8 different countries participated and developed 14 projects. A total of 75 mentorship video sessions were held. Participants reported increased confidence in starting a digital health venture or a research project after successfully participating in the hackathon, and stated that they were likely to continue working on their projects. Of the participants who provided feedback, 60% (n=18) would not have started their project without this particular hackathon and indicated that the hackathon encouraged and enabled them to progress faster, for example, by building interdisciplinary teams, gaining new insights and feedback provided by their mentors, and creating a functional prototype.ConclusionsThis study provides insights into how online hackathons can contribute to solving the challenges and effects of a pandemic in several regions of the world. The online format fosters team diversity, increases cross-regional collaboration, and can be executed much faster and at lower costs compared to in-person events. Results on preparation, organization, and evaluation of this online hackathon are useful for other institutions and initiatives that are willing to introduce similar event formats in the fight against COVID-19.

Comparison of K-Medoids and Self Organizing Maps Algorithm in Grouping Hydrometeorological Natural Disasters in Java Island

Indonesia is a country that is prone to disasters. Based on data from the National Disaster Management Authority, there are 6868 disasters in the last 3 years. Changes in weather, temperature, and wind direction that are quite extreme in Indonesia lead to one of the disasters namely hydrometeorological disasters such as floods, landslides, tornadoes, forest fires, and drought. According to the National Coordinating Agency for Disaster Management, this type of disaster is quite common in Indonesia. Over the past 3 years, Java Island received the highest number of disasters. The National Disaster Management Authority states that tornadoes contribute 21% of all disasters in Indonesia. Therefore, the researcher wants to grouping the cases of hydrometeorological natural disaster in Indonesia, especially in Java. This research is to determine the areas that are the focus of the government in disaster mitigation efforts using K-Medoids Clustering and Self Organizing Maps. Based on the results of this study, it was found that the suitable method used was the Self Organizing Maps Method because it was seen based on a smaller standard deviation value. Cluster one there are 108 members who are groups with relatively low disaster vulnerability, while cluster two there are 10 members are city and district groups that have relatively high disaster vulnerability.

University organizational culture mapping using Organizational Culture Assessment Instrument

Culture is the core element of an organization. Organizational culture must reflect the shared values and beliefs that show people meaning and provide them the guidance to working behaviour. The organizational culture should be constructed by following the expected future values and considering the common value of people in the organization. Organizational Culture Assessment Instrument (OCAI) is one of the structural approaches to evaluate the implementation of organizational culture. Nowadays, many of the public university in Indonesia has been transforming into a more productive and agile organization. The university transformation also needs redefine or evaluate their organization’s values as a part of strategic plan. Applying OCAI framework is necessary to assess the excellence of the transformation process in university. OCAI has applied in our university, i.e.: Sepuluh Nopember Institute of Technology (ITS), to define the organizational culture for the higher agility of the organization in the future. More than 400 university employees, including lecturers and administration staffs, have involved in organization culture’s survey. The results of OCAI assessment showed that ITS tend to perform as the clan and the hierarchy type of organizational culture. While the management capabilities of ITS leaders showed that, the leadership characteristic is related to the clan culture.

Proactive Virtual Machine Scheduling to Optimize the Energy Consumption of Computational Cloud

The rapid expansion of communication and computational technology provides us the opportunity to deal with the bulk nature of dynamic data. The classical computing style is not much effective for such mission-critical data analysis and processing. Therefore, cloud computing is become popular for addressing and dealing with data. Cloud computing involves a large computational and network infrastructure that requires a significant amount of power and generates carbon footprints (CO2). In this context, we can minimize the cloud's energy consumption by controlling and switching off ideal machines. Therefore, in this paper, we propose a proactive virtual machine (VM) scheduling technique that can deal with frequent migration of VMs and minimize the energy consumption of the cloud using unsupervised learning techniques. The main objective of the proposed work is to reduce the energy consumption of cloud datacenters through effective utilization of cloud resources by predicting the future demand of resources. In this context four different clustering algorithms, namely K-Means, SOM (Self Organizing Map), FCM (Fuzzy C Means), and K-Mediod are used to develop the proposed proactive VM scheduling and find which type of clustering algorithm is best suitable for reducing the energy uses through proactive VM scheduling. This predictive load-aware VM scheduling technique is evaluated and simulated using the Cloud-Sim simulator. In order to demonstrate the effectiveness of the proposed scheduling technique, the workload trace of 29 days released by Google in 2019 is used. The experimental outcomes are summarized in different performance matrices, such as the energy consumed and the average processing time. Finally, by concluding the efforts made, we also suggest future research directions.

Mapping of Environmental Conditions and Non-Governmental Organizations in the Implementation of Community-Based Total Sanitation Program (STBM) in Siak River Side Settlement in Pekanbaru City

Background and Objectives: Based on STBM Data in Indonesia in 2018 Implementation of the 5 Pillars of Community-Based Total Sanitation Program (STBM) has not yet implemented 100% of the 5 STBM pillars. While the STBM data in Pekanbaru City in 2018 for the Stop BABS pillar has not reached the 100% target. The purpose of this study was to analyze the application of STBM, Non-Governmental Organizations and find an educational model in the application of Community-Based Total Sanitation Program (STBM) to the sustainable environment in the Siak riverbanks of Pekanbaru City. Material and Methods: Results: Implementation of the 5 Pillars of Community-Based Total Sanitation (STBM) showed that most of the districts had not implemented these 5 STBM pillars. However, the implementation of the 3 STBM pillars has been fully implemented by Payung Sekaki District well, the non-governmental organizations in implementing the STBM program have not been implemented optimally and the community education model was found to be a module used for reference learning to the community in implementing the STBM program. Conclusion: Model The result of this research is in the form of scenarios and modules that can be used by health educators as a guide or guideline in implementing STBM education that is true to the community so that a sustainable environment is implemented.

Framework for the integration of Process Mining into Life Cycle Assessment

An increasing product variance, shorter life cycles and the integration of new products and technologies into existing factories lead to a high complexity in today’s production systems. This makes it difficult to carry out Life Cycle Assessments (LCA) continuously and effectively. Major challenges of LCA are on the one hand a high expenditure of time and on the other hand a static evaluation of individual products. In order to perform dynamic and continuous process analyses, companies increasingly rely on new technologies and methods. Numerous studies have already been able to tap promising potentials by using Process Mining (PM). In contrast to traditional methods of process modeling, PM uses event log data to model the actual production processes. Based on this data, a real-time model is built to identify waste. In a holistic approach, PM is able to automatically uncover social and organizational networks and map them in a simulation model. According to the current state of research, there is no concept for integrating PM into LCA. Therefore, the present work focuses on the investigation of interfaces between PM and LCA in order to systematically identify and evaluate the potentials of using PM. The results are applied to a use case in the sector of commercial vehicles.

Lessons in Failure: Applying an Organizational Learning Framework to Understanding Attitudes Towards Failure in Development.

This paper applies an organizational learning framework to explore attitudes towards failure in the water, sanitation and hygiene (WASH) development sector. It draws on 35 key-informant interviews, contextualized by organizational theory and existing scholarship on failure in development, to understand the challenges faced by WASH practitioners in identifying failure, analyzing failure, and deliberate experimentation. Through interrogating past and present initiatives for publicizing failure in development, this paper digs deeper into the successes, obstacles, and lessons learnt from mainstreaming failure into organizational practices. It then synthesizes these findings to advance a 3-tier conceptual map for organizations to build an enabling environment for learning from failure in development.

The Contributors Index to the 3rd edition of “The Russian Language” Encyclopedia (Moscow, 2020)

In early 2020, there was published the book, Moldovan, A.M. (gen. ed.). The Russian Language: An Encyclopedia. 3rd ed. Moscow, AST, 2020. 902 pp. (In Rus.), which contained no indices. The author of the present article comes up with the following contributors index to this volume. The index might be helpful to those readers of the encyclopedic edition who look for the articles written specifically by certain specialists in the field. The index here fulfills its usual function and serves as an organized map of the book’s content.