Journal of Information Systems and Informatics (Journal-ISI)
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Efficient Thesis Management: A Study of Universitas Multimedia Nusantara's Application Development Using Extreme Programming Principles
Get PDFThe objective of this study is to develop and construct a thesis application utilizing the extreme programming approach, and to assess user contentment with the application using the End User Computing Satisfaction measurement technique. The Informatics study program at the Multimedia Nusantara University campus is encountering issues pertaining to the thesis procedure. The problems were identified through interviews with numerous lecturers, students, and the head of the Informatics department's study program at Universitas Multimedia Nusantara. The challenges include the decentralized distribution of information pertaining to theses, obstacles in obtaining thesis proposals, difficulties in obtaining details regarding the research specializations of lecturers, recapitulation of supervisors, and an array of additional issues. Based on these problems, a thesis application was designed and built using the extreme programming development method. The research findings indicate that the application has been effectively developed. The test results reveal that 87.267% of users strongly agreed that the application was highly beneficial in the thesis process
Analyzing an Interest in GPT 4o through Sentiment Analysis using CRISP-DM
Get PDFThis study investigates the sentiment of viewers towards GPT-4o technology videos by analyzing 1538 English language posts using two sentiment analysis tools, VADER and TextBlob. The analysis reveals a fair level of agreement between the two tools, with 929 posts (60.40%) classified consistently, yielding a Cohen’s kappa statistic of 0.388. The sentiment distribution among the posts is as follows: 182 posts (19.59%) exhibit negative sentiments, 390 posts (41.98%) are neutral, and 357 posts (38.43%) show positive sentiments. These findings highlight the importance of utilizing multiple tools for comprehensive sentiment analysis and underscore the complexity of interpreting public reactions to AI advancements. The study provides valuable insights into the nuanced responses of viewers, emphasizing the diverse perspectives towards the GPT-4o technology
A Statistical Analysis of System Usability Scale (SUS) Evaluations in Online Learning Platform
Get PDFThis study evaluates the usability of a Moodle-based online learning system at a university using the System Usability Scale (SUS). Introduced in 2019, the platform has been instrumental in facilitating access to educational resources, enhancing the distribution of course materials, and supporting academic activities through interactive features. The SUS was employed to gather subjective feedback from 120 student respondents, providing insights into the system's efficiency, effectiveness, and user satisfaction. Statistical analysis was performed using methods including the Shapiro-Wilk test to address the skewed distribution of the original dataset with a mean SUS score of 71.52 and a high standard deviation of 16.18, indicating varied user experiences. Analysis of sample means was also conducted to achieve a more stable estimate of usability, resulting in a mean of 71.51 with a significantly reduced standard deviation of 2.44, suggesting a more consistent assessment across users. The final SUS score placed the system in the 'C+' category according to the Sauro and Lewis grading scale, indicating that while usability is acceptable, substantial improvements are necessary
Information Technology Governance Design in Trading Companies Using the COBIT 2019 Framework
Get PDFEffective information technology (IT) governance is very important for trading companies in managing their information assets well. The COBIT 2019 has been recognized as an international standard for IT governance that helps organizations achieve their strategic goals through the implementation of structured practices and processes. This research aims to design an IT governance framework based on COBIT 2019, especially for trading companies. The research methodology uses a case study approach by analyzing the unique needs and characteristics of trading companies in the IT context. The result is a design that is tailored to the needs and challenges faced by trading companies, including aspects of information security which are crucial in today's digital era. This research contributes to IT practitioners and senior managers to understand the practical and effective implementation of COBIT 2019 in improving IT governance in trading companies. By using COBIT 2019, trading companies can optimize the management of their information assets while ensuring regulatory compliance and increasing stakeholder trust
Optimizing Motorcycle Sales: Enhancing Customer Segmentation with K-Means Clustering and Data Mining Techniques
Get PDFInformation plays a crucial role in the sustainability of company operations. The development of information technology, especially in the industry 4.0 era, affects various fields including economics, social, and education. The company faces challenges in declining motorcycle sales due to intense competition and ineffective customer segmentation. To address these issues, this study proposes the use of the K-Means algorithm with Python tools for better customer segmentation. The study aims to identify diverse customer groups and tailor marketing strategies accordingly. By utilizing the Elbow method and Silhouette score, the analysis of customer data is simplified. This study also employs data mining techniques to uncover hidden patterns in motorcycle sales data, aiding companies in improving operational efficiency and decision-making
Development of Virtual Reality Application for Arachnophobia Using Multimedia Development Life Cycle Method
Get PDFArachnophobia or known as irrational fear of spiders, can be detrimental to one’s health and overall well-being. Common procedures, such as Cognitive Behavioral Therapy or medication, are often inefficient and take a lot of time to demonstrate results, thus the alternative way of dealing with arachnophobia is urgently needed. Such alternatives can be achieved by utilizing Virtual reality technology, hence the purpose of the following research is to address the matter by developing Virtual reality based application with the help of Multimedia development life cycle method. The MDLC method was chosen due its ability to create multimedia application, and the collected results of the experiment demonstrate that indeed MDLC can be used as a method to develop the arachnophobia therapy application that is both time efficient and works as test shows the decreasing time used for use application as well as two respondents that able to be detected for having arachnophobia. In conclusion the application developed using MDLC is indeed able to be an alternative way of arachnophobia therapy
Digital Transformation Maturity Analysis of Indonesian Navy Staff and Command College Using DTMM
Get PDFThe maturity of digital transformation refers to an organization’s development level in adopting and integrating digital technology into its business processes. The level of maturity of digital transformation in the Indonesian Navy Staff and Command College as a military education institution is very essential. The problem is the information on digital transformation maturity has not been obtained. The qualitative method through in-depth interviews was conducted on the leadership elements, namely the director and departmental heads to answer the question of digital transformation maturity. Interviews were developed in the policy and strategy, technology and infrastructure dimensions, the use of IT in the business process of educational institutions, IT-based learning, lecturers and education, data, digital leadership, efficiency and IT-based performance, and IT culture. Determination of maturity level was carried out using DTMM at initial, developing, defined, managed, and optimized. The results of the study show that overall, the level of maturity of digital transformation at the Naval Staff and Command School has reached a defined (systematic) level. The strategy for increasing the maturity of digital transformation at the Naval Staff and Command School needs to be continuously developed and implemented in each dimension
Machine Learning Algorithms to Defend Against Routing Attacks on the Internet of Things: A Systematic Literature Review
Get PDFThe Internet of Things (IoT) has become increasingly popular, opening vast application possibilities in different fields including smart cities, healthcare, manufacturing, agriculture, etc. IoT comprises resource-constrained devices deployed in Low Power and Lossy Networks (LLNs). To satisfy the routing requirements of these networks, the Internet Engineering Task Force (IETF) created a standardised Routing Protocol for low-power and Lossy Networks (RPL). However, this routing protocol is vulnerable to routing attacks, prompting researchers to propose several techniques to defend the network against such attacks. Machine learning approaches demonstrate effective ways to detect such attacks in large quantities. Therefore, this paper systematically synthesised 17 publications to compare the performance of traditional and advanced machine learning algorithms to identify the best algorithm for detecting RPL-based IoT routing attacks. The findings of this paper show that machine learning algorithms are capable of effective detection of many routing attacks with high accuracy and a low False Positive Rate. Furthermore, the results demonstrate that on average, advanced machine learning algorithms can achieve an accuracy of 96.03% compared to traditional machine learning algorithms which achieved 91.67%. Traditional machine learning algorithms demonstrated the best performance on average False Positive Rate by achieving 2.75% compared to their counterparts which gained 4.79%. However, Random Forest showed the best performance and outperformed all the algorithms in the selected publications by achieving over 99% accuracy, precision and recall
IoT-Based Smart Door Lock System with Fingerprint and Keypad Access
Get PDFDoors are important components in a home, serving as entry points, room dividers, and security barriers. Door locks have evolved from manual mechanisms to automatic systems using technologies such as passwords, face sensors, and fingerprint sensors. To enhance practicality and efficiency, an Internet of Things (IoT)-based Smart Door Lock system using keypads and fingerprint sensors was developed in this research. The system was built using the Waterfall model Software Development Life Cycle (SDLC) and utilizes Firebase for real-time data communication and control through an Android application. Black box testing was conducted to verify the system’s functionality, achieving a 100% success rate across 20 trials. The system offers enhanced security and remote access control, with potential applications in both residential and commercial settings
Enhancing Automated Vehicle License Plate Recognition with YOLOv8 and EasyOCR
Get PDFThis research focuses on the development of an automatic system for vehicle license plate recognition using YOLOv8, EasyOCR, and CNN methods for object classification. The main issue raised is the need for an accurate and efficient system for recognizing vehicle license plates in real-time in dynamic environments, especially in urban areas with high traffic levels. The method used in this study involves resizing the input image to 416x416 pixels to standardize the data, analyzing the YOLO architecture that divides the image into a 7x7 grid, and using the Convolutional Neural Network (CNN) algorithm for feature extraction and object classification. Object detection uses the YOLOv8 method which is tasked with recognizing license plates using a previously trained YOLO (pretrained model) model then implemented and tested using video with 4k quality to ensure its effectiveness in detecting vehicle license plate objects, followed by the Optical Character Recognition (OCR) process with the EasyOCR method to read text on license plates and tested to ensure its effectiveness in reading characters on license plates vehicle number. The purpose of this research is to develop a system that can improve accuracy and efficiency in vehicle license plate recognition. The results show that the accuracy, precision, recall and F1-Score for object detection reach 100% and the average percentage of detected text conformity is 74.66%, which shows that this system is reliable in real applications and contributes to the development of automatic license plate recognition technology