Enthusiastic : International Journal of Applied Statistics and Data Science

Loan Approval Classification Using Ensemble Learning on Imbalanced Data

2024-08-03T05:00:57+00:00

Loan processing is an important aspect of the financial industry, where the right decisions must be made to determine loan approval or rejection. However, the issue of default by loan applicants has become a significant concern for financial institutions. Hence, ensemble learning needs to be used with random forest and Extreme Gradient Boosting (XGBoost) algorithms. Unbalanced data are handled using the Synthetic Minority Over-sampling Technique (SMOTE). This research aimed to improve accuracy and precision in credit risk assessment to reduce human workload. Both algorithms used a dataset of 4,296 with 13 variables relevant to making loan approval decisions. The research process involved data exploration, data preprocessing, data sharing, model training, model evaluation with accuracy, sensitivity, specificity, and F1-score, model selection with 10-fold cross-validation, and important variables. The results showed that XGBoost with imbalanced data handling had the highest accuracy rate of 98.52% and a good balance between sensitivity of 98.83%, specificity of 98.01, and F1-score of 98.81%. The most important variables in determining loan approval are credit score, loan term, loan amount, and annual income.

Detection and Quantification of Glandular Trichomes (Bulbous) on Potato Plant Leaf Images Using Deep Learning

2024-09-20T06:37:17+00:00

Potato plants have a very high nutritional value, making them widely cultivated in Indonesia. To ensure the cultivation of potatoes has good quality, many individuals, ranging from farmers to researchers and plant breeders, strive to explore and understand the characteristics of plant resistance sources, one of which is through the role of trichomes. Trichomes are fine hairs that coat the outer surface of plant leaves, serving as a physical barrier and regulating plant temperature. Identification and quantification of trichomes are commonly conducted manually by researchers, which consumes much time and is inefficient. Therefore, a system that can automatically detect and quantify trichomes is crucial to avoid manual identification and quantification, allowing these processes to be carried out more quickly. This study utilized a deep learning approach to train a model capable of detecting and quantifying trichome objects. The model architecture used was YOLOv8. From the training process, the resulting mean average precision (mAP) at a confidence threshold of 50 was 0.816, while the mAP at a confidence threshold of 90 was 0.38. This model is expected to assist experts or researchers in the field of agriculture in identifying trichomes, thereby optimizing crop yields.

Factor Influencing Delayed Completion in Mathematics Students at Nusa Cendant University: A Factor Analysis Approach

2024-08-03T05:06:09+00:00

This study aimed to identify and analyze factors contributing to the delay in the study period of students enrolled in the Mathematics Program at the Faculty of Science and Engineering (FST), Nusa Cendana University (UNDANA). The research employed a comprehensive analytical approach, starting with validity and reliability tests, followed by descriptive analysis, and culminating in factor analysis. Initially, 27 variables were considered; however, after conducting validity and reliability assessments, 18 variables were deemed suitable for further analysis. These 18 variables were subjected to factor analysis, revealing that they could be consolidated into four distinct factors, collectively accounting for 68.734% of the total variability observed among the students. The four identified factors influencing study delays are (1) student and supervisor commitment to completing the final project, (2) campus and peer support, (3) intelligence and discipline, and (4) motivation and relationships. Among these, the commitment of students and their supervisors to the timely completion of the final project emerged as the most dominant factor, demonstrating 43.417% of the total variance. The findings highlight the crucial role of both individual dedication and external support systems in ensuring timely academic progress, offering valuable insights for improving student outcomes in the Mathematics Program at UNDANA.