Sample preparationThe components of the bioactive ligands found in juwet and moringa are gallic acid (CID: 370), ellagic acid (CID: 5281855), corilagin (CID: 73568), moringyne (CID: 131751186), myricetin (CID: 5281672), chlorogenic acid (CID: 1794427), vitexin (CID: 5280441), and nirmatrelvir (CID: 155903259) as a control. Ligand structure data were obtained

Many troubles in life require decision-making with convoluted processes because they are caused by uncertainty about the process of relationships that appear in the system. This problem leads to the creation of a model called the Bayesian Network. Bayesian Network is a Bayesian supported development supported by computing advancements. The Bayesian network has also been developed in various fields. At this time, information can implement Bayesian Networks in determining the extent of damage to buildings using individual building data. In practice, there is mixed data which is a combination of continuous and discrete variables. Therefore, to simplify the study it is assumed that all variables are discrete in order to solve practical problems in the implementation of theory. Discretization method used is the K-Means clustering because the percentage of validity obtained by this method is greater than the binning method.

The Bayesian networks are a graphical probability model that represents interactions between variables. This model has been widely applied in various fields, including in the case of disaster. In applying field data, we often find a mixture of variable types, which is a combination of continuous variables and discrete variables. For data processing using hybrid and continuous Bayesian networks, all continuous variables must be normally distributed. If normal conditions unsatisfied, we offer a solution, is to discretize continuous variables. Next, we can continue the process with the discrete Bayesian networks. The discretization of a variable can be done in various ways, including equal-width, equal-frequency, and K-means. The combination of BN and k-means is a new contribution in this study called the k-means Bayesian networks (KMBN) model. In this study, we compared the three methods of discretization used a confusion matrix. Based on the earthquake damage data, the K-means clustering method produced the highest level of accuracy. This result indicates that K-means is the best method for discretizing the data that we use in this study.

Pterygospermin, Quercetin, Rutin, and β-amyrin which has an antiviral potential compounds against COVID 19 by inhibiting M pro and RdRp activity. [15][16][17][18] Besides all the compounds above, Oleic acid was most found at around 84% in M. Oleifera. 19 M. oleifera was the most appropriate candidate for an antiviral agent against SARS-CoV-2. The aim of this study was to screen bioactive compounds of Moringa oleifera and to identify the antiviral potential compounds toward SARS-CoV-2 through an entry inhibitor mechanism. METHODS Data mining of sampleThe bioactive compounds of M. oleifera which consist of anthraquinone, apigenin, aurantiamide acetate, benzyl isothiocyanate, chlorogenic acid, chrysin, dibutyl phthalate, ellagic acid, hesperidin, isorhoifolin, myricetin, pterygospermin, quercetin, rutin, and vitex. The bioactive compounds of M. oleifera were retrieved format from PubChem database (https://pubchem.ncbi.nlm.nih.gov/) in sdf format. 20 Protein modelingThe structure of M pro and RdRp as the target proteins which were not available in the RCSB PDB database was modeled based on their amino acid sequence. The NCBI (https://www.ncbi.nlm.nih.gov/) database was used to retrieve sequences of amino acids with fasta format. Furthermore, protein modeling is made through the SWISSMODEL site (https://swissmodel. expasy.org/). The selection of protein models was selected from several parameters such as QMQE value, QMEAN value, coverage value, local quality value, and comparison plot. In addition, the protein

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