Publication Search

This study examines the application of network models in daily life using a systematic literature review (SLR) method. Network models are mathematical approaches that aid in solving various optimization problems such as shortest routes, minimum spanning trees, and maximum flows. Findings reveal that the Floyd-Warshall and Dijkstra algorithms are applied for route navigation, Prim's algorithm for distribution efficiency, and the Edmonds-Karp algorithm for optimizing resource flows like water. This research highlights the significance of network models in enhancing operational efficiency, sustainability, and accessibility across various domains such as transportation, logistics, and resource management. With the integration of modern technology, network models hold significant potential for developing innovative solutions in everyday life.

Education is a learning process aimed at developing individual potential in cognitive, affective, and psychomotor aspects, enabling them to contribute optimally to society. In formal education, such as in schools, various disciplines are taught, including mathematics, which helps students think logically, analytically, and solve problems. Mathematics education is expected to help students understand concepts that can be applied in various fields of science and real-life situations. Students should understand mathematical concepts, recognize relationships between concepts, derive formulas and properties, perform mathematical operations for generalization, gather evidence or explain ideas and mathematical statements, solve problems, use mathematics to communicate ideas, explain situations or problems, and appreciate the importance of mathematics in their lives. Conceptual understanding represents the lowest level of the cognitive domain and is a crucial learning objective as it provides the understanding that the lessons taught are not merely about mathematics. The conceptual understanding of mathematics among the 24 students in class XI at SMK RPI Jakarta resulted in an average score of 76.9. This indicates that the majority of students have a good understanding of the concepts, although some still require improvement..

File systems are key operating system components because they store and retrieve files as needed. Traditional hierarchical file systems no longer suit current users’ expectations in organizing extensive collections of files for easy retrieval based on intrinsic and user-defined properties. The number of files in users’ collections is growing substantially, partly because of the ease with which consumer devices capture information. With the enormous capacity of contemporary storage devices and the rising trend of users storing their data in the cloud, they only fuel the number of files needing to be managed. We suggest that current file systems require an enhanced technique of data organization and access so that users can properly handle these ever-increasing data collections. File systems are key operating system components because they store and retrieve files as needed. This study offers improvements to the conventional Hierarchical File System to improve file organization and retrieval through built-in querying capabilities and support filesystem-level operations that execute significant amounts of metadata updates. This is accomplished using attributes (name-value pairs) in a file collection hierarchy. A series of improvements to the HFS introduce the “AttFS” file system. These improvements include using attributes rather than names, logical collections rather than directories, and introducing a query language to the API. We assess the expressive capacity of the resulting model, demonstrate that it solves the relevant shortcomings of traditional file systems in this space, and compare our new approach to those provided by others and our earlier work. We conclude that attributes are better suited than tags to overcome traditional HFS shortcomings.

Waqf plays an important role and offers significant contributions in various sectors, especially in the advancement of Islamic boarding school education. The purpose of this study is to explore the implementation of productive waqf development as a means of educational empowerment at the Daarul Qolam Binjai Islamic Boarding School. The approach used in this study is qualitative descriptive. This research was conducted at the Daarul Qolam Islamic boarding school in Binjai, where data was collected through observation, interviews, and documentation, while data verification was carried out through triangulation techniques. The results of the study indicate that first, the strategy of developing productive waqf for educational empowerment at the Daarul Qolam Binjai Islamic Boarding School aims to increase the independence of the pesantren and develop human resources as business actors through various assets and programs managed by the Daarul Qolam Binjai Islamic Boarding School Waqf Agency. Second, the productive waqf mechanism is implemented through a systematic, detailed, and in accordance with legal provisions. Third, the development of productive waqf plays a crucial role in the progress and welfare of the education of students at the Daarul Qolam Binjai Islamic Boarding School. The form includes the allocation of productive waqf utilization which is prioritized on strengthening Islamic boarding schools and support for education, including the provision of logistics, infrastructure development, facilities and infrastructure, and Islamic boarding school operations, which are partly sourced from the surplus of productive waqf assets.  

This study aims to explore the influence of religiosity on depression levels among working women in Indonesia, using data from the Indonesian Family Life Survey (IFLS) 2007-2014. With the increasing participation of women in the workforce, the challenge of maintaining mental health, particularly depression, becomes increasingly relevant. Employing a quantitative approach and logistic regression analysis, this study examines the relationship between religiosity, socioeconomic variables, and depression levels. The results show that religiosity has a significant positive effect on the probability of not experiencing depression, while age, education, and income also exhibit significant positive influences. Conversely, living in urban areas is found to have a significant negative impact. These findings highlight the importance of religiosity and socioeconomic factors in shaping the mental well-being of working women in Indonesia.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.