Hate speech detection is crucial in curbing online toxicity and fostering a safer digital environment. Previous research has proposed the use of hybrid CNN-RNN models for this purpose. This study aims to improve the performance of the hybrid CNN-RNN method by using a double-layer approach to address imbalanced datasets. The novelty lies in using double layers of hybrid CNN-RNN to improve hate speech detection accuracy. The research also employs oversampling techniques alongside the double-layer model. The process includes pre-processing, feature extraction, training tuning, testing, and performance evaluation. Results demonstrate that the double-layers Hybrid CNN-RNN model achieved an accuracy of 0.827, precision of 0.797, recall of 0.759, and F1 score of 0.883 with imbalanced data. Balanced data yielded an even higher accuracy of 0.908, precision of 0.943, recall of 0.894, and F1 score of 0.914. Moreover, the proposed model outperforms the hybrid CNN-RNN with imbalanced datasets, showing an accuracy of 0.752, precision of 0.797, recall of 0.559, and F1 score of 0.657. Techniques such as dropout and early stopping address overfitting in complex models and large datasets. This research advances hate speech detection methodologies by demonstrating the effectiveness of a double-layer hybrid CNN-RNN model, especially for imbalanced data. It underscores the importance of addressing imbalanced datasets for improved model accuracy. Future work could explore alternative data augmentation techniques or compare the proposed model with other architectures.

Prior to export, papaya are subjected to inspection for the purpose of quality control and grading. For size grading, the fruit is weighed manually hence the practice is tedious, time consuming and labor intensive. Therefore, this paper will discuss the development of a computer vision system for papaya size grading using shape characteristic analysis. The methodology involves data acquisition to collect the images and their weights. The RGB images were converted to binary images using automatic thresholding based on the Otsu method. Some morphological procedures were involved for image enhancement to distinguish the papaya object from the background. Then the shape characteristics consisting of area, mean diameter and perimeter were extracted from the papaya images. We classified according to combinations of the three features to study the uniqueness of the extracted features. Each combination was fed separately to a neural network for training and testing. The proposed technique showed the ability to perform papaya size classification with more than 94% accuracy in this research.

Penerapan Algoritma C4.5 untuk Klasifikasi Jenis Pekerjaan Alumni di Universitas Muhammadiyah Yogyakarta

A weed can be thought of as any plant growing in the wrong place at the wrong time and doing more harm than good. Weeds compete with the crop for water, light, nutrients and space, and therefore reduce crop yields and also affect the efficient use of machinery. The most widely used method for weed control is to use agricultural chemicals (herbicides and fertilizer products). This heavy reliance on chemicals raises many environmental and economic concerns, causing many farmers to seek alternatives for weed control in order to reduce chemical use in farming. Since hand labor is costly, an automated weed control system may be economically feasible. A real-time precision automated weed control system could also reduce or eliminate the need for chemicals. In this research, an intelligent real-time automatic weed control system using image processing has been developed to identify and discriminate the weed types namely as narrow and broad. The core component of vision technology is the image processing to recognize type of weeds. Two techniques of image processing, GLCM and FFT have been used and compared to find the best solution of weed recognition for classification. The developed machine vision system consists of a mechanical structure which includes a sprayer, a Logitech web-digital camera, 12v motor coupled with a pump system and a small size CPU as a processor. Offline images and recorded video has been tested to the system and classification result of weed shows the successful rate is above 80%.

For export, papaya fruit should be free of defects and damages. Abnormality in papaya fruit shape represents a defective fruit and is used as one of the main criteria to determine suitability of the fruit to be exported. This paper describes a wavelet-based technique used to perform feature extraction to extract unique features which are then used in the classification task to discriminate deformed papaya fruits from well formed fruits using image processing approach. The extracted features, when used in the classification task using linear discriminant analysis (LDA), afford accuracy of more than 98%.