21 research outputs found
Mathematics in Transition: Assessing the Impact of Curriculum Changes on Student Performance Metrics
Get PDFCurriculum changes in higher education, especially in mathematics, are intended to align academic content with scientific advancements and evolving workforce demands; however, such reforms often bring unintended academic challenges for students. In Indonesia, recent changes in the 2016, 2020, and 2024 mathematics curricula introduced shifts in course credit allocations, course learning outcomes (CLOs), material scope, instructional methods, and evaluation systems. This study specifically aims to evaluate the impact of these curriculum changes on student academic performance across five core mathematics courses: Introduction to Data Science, Calculus 1, Calculus 2, Linear Algebra, and Mathematical Statistics. Employing a quantitative, exploratory approach, the research analyses academic records from 586 students using descriptive statistics and visualisation techniques such as boxplots and bar-line charts. The findings reveal fluctuating average grades and a general decline in pass rates, particularly under the 2024 curriculum, which introduced more complex CLOs, deeper content coverage, and application-oriented assessments. These results highlight the urgent need to balance curriculum innovation with student readiness and provide valuable insights for curriculum development and educational policy planning.
Analysis of Students' Academic Performance in the Department of Mathematics Based on Semester GPA Dynamics: A Case Study of the 2017–2024 Cohorts
Get PDFThis quantitative exploratory study investigates changes in students' Semester Grade Point Average (GPA) and their relationship with graduation status and study duration. It uses academic records from the Department of Mathematics at a public university in Indonesia for cohorts from 2017 to 2024. The study addresses concerns raised after the COVID-19 pandemic, which may have disrupted academic progression and altered the predictive power of initial GPA on graduation outcomes a gap not sufficiently explored in existing literature. Data were collected directly from the university's academic database, ensuring accuracy and consistency without relying on self-reported surveys. Descriptive statistical methods and visual analytics (e.g., line charts, boxplots, and scatter plots) were applied to uncover trends and patterns. Results show that earlier cohorts (2017–2020) have high graduation rates (82.7%–94.4%), while the 2019 cohort recorded the highest dropout rate (11.1%). Newer cohorts (2021–2024) predominantly consist of students still enrolled, though some early graduations and dropouts occurred. A positive correlation was found between first-semester GPA and graduation success, yet the pandemic likely introduced new variables that affect academic outcomes. These findings provide actionable insights for academic policy and support the development of early detection systems to identify students at academic risk
Utilizing the Welch-Powell Algorithm and the IDO (Incident Degree Ordering) Algorithm in Traffic Light Settings
Get PDFThe road junction needs some help with the timing of traffic lights. One method for optimizing crossroads traffic light settings is using a graph approach that applies a vertex coloring algorithm. The Welch-Powell and IDO (Incident Degree Ordering) algorithms are used to solve this problem. This case study covers two crossroads, namely: the crossroads of Prof. Dr. H.B. Jassin, Jenderal Sudirman Street, and the crossroads of Prof. Dr. H.B. Jassin, Palma, Sarini Abdullah Street. The result showed that the Welch-Powell and IDO algorithms used for vertex coloring produced XG=3 chromatic numbers for Prof. Dr. H.B Jassin, Jenderal Sudirman Street, and XG=4 for Prof. Dr. H.B Jassin, Palma, and Sarini Abdullah Street. New data shows that green-light efficiency increases by 23.85% and red-light efficiency decreases by 19.26% for crossroads of three, and new data at crossroads of four shows that data in the field is more effective than new data
Statistika Tanpa Stres : Panduan Lengkap Untuk Penelitian Cocok Untuk Mahasiswa,Karyawan,dan Umum
No full textEASY ENSEMMBLE WITH RANDOM FOREST TO HANDLE IMBALANCED DATA IN CLASSIFICATION
Get PDFImbalanced data might cause some issues in problem definition level, algorithm level, and data level. Some of the methods have been developed to overcome this issue, one of state-of-the-art method is Easy Ensemble. Easy Ensemble was claimed can improve model performance to classify minority class, and overcome the deficiency of random under- sampling. In this paper we discussed the implementation of Easy Ensemble with Random Forest Classifiers to handle imbalance problem in credit scoring case. This combination method is implemented in two datasets which taken from data science competition website, finhacks.id and kaggle.com with class proportion within majority and minority is 70:30 and 94:6. The results showed that resampling with Easy Ensemble can improve Random Forest classifier performance upon minority class. Recall on minority class increased significantly after the resampling. Before resampling, the recall on minority class for the first dataset (finhacks.id) was 0.49, and increased to 0.82 after the resampling. Similar results were obtained for the second data set (kaggle.com), where the recall for the minority class was increased from just 0.14 to 0.73
Penentuan Distribusi Dari Banyaknya 'Hit' Kerandoman Barisan Bilangan Biner Pada Metode Overlapping Template Matching Test
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Comparison of random forest and support vector machine for prediction of cognitive impairment in Parkinson's disease
No full textAnalysis Accuracy of Random Forest Model for Big Data – A Case Study of Claim Severity Prediction in Car Insurance
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