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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 653 Documents
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Comparison of Naïve Bayes and Logistic Regression in Sentiment Analysis on Marketplace Reviews Using Rating-Based Labeling Satya Abdul Halim Bahtiar; Chandra Kusuma Dewa; Ahmad Luthfi
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.539

Abstract

This research focuses on sentiment analysis in the marketplace reviews in Google Play Store, a platform for downloading Android applications and providing reviews. Sentiment analysis is essential for understanding user responses to applications, particularly in the app marketplace. In this study, two machine learning algorithms, Naïve Bayes and Logistic Regression, are employed to classify user reviews. The application rating is used as a reference to determine the sentiment of each comment. The dataset is divided into two conditions: using 2 labels (positive & negative) and 3 labels (positive, neutral, & negative). The test results indicate that the highest performance is achieved by classifying with Logistic Regression on the Shopee dataset with 2 labels. The accuracy reaches 84.58%, precision reaches 84.66%, and recall reaches 84.63%. Additionally, the fastest processing time occurs when testing the Lazada 2-label dataset with Naïve Bayes, taking only 0.038 seconds. Overall, the research suggests that datasets with 2 labels tend to yield higher accuracy compared to datasets with 3 labels.
Black Box Testing of Futsal Field Rental Information Systems Using Automated Testing Method Fauzan Asrin
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.540

Abstract

A pivotal aspect of software development is testing, which serves as the final phase preceding the release of a software or information system. The realm of software testing encompasses a diverse array of methods, and within the scope of this investigation, the focus is on black box testing. In pursuit of this objective, the study leverages the capabilities of the Katalon Studio for Automated Testing. Its application is directed towards evaluating the functionality of an information system dedicated to the rental of futsal fields in the city of Singkawang. The essence of this examination lies in affirming the integrity of each feature and menu that constitutes the futsal field rental information system. This validation process is integral to ensuring that the system aligns seamlessly with the requisites of its users. The culmination of these testing endeavors culminates in the confirmation that the information system harmoniously resonates with user expectations. Consequently, it stands primed for implementation and subsequent release, signifying the attainment of a pivotal milestone in its development journey.
Exploring Customer Relationship Management: Trends, Challenges, and Innovations Erick Fernando; Rudi Sutomo; Yulius Denny Prabowo; Jullend Gatc; Winanti Winanti
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.541

Abstract

This study presents a comprehensive exploration of recent advancements in Customer Relationship Management (CRM), acknowledging its pivotal role in fostering crucial connections both within the industry and with customers at large. The study delves deeply into CRM, aiming to enhance overall customer satisfaction. The primary focus of this study centers around critical facets of CRM, encompassing strategies for managing customer relationships, applications of information technology, analysis of customer data, and approaches for customer retention. Employing a literature review methodology, this research rigorously examines the most recent journals germane to the field of CRM. A total of 46 articles sourced from reputable journal databases constitute the foundation of this investigation. The findings of this study yield an enriched comprehension of contemporary developments concerning challenges, factors driving success, relevant domains, and implementation goals within the realm of Customer Relationship Management.
Developing Web-Based Point of Sales Application with SHA-512 Encryption on DBMS for Indonesian MSME’s Culinary Industry Regant Fernando; Jansen Wiratama
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.544

Abstract

Efficiency and effectiveness are crucial in the food & beverage industry. Conventional methods employed by companies often lead to discrepancies between actual stock and the stock available for sale, resulting in significant losses. To address this issue, a point-of-sales-based system has been implemented, enabling companies to monitor transaction activities seamlessly. The research utilized the RAD (Rapid Application Development) method to develop a concise and fast software application. Furthermore, hashing and encryption methods have been incorporated to enhance database security, utilizing the SHA-512 algorithm for hashing and data encryption. This research has yielded a point-of-sales website-based application that supports various business processes. The website has been tailored to meet the specific requirements outlined by the company owner. The UAT test results have demonstrated that the application encompasses
Leveraging COBIT 2019 to Implement IT Governance in Mineral Mining Company Ray Farhan Mubarak; Melissa Indah Fianty
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.545

Abstract

The integration of Information Technology (IT) in companies comes with its own set of challenges. One major issue is the loss of operational data, which directly affects the company's workflow and disrupts its activities. To tackle this problem, the company assesses its IT capability using the COBIT 2019 framework. This framework outlines the objectives of different IT processes and assigns predetermined values to each process. The assessment reveals three levels of IT capability for specific processes within the company: EDM03 (Ensured Risk Optimization) at level 3, APO13 (Managed Security) at level 2, and MEA03 (Managed Compliance with External Requirements) at level 3. The company aims to reach level 4 for these processes. Consequently, recommendations for improvement primarily focus on enhancing the management of information security risks and information security management systems, as well as increasing compliance with policies and regulations.
Securing Against Zero-Day Attacks: A Machine Learning Approach for Classification and Organizations’ Perception of its Impact Anietie P. Ekong; Aniebiet Etuk; Saviour Inyang; Mary Ekere-obong
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.546

Abstract

Zero-day malware is a type of malware that exploits system vulnerabilities before it is detected and sealed. This type of malware is a significant threat to enterprise cybersecurity and has tremendous impact on organizations’ performance, as it can spread widely before organizations can clamp down on the threat. Unfortunately, exploit developers can attack system’s vulnerabilities at a pace that is faster than defensive patches. In this research, classification of zero-day attack was carried out. Exploratory Data Analysis (EDA) on malware zero data was conducted. Then feature selection was carried out using Principal Component Analysis (PCA) for the selection of the most important features in the dataset after which a Random Forest (RF) Algorithm was adopted for the classification of zero-day attack. The impact of such attacks was also analyzed, and results were evaluated using confusion matrix and an accuracy of 95% in the classification of zero-day attack with a class error of 3.8% was obtained. A survey of the perception of the potential impacts of these attacks on organization was also carried out. These results indicate efficiency of machine learning algorithm in the classification of attacks as zero-day malware attacks or not. The research also offered pragmatic insights into the perception by organizations of its potential negative impacts and their eagerness to embrace and prioritize proffered cyber security solution(s) to avoid such attacks in order to avert undesirable consequences.
Fake News Detection Using Optimized CNN and LSTM Techniques Emmy Danny Ajik; Georgina N Obunadike; Faith O Echobu
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.548

Abstract

Concerns have been raised about the social consequences of fake news as it has spread rapidly on online platforms. It is critical to detect and mitigate the spread of fake news in order to maintain a healthy community conversation. There is a need to put more effort into the identification of fake news as more people use the internet, especially as more internet-enabled gadgets become more widely available and inexpensive. With the help of two Neural Network techniques: long-short-term memory (LSTM) and Convolutional Neural Network (CNN). This research proposes novel deep-learning methods for identifying fake news using two datasets. These methods were considered for this research because they had proven to be successful in earlier studies that had been looked at. Finding the best-performing optimal models is the goal of this study. HyperOpt Technique was used for Neural Network model. The performance of the optimized models was compared with the performance of the models without optimization. The results obtained showed that for both datasets, CNN and LSTM performed better when training the models with the optimal values with an average difference of 12.7% for Accuracy, 11.9% for Precision, 12.3% for Recall and 15.4% for F1-Score.
Bibliometric Analysis of Deep Learning for Social Media Hate Speech Detection Raymond Tapiwa Mutanga; Oludayo Olugbara; Nalindren Naicker
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.549

Abstract

Social media has become an important web technology for creating and sharing information plus enhancing business reputations worldwide. However, the anonymity accorded by social media platforms has been cryptically vituperated to spread horrendous content such as hate speech. Recently, researchers have been progressively gravitating towards the use of deep learning techniques to address the problem of social media hate speech detection. This study provides bibliometric analysis and mapping of the existing literature on hate speech detection using deep learning algorithms. The study used articles published between 2016 and 2022 from the Scopus database, while Vos Viewer, Biblioshiny, and Panda’s software tools were employed for the bibliometric analysis. The research explored the yearly trajectory of recent publications, dominant countries, collaborative institutions, sources of primary studies that have employed deep learning for hate speech detection, and the intellectual and social structures of the research constituents. It has been observed that the literature on hate speech detection is rapidly growing, but research output and collaborations from the developing countries of the world are still limited. The findings of this study provide insights into the intellectual structure and advancements in deep learning applications for hate speech detection while identifying research gaps for future work.
E-consultation Acceptance in Ghana: A Quantitative Analysis and Proposed Model for Enhancing Digital Health Mark Ofori Nketia; Manoj Maharaj
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.553

Abstract

This study investigates the adoption and enhancement of digital health through e-consultation in Ghana's healthcare systems. It examines the challenges hindering the successful implementation of e-consultation, including security, privacy and awareness. By conducting a quantitative analysis and proposing a comprehensive model, this study aims to provide insights into overcoming these barriers, to improve healthcare accessibility and reduce disparities through e-consultation. The research integrates the DeLone and McLean IS Success Model, focusing on quality, use, user satisfaction, and net benefits, alongside the Diffusion of Innovation theory of technology adoption. The study employs a mixed-methods approach, combining literature review and quantitative analysis. Clinicians and patients received distinct questionnaires, covering knowledge, skills, performance, safety, and e-consultation challenges. Univariate statistics provides an initial data overview, followed by factor analysis. With a Kaiser-Meyer-Olkin value of 0.710 and Bartlett's test of sphericity yielding 1300.894, the data proves suitable for factor analysis. Using Principal Axis Factoring, the study reveals significant correlations among factors: Attitude, Regulatory Framework, Diffusion, and Acceptance. Based on these findings, an E-consultation Framework is proposed, emphasizing robust E-consultation Technology Frameworks. By adopting e-consultation and advanced digital health integration, Ghana can advance healthcare accessibility, diminish disparities, and enhance citizens' well-being within the digital health domain.
Factors Affecting Career Preferences and Pathways: Insights from IT Students Murimo Bethel Mutanga; Philip Xolisa Piyose; Sithembile Ndovela
Journal of Information System and Informatics Vol 5 No 3 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i3.556

Abstract

The selection of an appropriate career path plays a pivotal role in shaping students' futures and can greatly enhance their employability prospects. By making well-informed decisions, students can align their educational pursuits with their professional aspirations, ensuring a seamless transition from academia to the workforce. This study aims to investigate the career choices of IT students within the Department of ICT. Through an analysis of the career choices made by students in these specific areas, the research seeks to uncover the underlying factors that influence their decision-making processes. By examining the career preferences of IT students, this research endeavor seeks to provide valuable insights into the motivations, interests, and aspirations that drive their choice of specialization. Additionally, it aims to explore how external factors, such as industry trends, job market demands, and personal experiences, impact their decision-making. Understanding these factors can offer valuable guidance to educational institutions, curriculum developers, and career advisors, enabling them to provide tailored support and guidance to IT students, thus facilitating more informed career decisions. Ultimately, the findings of this study will contribute to a better understanding of the career preferences and pathways within the IT field. This knowledge can aid educators and institutions in developing relevant and responsive curricula, fostering an environment that nurtures students' talents and equips them with the necessary skills and knowledge for their chosen career paths. Additionally, it can assist students in making informed choices that align with their passions and long-term goals, ultimately enhancing their employability and success in the dynamic and ever-evolving IT industry.

Page 26 of 66 | Total Record : 653

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