Pemetaan Secara Sistematis Pada Metrik Kualitas Perangkat Lunak

. Software quality assurance is one method to increase quality of software. Improvement of software quality can be measured with software quality metric. Software quality metrics are part of software quality measurement model. Currently software quality models have a very diverse types, so that software quality metrics become increasingly diverse. The various types of metrics to measure the quality of software create proper metrics selection issues to fit the desired quality measurement parameters. Another problem is the validation need to be performed on these metrics in order to obtain objective and valid results. In this paper, a systematic mapping of the software quality metric is conducted in the last nine years. This paper brings up issues in software quality metrics that can be used by other researchers. Furthermore, current trends are introduced and discussed

Beyond traditional manuals: A comparative analysis of instruction manual effectiveness in handheld consumer products

Modern consumer products often come with instruction manuals that play a crucial role in guiding users through their complexities. However, a widespread lack of enthusiasm among consumers to engage with these manuals is rooted in negative experiences. This research evaluated the usability and effectiveness of traditional paper-based instruction manuals versus the redesigned version, using the HS210 drone as a case study. Employing a dual-phase methodology, this study first conducted a thorough analysis of the HS210 drone's existing paper manual, identifying its strengths, weaknesses, and user perceptions. The subsequent phase entailed the creation and evaluation of an improved manual informed by initial findings. This comparative analysis sought to measure user performance, experience, and preference regarding both the original and redesigned instruction manuals, aiming to enhance engagement and effectiveness in drone operational guides. Data collection utilized a comprehensive methodological approach, incorporating contextual inquiry, think-aloud protocols during usability tests, a pre-test survey, and post-test semi-structured interviews. The findings revealed a pronounced preference for multimedia instruction manuals, showcasing higher levels of user satisfaction, better engagement with the manuals, and significantly enhanced usability. This research underscored the potential of innovative instructional design in improving user interaction with complex consumer products. This research holds substantial potential to advance our understanding of instructional materials' effectiveness in the context of technological products. As technology becomes increasingly integrated into everyday life, the ability to effectively use and understand technological products is crucial. This study will contribute to this understanding by identifying the most effective ways to guide users in navigating new technologies, thereby reducing the frustration and time often associated with learning new devices. Moreover, the findings from this research could significantly benefit businesses and educators by providing evidence-based recommendations for instructional material design. For businesses, this means creating more user-friendly manuals that could lead to increased customer satisfaction and reduced the need for customer support

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI