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Alzheimer's disease is a common form of neurodegenerative disorders characterized by defective brain cells, such as neurofibrillary tangles and amyloid plaque that is progressive. One of the physical characteristics of someone suffering from Alzheimer's disease is shrinking of the hippocampus area of the brain. The hippocampus is the smallest part of the brain that serves to save memory. The detection of Alzheimer's disease can be done using a Magnetic Resonance Image (MRI) which is a technique of noninovasive for an analysis of the structure of the brain in the Alzheimer's patient. In this research, K-Means Clustering and Watershed method are used to segment the hippocampus area which is one part of the brain that was attacked by Alzheimer's disease. The analysis used to detect Alzheimer's is comparing the value of the threshold with the number of white pixels in the images. The data used in this research are Open Access Series of Image Studies (OASIS) database by using the image of coronal slices. Based on the our experiment result, both K-Means Clustering and Watershed method can segment the hippocampus area to detect Alzheimer's disease.

Diabetic Retinopathy (DR) is one of the eye diseases suffered by diabetes patients that will cause blindness if it does not get effectively treated for a certain period of time. Early detection is needed to help patients get effective treatment based on their severity. Researchers have done copious amounts of research regarding the methods for DR detection using shallow learning and deep learning approaches. The proposed method in this paper is a combination of two deep learning architectures, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). CNN is used to detect lesions on retinal fundus images, and LSTM is used for generating description sentences based on those lesions. In the training and testing process, the CNN output will be used for the input of LSTM. The training process's target is to produce a model that can map retinal fundus images into a sentence. The results of this experiment using the MESSIDOR data set has an accuracy of around 90%.

In modern times, the chatbot is implemented to store data collected through a question and answer system, which can be applied in the Python program. The data to be used in this program is the Cornell Movie Dialog Corpus which is a dataset containing a corpus which contains a large collection of metadata-rich fictional conversations extracted from film scripts. The application of chatbot in the Python program can use various models, the one specifically used in this program is the LSTM. The output results from the chatbot program with the application of the LSTM model are in the form of accuracy, as well as a data set that matches the information that the user enters in the chatbot dialog box input. The choice of models that can be applied is based on data that can affect program performance, with the aim of the program which can determine the high or low level of accuracy that will be generated from the results obtained through a program, which can be a major factor in determining the selected model. Based on the application of the LSTM model into the chatbot, it can be concluded that with all program test results consisting of a variety of different parameter pairs, it is stated that Parameter Pair 1 (size_layer 512, num_layers 2, embedded_size 256, learning_rate 0.001, batch_size 32, epoch 20) from File 3 is the LSTM Chatbot with the avg accuracy value of 0.994869 which uses the LSTM model is the best parameter pair.

All living viruses have important structures such as protein. Proteins can interact with each other forming large networks of Protein-Protein Interaction (PPI). In order to facilitate the study of these PPI networks, there needs to be clustering analysis of the PPI. In this research, we use PPI network datasets from SARS-CoV-2 and humans. The interactions of the PPI network will then be formed into graphs. Regularized Markov Clustering (RMCL) is used to perform graph clustering. RMCL consists of three main steps which are regularization, inflation, and pruning. The RMCL algorithm is a variant of Markov Clustering (MCL). However, the inflation parameter in RMCL must be inputted manually by the user to obtain the best results. To solve the limitations of RMCL, we developed a new method by combining each Cuckoo Search (CS) and Ant Lion Optimization (ALO) with the original RMCL algorithm. The optimizers are used to optimize the inflation parameter in RMCL. CS and ALO are a part of swarm intelligence which is inspired by the behaviour of cuckoo birds and antlions in nature. The results show that the interactions formed from CS-RMCL vary from 1401 to 1402. It is more stable than the interactions formed from ALO-RMCL which ranges from 1408 to 3641. The difference between the best elite in each iteration of ALO-RMCL is very influential to the interaction compared to the best nest from the CS-RMCL.

In mid-September of 2020, WHO released data starting that more than 28 million people worldwide have contracted coronavirus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the full name coronavirus, specifically Covid-19. This virus attacks the human respiratory system and can cause infection in the human lungs and even death. WHO noted that more than 900 thousand people worldwide have died due to being infected with the coronavirus. In Indonesia, more than 210 thousand people have been infected by the coronavirus, and more than 8,5 thousand of them have died. Based on this data, it is necessary to analyze the coronavirus’s kinship to reduce the spreading. This research uses The Euclidean distance in determining the distance matrix. This research will then use the Hierarchical Clustering method for analyzing the genetic relationship on DNA SARS-CoV-2 sequences. This research will take samples of SARS-CoV-2 DNA sequences from 20 countries infected. From the simulation result, the ancestors of SARS-CoV-2 coming from China. Besides, it also found that the SARS-CoV-2 DNA sequence from Indonesia has the closest ancestor from Pakistan.

Covid-19 is a global disease that has already infected people in the various parts of the world with increasing cases each day. So far, there has been around 20 million cases of Covid-19 that have occurred around the world. Furthermore, a lot of research has been conducted to overcome and cure this disease. One of the studies was uses protein-protein interactions (PPI) in Sars-Cov-2 and other coronavirus to analyze the interactions on the virus which can be used to find out more about how this virus interacts with each other. In this study, we used Markov Clustering (MCL) to analyze this virus. There are many variations of Markov Clustering that have been used in various studies, one of the variations that used in this study is Soft Regularized Markov Clustering (SR-MCL). This model is used to ensure that modules on protein interactions do not overlap and can be used for better analysis. The result shows that SR-MCL can be used to determine the cluster from PPI of Sars-Cov-2 and the other related coronavirus.

Proteins are complex organic compounds made up of smaller units called amino acids that are bonded together in long chains. Protein interacts with other proteins or molecules and becomes essential in the structure, function, and regulation of organisms' cells. The Protein-Protein Interaction (PPI) results in a considerably large network. Consequently, there is a need to find a method to simplify the network for easy interpretation of the protein-protein interaction. One of the most common methods is Markov Clustering (MCL). MCL has been applied to solve graph clustering problems based on stochastic flow simulation. MCL has three main stages in the process, namely expansion, inflation, and pruning. Although MCL produces a fast and well-balanced non-hierarchical clustering, it has a limitation where the results depend on the inflation parameter being inputted manually. In this study, we develop a method to combine Markov Clustering (MCL) with Cuckoo Search (CS) and Ant Lion Optimization (ALO) Algorithm. CS and ALO are applied in MCL algorithm to obtain an optimized inflation parameter automatically. PPI network of SARS-CoV-2 and other related coronavirus datasets are used in this research and is presented in the form of a graph. The experiment shows that CS-MCL forms 47 clusters, while ALO-MCL yields 14 cluster on the PPI dataset.

A time series with binary response variable is called a binary time series. Binary time series can be modelled using the Autoregressive general model and nonlinear regression approach. Kedem & Fokianos introduced a binary time series model through the Autoregressive and logistic regression approach. The parameters of binary time series are estimated using the Partial Likelihood method. The Partial Likelihood method is performed by determining the Partial Likelihood function derived from the marginal probability density function (pdf) of Bernoulli distribution. However, in the process of parameter estimation using this method, the form of final function to obtain parameters is not in the closed form equation. To face this problem, Fisher scoring iterations are performed. The application of parameter estimation of the model uses the data about boat racing competition between the University of Cambridge and Oxford University from 1946 to 2011. Based on the data application, parameter estimation of the binary time series model using partial likelihood with different amounts of data resulting in a relatively same or no significant parameter estimator.

The existence of COVID-19 in Indonesia is not the only disease which we must be aware of. The Health Ministry has said that Dengue Hemorrhagic Fever is as dangerous as COVID-19 and must also be treated with caution. Based on data, until July 2020, there are 71,633 dengue cases in Indonesia and DKI Jakarta has the sixth-highest dengue incidence number. One of the factors that affects the spread of dengue vector is weather. It is necessary to predict the number of dengue incidences so that the dengue handling and prevention efforts can be done optimally. In this study, the number of dengue incidences will be predicted by involving weather factors (rainfall, temperature, and humidity) using Extreme Learning Machine and Artificial Neural Network-Back Propagation and also comparing the both of their performance. The result shows that Extreme Learning Machine can give the dengue incidence prediction in DKI Jakarta with the best RMSE testing result of 0.04584, which is more accurate than the dengue incidence prediction that is given by using Artificial Neural Network-Back Propagation with 100 epochs. Moreover, Extreme Learning Machine can do the training process faster than Artificial Neural Network-Back Propagation.

A Pulmonary Tuberculosis (pulmonary TB) is a chronic infectious disease caused by Mycobacterium tuberculosis. Chronic infection cause the body in a state of oxidative stress. In the state of stress, stress hormone production increases and can affect the increase of blood sugar levels which then trigger the occurrence of diabetes mellitus (DM). The purpose of this study is to determine the factors associated with the emergence of DM Type 2 and make a classification to characterize DM Type 2 in patients with pulmonary TB. In this research, the data used are secondary data of pulmonary TB patients obtained from Cipto Mangunkusumo Hospital (RSCM) for six years, 2012 - 2017. Chi Square Automatic Interaction Detection (CHAID) method is used to classify categorical data by dividing the data set into subgroups. The dependent variable is type 2 DM status, and the independent variables are gender, age, body mass index, level of neutrophil, level of lymphocytes, erythrocyte sedimentation rate, and anti-tuberculosis medicines such as rifampicin, isoniazid, pyrazinamide, ethambutol, and streptomicin. There are seven classes are obtained by classification using CHAID. The result from analysis of CHAID method shows that the factors related to the occurrence of type 2 DM on TB patients are age, body mass index, gender and pirazinamid (Z). Based on CHAID method, a classification of the occurrence of type 2 DM on pulmonary TB patients is obtained, which is pulmonary TB patients who are ≥ 40 years old, have body mass index ≥ 25 kg/m2, and the most patients are male (Class-4th).