Journal of Information Systems Research and Practice

Preface

Tutut Herawan — 2024-05-14

The Volume 6 Issue 2 of JISRP has been available online since 14th May 2024 for the regular issue of June 2024. The issue has been available online for accepted papers (uncorrected proofs).

Big Data Analytics and Public Health Strategies in Coping Covid-19 Outbreak

Lucia Ika Fitriastuti — 2024-05-14

The use of big data analytics plays an important role in determining the public health strategies taken by a country in relation to the Covid-19 pandemic which is currently ongoing worldwide. The objective of this article is to present both conceptual analysis and analysis of the conditions of the Covid-19 pandemic that occurs worldwide to design a framework which shows the relationship between the use of big data analytics and public health strategies to cope with the Covid-19 pandemic. In fact, the public health strategy taken by a country is inseparable from both the public policies strictly taken by its government and the local culture. This way, this paper also discusses the variables of government policies and culture as moderating variables. This article uses the conceptual analysis method and literature review to develop a conceptual model which is followed by proposing three related propositions. In addition, this paper also presents a number of research opportunities as well as the possible research objects, the possible additional variables, and data analysis. This article has implications to assist and provide direction for future research to further examine other interesting topics related to the use of big data analytics in coping with the Covid-19 outbreak. Besides, it also has implications for stakeholders in making decisions and policies to use important variable, i.e. big data analytics, by taking into account government regulations and local culture in dealing with the Covid-19 outbreak.

Utilizing The Naive Bayes Method for Heart Disorder Classification Based on Body Weight Factors

Mardi Turnip — 2024-05-14

The main cause of death which is ranked number one in Indonesia is heart disease which is usually triggered by cholesterol, diabetes and high blood pressure. Heart disorders are a significant public health concern, and understanding their correlation with body weight can aid in early diagnosis and intervention. This study presents an analysis of heart disorder classification by leveraging the Naïve Bayes method, focusing on body weight factors as key indicators. The Naïve Bayes method, a probabilistic classification technique, is applied to assess the relationship between body weight factors and the presence of heart disorders. The classification model achieved an accuracy rate of more than 95%, highlighting its potential as a valuable tool for healthcare professionals in identifying individuals at risk of heart disorders. This high level of accuracy provides a strong foundation for future research and clinical applications in the field of cardiovascular health.

Laplacian Metropolis Hastings Gradient Torgerson Scaling and Optimized Vgg16 Based Mammogram Classification

T. Leena Prema Kumari — 2024-05-14

Breast cancer (BC) is proliferating to a greater extent day to day. Early discovery can help the patient in saving life. Mammography is commonly utilized in diagnosing BC. Mammography classification is the most critical stage as it assists medical professionals in identifying BC. In this paper, a hybrid method accomplishes accurate and precise classification in a timely manner is proposed and tested. In this work a method called, Laplacian Metropolis Hastings Gradient Torgerson Scaling and Optimized (LMHG-TSO) VGG16-based mammogram classification is proposed. The LMHG-TSO VGG16-based mammogram classification method is split into four sections, namely, preprocessing, segmentation, feature selection and finally classification. First, with the raw mammogram images obtained as input is subjected to Laplacian Neighborhood Gabor Filter-based preprocessing for acquiring computationally efficient preprocessed mammogram images. Second watershed segmentation model called Metropolis Hastings Gradient Region is applied to the preprocessed images to obtain precise segmentation results. Next, to minimize dimensionality and reduce loss Torgerson Scaling Sequential Feature Selection model is applied to the segmented preprocessed mammogram images. Finally, Affine Linear Optimized VGG16-based Mammogram Classification model is applied to make distinct classification between three types (i.e., normal, benign and malignant) in an accurate manner. Experimental evaluation is carried out on factors such as precision, recall, training time and classification accuracy for different mammogram images. The reported results prove the efficiency of the suggested method against prevailing state-of-the-art methods.

Applications of Information System Success Model and Task-Technology-Fit Theories: A Review

Hend Ali Musbah Jebril — 2024-05-14

The Information System Success Model (ISSM) and Task-Technology Fit (TTF) theories offer a means of evaluating the efficiency of technology in a system by analyzing the correlation between the technology and the tasks it is intended to facilitate. The two theories are generally acknowledged and have been implemented in several ways, although there has been less effort to consolidate and integrate the utilization of ISSM and TTF in literature. The objectives of this study are (1) to find and analyze pertinent literature that utilizes the TTF and ISSM models in the context of higher education, (2) to identify which of these theories are the most commonly used models, and (3) to evaluate the studies that combine TTF and ISSM in the context of digital libraries. The findings revealed that the applied studies are primarily aimed to develop theories or evaluate specific real-world phenomena. These studies were conducted in various settings, with a majority of them being in public universities. Additionally, the combination of these two theories is seldom in the context of digital libraries. The outcomes of this study lead to a better knowledge of TTF and ISSM applications, and help in the process of formulating future research to further analyze TTF and ISSM investigations.

New Technologies-based Defense for Web Application Vulnerabilities: A Survey

Muath AlShaikh — 2024-05-27

Many of the most dangerous attacks on the web come as a result of the exploitation of vulnerabilities that exist on the web. Fundamentally, malicious actors’ purpose is to identify and engage vulnerabilities inherent in web applications that accept and execute code without necessarily determining its origin or safety. Despite the numerous solutions and coordinated efforts that researchers, governments, and organizations have put towards addressing web application threats, security issues contextual to the threat that these attacks pose remain the subject of survey in many security bulletins at national and global forums. This survey examines how state-of-the-art defense systems have evolved and how web application vulnerabilities (WAV) are identified and mitigated. Moreover, this study also delves into the evolution of defense systems against WAV, by tracing their development from rudimentary measures to sophisticated strategies that leverage the latest in Blockchain, Quantum Computing, and Artificial Intelligence technologies. The characteristics and challenges concerning the use of these three technologies were discussed and then appropriate solutions to these various challenges were proposed. Despite these advancements, we proved that no single solution offers a panacea for web application security. We show how the dynamic and ever-evolving nature of cyber threats necessitates a multi-layered and adaptive approach. The presented survey underscores the importance of continuous innovation and collaboration in the quest to protect web applications from vulnerabilities and ensure the security of our digital lives.