## 4 Conclusions  
Tsunami inundation scenarios and mitigation efforts can be conveyed through tsunami modeling. However, ordinary people have difficulty understanding currently used tsunami models. The contribution of this research is the development of a method that can be used to determine the distribution point of tsunami waves through the 3D orthomosaic technique that compares the heights of the buildings closest to the shoreline and a Digital Elevation Model (DEM) of the buildings closest to the shoreline and predicts the height of tsunamis waves and their slopes as they reach land. The 3D visualization results from this modeling provide a better understanding of the behaviors of tsunami inundation on land. Moreover, the 3D visualization is easier to understand by ordinary people and the policy-making community. Hence, it can be used for developing strategic policies for tsunami risk reduction, for example, for preparing evacuation routes and locations for building shelters and contingency planning. The model can also be used as a medium for disaster education and the delivery of accurate disaster information. This research is limited to describing the process of conducting 3D modeling, the prediction of distribution, and determining the endpoint distance of the tsunami wave. Therefore, t further improvement in this area of research is needed in order to determine the strength of tsunami waves, the time of wave landing, the time of inundation from the shoreline to the endpoint of the waves on land, and the passage of time to the safe zone or \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Object Type**} & \multicolumn{2}{c}{**Latitude and Longitude**} & \multicolumn{2}{c}{**Latitude and Longitude**} & \multirow{2}{*}{**Distance**} \\  & **Start Points** & & **End Points** & \\ \hline \multirow{2}{*}{Scatter point 1} & \({}^{7}\)\({}^{\circ}\)43\({}^{\prime}\)39.52\({}^{\circ}\) S 109\({}^{\circ}\)01\({}^{\prime}\)28.46\({}^{\circ}\) E & 7\({}^{\circ}\)43\({}^{\prime}\)35.28\({}^{\circ}\) S 109\({}^{\circ}\)01\({}^{\prime}\)16.88\({}^{\circ}\) E & \multirow{2}{*}{381 M} \\  & 8.388 m & 7.766 m & \\ \hline \multirow{2}{*}{Scatter point 2} & \({}^{7}\)\({}^{\circ}\)43\({}^{\prime}\)45.98\({}^{\circ}\) S 109\({}^{\circ}\)01\({}^{\prime}\)24.64\({}^{\circ}\) E & 7\({}^{\circ}\)43\({}^{\prime}\)42.81\({}^{\circ}\) S 109\({}^{\circ}\)01\({}^{\prime}\)13.14\({}^{\circ}\) E & \multirow{2}{*}{367 M} \\  & 7.530 m & 6.443 m & \\ \hline \multirow{2}{*}{Scatter point 3} & \({}^{7}\)\({}^{\circ}\)43\({}^{\prime}\)49.06\({}^{\circ}\) S 109\({}^{\circ}\)01\({}^{\prime}\)25.11\({}^{\circ}\) E & 7\({}^{\circ}\)43\({}^{\prime}\)46.55\({}^{\circ}\) S 109\({}^{\circ}\)01\({}^{\prime}\)03.24\({}^{\circ}\) E & \multirow{2}{*}{674 M} \\  & 7.206 m & 5.639 m & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Distribution points and distances from tsunamis waves. assembly points. Future research should consider using these factors. Additionally, this modeling technique could be used to assess tsunami risk reduction. Three-dimensional modeling enables the identification of risk factors, the assessment of the economic impact of disaster damage, and assistance with spatial planning and disaster management\". Data curation, A.Y., N.R.H., A.P., B.N. and A.M.R. ; Formal analysis, A.P., B.N. and A.M.R. ; Funding acquisition, Y.K. ; Investigation, N.R.H. ; Methodology, M.Y.R. ; Project administration, S.K.K. ; Supervision, M.Y.R. ; Validation, N.R.H. and Y.K. ; Visualization, M.Y.R. ; Writing--original draft, M.Y.R. ; Writing--review & editing, A.Y., N.R.H., Y.K. and S.K.K. All authors have read and agreed to the published version of the manuscript. The writing team would like to express their deepest gratitude to the National Research and Innovation Agency (BRIN), InaTews Program, Research Center for Geotechnology, and Research Center for Informatics. In particular, we would like to extend many thanks and our appreciation to Kagawa University in Japan, which helped fund the publication of this paper.