Macular Edema Classification Using Self-organizing Map and Generalized Learning Vector Quantization

Macular edema is a kind of human sight disease as a result of advanced stage of diabetic retinopathy. It affects the central vision of patients and in severe cases lead to blindness. However, it is still difficult to diagnose the grade of macular edema quickly and accurately even by the medical doctor\u27s skill. This paper proposes a new method to classify fundus images of diabetics by combining Self-Organizing Maps (SOM) and Generalized Vector Quantization (GLVQ) that will produce optimal weight in grading macular edema disease class. The proposed method consists of two learning phases. In the first phase, SOM is used to obtain the optimal weight based on dataset and random weight input. The second phase, GLVQ is used as main method to train data based on optimal weight gained from SOM. Final weights from GLVQ are used in fundus image classification. Experimental result shows that the proposed method is good for classification, with accuracy, sensitivity, and specificity at 80%, 100%, and 60%, respectively

Design of E-New Higher Education Quality Assurance Using Waterfall Method

This study is focused on designing e-quality assurance services to improve the performance of the USN Kolaka FTI Quality Assurance group, which during the COVID-19 pandemic was still applying manual SOP submissions and approvals. the importance of designing e-quality assurance considering the magnitude of its support in academic policy making during the new normal adaptation period in order to support the implementation of quality education in accordance with expectations. the results of this study indicate that 95% of users agree that the faculty's e-quality assurance is able to encourage increased work productivity of GJM FTI through the use of the online SOP draft submission feature to approval and automatic review. The importance of adding MONEV and Internal Audit features will maximize the performance of GJM FTI in the future

Penggabungan Fitur Bentuk dan Fitur Tekstur yang Invariant terhadap Rotasi untuk Klasifikasi Citra Pap Smear

. Pap test is a cervical cancer screening manually and requires a long time that it needs an exact cell classification system based computers. Features determination by observation in characteristic differences between the datasets visually betweenclass will help a cell classification results which has relevant characteristics between classes. In addition, the change in orientation of the cells at the time of the acquisition will affect the value of the generated feature so extraction method that is rotation invariant is needed to overcome that problem. This research proposes the combination of simple shapes feature and the texture feature from extraction Local Binary Pattern Histogram Fourier (LBP-HF) that invariant to rotation as additional features to classify pap smear images. The result show that the proposed feature combination yield good performance with accuracy 92.44% for two category cell and 70.06% for seven class cell