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Journal : International Journal Of Computer, Network Security and Information System (IJCONSIST)

A Structured Approach to Organizational Website Development and Usability Measurement Using the Modified System Usability Scale Vinza Hedi, Satria; Hedi Amelia Bella, Cintya
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.156

Abstract

The increasing demand for accessible and up-to-date digital identity has encouraged organizations to adopt websites as a primary information platform. However, limited technical resources often hinder effective content management. This study proposes a structured approach to developing an organizational website using the Waterfall model and evaluates its usability through the System Usability Scale (SUS). The development process consists of five stages: communication, planning, modelling, construction, and deployment. Requirements were collected through semi-structured interviews with organizational stakeholders, resulting in the decision to implement a web-based system using a Content Management System (CMS) to ensure ease of maintenance. After deployment, usability testing was conducted using Modified SUS with a Likert scale of 1–5, involving five respondents instead. The evaluation produced an average score of 4.6, indicating that the system is highly acceptable and easy to use, although suggestions were made to improve dashboard terminology and add a search feature. The results demonstrate that a structured web development approach combined with CMS integration can effectively empower non-technical users in managing digital content. Future development may include interface personalization and multi-admin features to further enhance usability.
Sentiment Analysis of User Reviews for the LinkedIn Application Using Support Vector Machine and Naïve Bayes Algorithm Ulinnuha, Nurissaidah; Pertiwi, Aisyah; Basuki, Athiyah Fitriyani; Kristanti, Beni Tiyas; Haniefardy, Addien; Burhanudin, Muhamad Aris; Satria, Vinza Hedi
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.159

Abstract

Social Networking Sites (SNS) have become integral communication platforms for knowledge sharing and professional connections. LinkedIn, a leading professional network, is widely utilized in today's digital era, primarily by professionals and the business community. This research focuses on analyzing user sentiment on LinkedIn through the application of the Support Vector Machine (SVM) and Naive Bayes methods. Understanding user opinions and satisfaction is important, and sentiment analysis serves as a key tool for this purpose. This study is a comparative analysis of Support Vector Machine (SVM) and Naïve Bayes algorithm for classifying user reviews of the LinkedIn application. Drawing on data from Google Play reviews, this research explores a range of user sentiment towards the LinkedIn platform, including positive, negative and neutral reviews. The application of SVM and Naive Bayes algorithms successfully classifies reviews into relevant sentiment categories. Analyzing 2000 review datasets with an 80% training and 20% testing data split, Support Vector Machines demonstrate an 80% accuracy rate, while Naïve Bayes achieves a 70% accuracy rate. The Support Vector Machines (SVM) algorithm has better accuracy than the Naïve Bayes algorithm based on the test scenarios that have been carried out.