Recommendation Model for Learning Material Using the Felder Silverman Learning Style Approach

The biggest obstacle that students have when participating in a virtual learning environment (e-learning) is discovering a platform that has functionalities that can be customized to fit their needs. This is usually accomplished in several ways using educational resources such as learning materials and virtual classroom design elements. Our research has tried to meet this demand by suggesting an extra element in the virtual classroom design, i.e., classifying the students’ learning styles through machine-learning techniques based on information gathered from questionnaires. This feature allows teachers or instructors to modify their lesson plans to better suit the learning preferences of their students. Additionally, this feature aids in the creation of a learning path that serves as a guide for students as they choose their course materials. In this study, we have selected the Felder-Silverman Learning Style Model (FSLSM) in the questionnaire design, which focuses on identifying the students' learning styles. After that, we employ several machine learning algorithms to create a prediction model for the students’ learning styles. The algorithms include Decision Tree, Support Vector Machines, K-Nearest Neighbors, Naïve Bayes, Linear Discriminant Analysis, Random Forest, and Logistic Regression. The best prediction model from this exercise contributes to the recommendation model that was created using a collaborative filtering algorithm. We have carried out a pre-test and post-test method to evaluate our suggestions. There were 138 learners who were following a learning path and participated in this study. The findings of the pretest and post-test indicated a notable increase in students' motivation to study. This is confirmed by the fact that learners' satisfaction with online learning climbed to 87% when the learning style was considered, from 60% when it wasn't. Doi: 10.28991/HIJ-2023-04-04-010 Full Text: PD

Enterprise Architecture: Enabling Digital Transformation for Operational Business Process during COVID-19

The SARS-CoV-2 pandemic and the global response to contain its spread and deaths have been unprecedented, according to UNICEF research on COVID-19 released in 2021. Many steps had been taken by countries worldwide, particularly those in South Asia. As of May 17th, 2020, Indonesia reported a total of 17,514 daily positive cases. It has been confirmed that the majority of cases throughout the archipelago occur primarily on Java, particularly in the Greater Jakarta, Greater Bandung, Semarang, Solo, and Greater Surabaya areas. The research object of this paper is a system integrator company located in, Central Jakarta. The company's business is badly impacted by this pandemic. The company provides nearly all ICT solutions, yet improving their internal systems is an issue that has never been brought up. Due to physical distance regulations, leading workers to work from home. To keep the business running, the company began using email as their only tool to run the whole system, which is not effective and causing a crisis for the company. The purpose of this paper is to propose a digital transformation plan as a solution and to support business continuity by utilizing TOGAF ADM. Doi: 10.28991/HIJ-2023-04-01-01 Full Text: PD

Design of 360° Dead-Angle-Free Smart Desk Lamp based on Visual Tracking

Objectives: This study aims to design a dead-angle-free smart desk lamp. Methods: The convolutional neural network (CNN) algorithm was used to realize the identification and positioning of objects. Then, the desk lamp arm was driven according to positioning to realize dead-angle-free illumination. In the subsequent testing, the designed desk lamp was compared with others driven by the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms. Findings: The CNN algorithm implemented in the smart desk lamp demonstrated superior target recognition performance and positioning accuracy when compared to the other two algorithms. Moreover, with this algorithm, the smart desk lamp efficiently generated tracking responses for targets and displayed minimal positioning errors once tracking became stable. Novelty:The novelty of this article lies in the utilization of the CNN algorithm to achieve visual tracking for a smart desk lamp, which serves as the basis for its automatic adjustment. Doi: 10.28991/HIJ-2023-04-04-05 Full Text: PD

System Architecture for IT Talent Ecosystem Using Service Oriented Approach

The purpose of this research is to propose a System Architecture to facilitate the IT Talent ecosystem using a service-oriented approach. The need for this is important to support digital transformation in the IT Talent ecosystem. Human resources in the IT field are one of the key factors in implementing IT in organizations. However, the availability of IT human resources has not been able to meet the needs and challenges of the organization in synergizing IT and business. Meanwhile, on the other hand, the qualifications of IT human resources do not meet the existing competency standards. In this research, we use a service-oriented system development method. It consists of three stages, such as (1). Analysis and Observation, (2). Analysis from an in-depth interview, and (3) System Architecture Design, which includes Analysis Features of the Systems, Service Analysis and Identification, Specification of Architecture, and Layering. The novelty and findings of this research are a system architecture, which is called a middleware architecture, that can bridge entities in the IT Talent ecosystem to provide and use services to each other for support collaboration. In this study, we proposed a system architecture that acts as middleware to support collaboration and integration in the IT Talent ecosystem. We proposed TALENT-IT, which acts as a service bus mechanism. We used a service-oriented approach to develop this platform. The results of this study are: list of features, list of services, SOA layer, SOA architecture, and monetization feasibility and challenges. Doi: 10.28991/HIJ-2023-04-04-03 Full Text: PD

Innovative Strategy for Selecting Industries for Program-Target Stimulation of Regional Economic Diversification

The purpose of this study is to substantiate the approach to the selection of industries for program-target stimulation of regional economy diversification, focusing on developing new strong industries and increasing the economic complexity of the regional economy. The research methodology is based on the application of the concept of revealed comparative advantages and an assessment of the economic complexity of industries and regions of Russia (the Udmurt Republic, Republic of Mordovia, Kaliningrad Region, and Trans-Baikal Territory) using data on tax revenues by economic sectors. The novelty of this research lies in demonstrating the effectiveness of applying the revealed comparative advantage concept, an approach to assessing economic complexity based on the use of tax revenue data by economic sectors, and a strategy for modernizing intermediate opportunities when selecting industries for program-target stimulation of regional economy diversification. The practical significance of the results is determined by the possibilities of their use in the application of program-target mechanisms to solve problems of stimulating the development of individual sectors of the regional economy. Selecting priority areas for diversification based on economic complexity methods can contribute to the improvement of budget balancing, economic growth and sustainable development, and mitigation of interregional inequality. Doi: 10.28991/HIJ-2023-04-03-013 Full Text: PD

Descriptive Statistical Analysis of the Coach-Player Relationship with CART-Q and SCI

Providing maximum performance in a long-term competitive load is associated with a quality relationship between the player and his coach. A coach plays one of the most important roles in an athlete's sports career and has the potential to positively or negatively impact the mental health of athletes. The aim of the presented paper is to map the bond between the quality of the relationship between the player and the coach and the sports self-confidence of elite junior tennis players. The research sample consisted of 236 elite junior tennis players competing at the national and international levels. The average age was 17.2 years. Data collection was carried out using the questionnaire methods of the Coach-Athlete Relationship Questionnaire (CART-Q) and the Sport Confidence Inventory (SCI). The results found significant differences in the perception of the quality of the coaching relationship between Czech and foreign athletes. Gender differences were also found among Czech athletes. A significant relationship was found between the quality of the player-coach relationship and sports self-confidence. The results point to the connections between performance, mental well-being, and the quality of the relationship between the player and the coach and can be the basis for further studies and motivate coaches to think about whether there is a need to modify the ways of training and dealing with their athletes. Doi: 10.28991/HIJ-2023-04-02-01 Full Text: PD

Development and Algorithmization of a Method for Analyzing the Degree of Uniqueness of Personal Medical Data

The purpose of this investigation is to develop a method for quantitative assessment of the uniqueness of personal medical data (PMD) to improve their protection in medical information systems (MIS). The relevance of the goal is due to the fact that impersonal PMD can form unique combinations that are potentially of interest to intruders and threaten to reveal the patient's identity and medical confidentiality. Existing approaches were analyzed, and a new method for quantifying the degree of uniqueness of PMD was proposed. A weakness in existing approaches is the assumption that an attacker will use exact matching to identify people. The novelty of the method proposed in this paper lies in the fact that it is not limited to this hypothesis, although it has its limitations: it is not applicable to small samples. The developed method for determining the PMD uniqueness coefficient is based on the assumption of a multidimensional distribution of features, characterized by a covariance matrix, and a normal distribution, which provides the most reliable reflection of the existing relationships between features when analyzing large data samples. The results obtained in computational experiments show that efficiency is no worse than that of focus groups of specialized experts. Doi: 10.28991/HIJ-2023-04-01-09 Full Text: PD

An Innovative Mobile Application for Wellness Tourism Destination Competitiveness Assessment: The Research and Development Approach

Objectives: This research developed and evaluated the effectiveness of an innovative mobile application for wellness tourism destination competitiveness and also studied the adoption effectiveness of this application. Methods/Analysis: A mixed-methods research and development approach was applied to construct a wellness tourism destination competitiveness evaluation model for qualitative research using in-depth interviews, followed by quantitative research using a questionnaire. Weighted scores of criteria and indicators for wellness tourism destination competitiveness were evaluated by the DEMATEL method. The cut-off points for classifying the competitiveness level were set by K-means cluster analysis, while the internal and external accuracy of the model were validated by the confusion matrix technique and the Kruskal-Wallis test. The innovative mobile application was developed using a linear waterfall conceptual design consisting of five software development phases: requirement, design, implementation, verification, and maintenance. A questionnaire was also used to assess the adoption and commercialization of the innovative mobile application. Findings: Results showed that 1) the model gave high accuracy with the confusion matrix technique at 85.42% and the Kruskal-Wallis test classified destination competitiveness at a significance level of 0.0001; and 2) the level of adoption of the innovative mobile application was high. Target users were interested in purchasing a license as the commercial mode of the program. Novelty/Improvement:This research provides a tool to assess the overall competitiveness of wellness tourism destinations. Results can be used to support decision-making and provide practical suggestions for wellness tourism cluster users to adapt when conducting their own competitiveness assessment. The competitiveness assessment results were accurate and in line with the research objectives. Doi: 10.28991/HIJ-2023-04-03-010 Full Text: PD

Trainable Regularization in Dense Image Matching Problems

This study examines the development of specialized models designed to solve image-matching problems. The purpose of this research is to develop a technique based on energy tensor aggregation for dense image matching. This task is relevant within the framework of computer systems since image comparison makes it possible to solve current problems such as reconstructing a three-dimensional model of an object, creating a panorama scene, ensuring object recognition, etc. This paper examines in detail the key features of the image matching process based on the use of binocular stereo reconstruction and the features of calculating energies during this process, and establishes the main parts of the proposed method in the form of diagrams and formulas. This research develops a machine learning model that provides solutions to image matching problems for real data using parallel programming tools. A detailed description of the architecture of the convolutional recurrent neural network that underlies this method is given. Appropriate computational experiments were conducted to compare the results obtained with the methods proposed in the scientific literature. The method discussed in this article is characterized by better efficiency, both in terms of the speed of work execution and the number of possible errors. Doi: 10.28991/HIJ-2023-04-03-011 Full Text: PD

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

Big data collection involves enormous amounts of raw data. To boost the sustainability of corporate value and support business intelligence and decision-making systems, in-depth data analysis is necessary. The data storage, analysis, and visualization methods, as well as the discovery of patterns and linkages, all depend on extensive data analysis. This study aims to process datasets to learn things like how ratings impact market sales transactions and how much of an impact factor connected to consumers and items have on ratings. Elasticsearch and Kibana were used for the dataset processing. This study evaluated traits related to the test parameters using a variety of test procedures. The product is scored as a representation of the product types involved in the sales transaction, and the name is assessed as a reflection of the customer. Kibana and Elasticsearch, a full-text search engine, were used in this work to do extensive data analysis on data sets. It is a visualization tool that is employed in a controlled environment to evaluate how ratings impact market exchanges for electronic goods, and it offers suggestions. The study found a substantial relationship between electronic product sales on the Amazon marketplace from 2012 to 2018. It suggested the importance of buyer constituents as users and how different product categories relate to ratings in business transactions. Doi: 10.28991/HIJ-2023-04-03-09 Full Text: PD