This article discusses the maize leaf disease image classification. The experimental images consist of 200 images with 4 classes: healthy, cercospora, common rust and northern leaf blight. There are 2 steps: feature extraction and classification. Feature extraction obtains features automatically using convolutional neural network (CNN). Seven CNN models were tested i.e AlexNet, virtual geometry group (VGG) 16, VGG19, GoogleNet, Inception-V3, residual network 50 (ResNet50) and ResNet101. While the classification using machine learning methods include k-Nearest neighbor, decision tree and support vector machine. Based on the testing results, the best classification was AlexNet and support vector machine with accuracy, sensitivity, specificity of 93.5%, 95.08%, and 93%, respectively.

Image classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70%  and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%,  and 85%.

Participation from all relevant stakeholders is important to achieve the goal of every activity successfully. Nowadays, Information and Computer Technologies, for example, Biometrics, Internet of Things (IoT) and Big Data are used to support participation from the relevant stakeholders. Electronic Participation (e-Participation) already utilized broadly to empower people participation in politics, business, government, cultural activities. Moreover, Biometrics has been used broadly for an identification system. Biometrics system identifies physiological instead of behavioral attributes, such as palm veins, iris recognition, face recognition, fingerprint, DNA, palm print, hand geometry, retina, and odor/scent. Therefore, this research would like to collaborate e-Participation and Biometrics fields from the multidisciplinary perspective. Furthermore, the literature reviews show that research collaboration between e-Participation and Biometrics Technologies are still limited. Hence, the objective of this research was to develop a novel conceptual model of e-Participation using Biometrics technologies. This paper contributes by developing a novel conceptual model of biometrics technologies for e-Participation implementation. This research has some implications. For theory development, this research contributes to the novel conceptual model in e-Participation, E-Government, Information Systems, Informatics, Computer Science, Image Processing, and Biometrics fields. For Practice, the novel model could be utilized for practitioners, policy-makers, and other relevant stakeholders for e-Participation implementation using Biometrics Technologies.

Corn is a plant that is widely grown in developing countries such as Indonesia. To increase maize yields, researchers are always innovating on the current state of technology for classifying maize plant diseases. Three kinds of diseases attack corn leaves, namely Gray leaf Spot, Blight, and Common Rush. The amount of data that we use is 3500 data consisting of 500 Gray Leaf Spots, 1000 Blights, 1000 Common Rushes, and 1000 healthy leaves. This study aims to develop an artificial intelligence model. The artificial intelligence model that we developed uses LBP feature extraction combined with k-NN for the classifier. In addition to using the k-NN method, our tests were carried out using several classification methods such as Naïve Bayes and Adaboost. The result of our test is that the k-NN method has the highest value compared to the Naïve Bayes and Adaboost methods. The results of the performance using k-NN with k=5 resulted in a value of 81.1%, the AUC value of 94.1%, the F1-Score of 80.9%, Precision of 81.8%, and Recall of 81.1%.

Recently, computer vision research results have supported many sectors to assist and solve problems. One of branch of the computer vision fields is biometric system. Many modalities have been implemented to depict the human characteristics. Face is one of the modalities that has been employed to recognize the human. A crucial problem of the face recognition is high dimensionality. The problem would impact on the computational performance, and even it could cause the process failure. Feature extraction is the solution to reduce the dimensionality. However, many cases have shown that feature extraction could fail as singularity problem. In this research, we proposed the improvement of the fisherface algorithm to solve the singularity problem. We have modified the singularity covariance matrix so that the matrix can be further handled and processed. The purpose of the paper is to improve the performance of the fisherface algorithm. We have verified our proposed algorithm by using the Olivetty Research Laboratory face image. We applied 7-cross validations to evaluate our proposed algorithm, the evaluation results achieved more than 92% accuracy.