Biclustering, in general, is a process of clustering genes and conditions simultaneously rather than clustering them separately. The purpose of biclustering is to discover a subset from experimental data. Further, biclustering results can be analyzed from a biological perspective. Biclustering can also be used for protein-protein interaction. In protein-protein interaction, biclustering can cluster interactions based on rows and columns. In this research, we applied three biclustering algorithms based on graph approach, Binary inclusion-Maximal (BiMax), local search framework based on pairs operation (POLS), and (LCM-MBC) to clustering data of protein-protein interaction between HIV-1 and human. We change the interaction protein-protein interaction data into binary then divided into two datasets called HV positive and HV negative. Then compare the biclustering results of each dataset using heatmap and analyze them with GO terms. From dataset HV positive, BiMax found 30 biclusters, LCM-MBC 31 biclusters, and POLS 13 biclusters. From dataset HV negative, BiMax found eight biclusters, LCM-MBC 14 bicluster, and POLS 10 biclusters. Based on the results of the heatmap, all bicluster entry from BiMax is a protein that interacts, whereas biclusters entry of LCM-MBC and POLS still have proteins that do not interact. It can be concluded that BiMax algorithm is good for clustering protein-protein interaction, especially for binary data.
<span lang="EN-US">Indonesia is a major exporter and producer of coffee, and coffee cultivation adds to the nation's economy. Despite this, coffee remains vulnerable to several plant diseases that may result in significant financial losses for the agricultural industry. Traditionally, plant diseases are detected by expert observation with the naked eye. Traditional methods for managing such diseases are arduous, time-consuming, and costly, especially when dealing with expansive territories. Using a model based on transfer learning and deep learning model, we present in this study a technique for classifying Robusta coffee leaf disease photos into healthy and unhealthy classes. The MobileNetV2 network serves as the model since its network design is simple. Therefore, it is likely that the suggested approach will be deployed further on mobile devices. In addition, the transfer learning and experimental learning paradigms. Because it is such a lightweight net, the MobileNetV2 system serves as the foundational model. Results on Robusta coffee leaf disease datasets indicate that the suggested technique can achieve a high level of accuracy, up to 99.93%. The accuracy of other architectures besides MobileNetV2 such as DenseNet169 is 99.74%, ResNet50 architecture is 99.41%, and InceptionResNetV2 architecture is 99.09%.</span>
Indonesia is a legal state that chooses a leader based on the results of general elections, such as the election of presidents and regional leaders. Electability is statistical data for each pair of candidates who show public interest to choose the candidate. Electability data is usually obtained from the results of questionnaires or interviews with constituents. The data search process is carried out by a survey institution. Most people discuss voluntarily in social media related to the candidate that they will choose. This study uses discussion data from social media to calculate the electability of each pair of candidates by using cluster method. The cluster method is K-Means. K-Means employs euclidean distance to determine the cluster of each data, while the number of cluster can be determined by the user. This study proposes SKM3 model (Subcontrolled K-Means Max-Min), which applies the minimum and maximum average values to decide the cluster of each data. SKM3 cluster is controlled by K-Means method that uses Euclidian distance. SKM3 model is processed using news data from detik.com site for the election of regional leader of West Java, Central Java, and East Java. The error value of SKM3 model is calculated through RMSE (Root Mean Square Error). The error value of West Java is 0.0452, the error value of Central Java up to 0.0343, and the error value of East Java is 0.2382. Based on the error values of each electoral region, it shows that SKM3 model has a small error value, so it can be concluded that SKM3 model is good for calculating the electability of the leader by using clustering method.
One indicator of the severity of diabetic retinopathy is the existence of lesion characteristics in the eyes such as microaneurysm, hemorrhages, exudates, and neovascularization. Without proper early medical attention, this lesion could lead to blindness. Considering its importance, a system that could detect such lesions will be beneficial. This paper investigates the lesion characteristics of diabetic retinopathy from fundus images such as microaneurysm (redsmalldots), exudates, hemorrhages, and neovascularization. In this study, we present three of feature extraction methods, i.e., Local Binary Pattern (LBP), Gray Level Co-Occurrence Matrix (GLCM) and Segmentation Fractal Texture Analysis (SFTA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) chosen as classifiers for classifying five classes (redsmalldots, hemorrhages, hard exudates, soft exudates, and neovascularization). The data used in this research obtained from DiaretDB0 database. The experimental results show that our proposed method can detect the lesion characteristics of diabetic retinopathy with higher accuracy of 86,84% and 96% for SVM and KNN, respectively.
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