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The suggested methodology presented in this work uses data mining to identify seismic zones and time series to forecast earthquake magnitudes. Utilize historical earthquake data gathered from the United States Geological Survey (USGS) and obtained by utilizing hierarchical and fartherst first clustering to predict seismic activity. Latitude and longitude cluster data were used to create a prediction model to forecast the size of upcoming earthquakes in the Nanggroe Aceh Darussalam region and its nearby areas.

Data Envelopment Analysis (DEA) is the use of non-parametric mathematical programming that is useful for measuring the efficiency of the Decision Making Unit (DMU) of an organization. This study uses the Cooper and Rhodes (CCR) method known as the DEA-CCR multiplier which aims to determine the weight value of each input and output variable of the DMU being evaluated, but it is not sufficient to measure efficiency optimization. To get an efficient value of the weight value of each DMU as a reference to get updated DMU input and output values. So that the DMU efficiency value is obtained which is evaluated. The results of this study show how to modify the Multiplier Model-CCR into the Envelopment Model-CCR. Then displays the efficient level DMU which is evaluated as a result of the weight each DMU gets from the results of processing the LINDO application. Illustrations of changes in input variables and output variables are displayed in the form of tables and figures before and after the changes. The modified DEA-CCR model can also complete DMU super efficiency, effectiveness and productivity.

Robust optimization is based on the assumption that uncertain data has a convex set as well as a finite set termed uncertainty. The discussion starts with determining the robust counterpart, which is accomplished by assuming the indeterminate data set is in the form of boxes, intervals, box-intervals, ellipses, or polyhedra. In this study, the robust counterpart is characterized by a box-interval uncertainty set. Robust counterpart formulation is also associated with master and subproblems. Robust Benders decomposition is applied to address problems with convex goals and quasiconvex constraints in robust optimization. For all data parameters, this method is used to determine the best resilient solution in the feasible region. A manual example of this problem's calculation is provided, and the process is continued using production and operations management–quantitative methods (POM-QM) software.

Mitigation is a pre-disaster action that aims to prepare for a disaster situation. One of the mitigation activities is evacuation, which aims to reduce disaster-related losses. Because disaster damage cannot be predicted, dealing with evacuation efforts requires a dynamic model. This study will utilize a dynamic model that combines the game theory model for choosing the evacuation location and the open vehicle routing problem (OVRP) model for selecting evacuation routes to create an intelligent system. The game theory model will be used to supplement the selection of alternate evacuation locations by taking into account geographical features that are very uncertain in the event of a crisis. An evacuation route equipped with an OVRP model with the goal of optimizing travel time is required to mobilize disaster-affected people. With the development of the Intelligent Evacuation System idea, combining the two models will create a new model. The simulation model is evaluated using linear, interactive, and discrete optimizer (LINDO), which may minimize the evacuation time from the evacuation route to a safe destination by 50 minutes if there is no contra-flow to allow more people to be evacuated.

Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical example demonstrates the performance efficiency of the proposed formulation method, with the results performing confirmed that the efficiency value is 89%.

Abstract. Patana P, Alikodra HS, Mawengkang H, Harahap RH. 2023. State of human tiger conflict around Gunung Leuser National Park in Langkat Landscape, North Sumatra, Indonesia. Biodiversitas 24: 837-846. The Sumatran tiger (Panthera tigris sumatrae) is one of the key species which is considered an endangered species by the IUCN (2008). There has also been a recent threat to their population due to conflict with humans. Information about landscape characteristics and livelihood related to human-tiger conflict (HTC) is needed for a proper mitigation strategy based on local parameters. Therefore, this study was carried out around Gunung Leuser National Park (GLNP) using quantitative and qualitative methods. Principal component analysis (PCA) was used to analyze and map the risk of HTC using landscape characteristics. The physical factors, including land cover, slope, elevation, distance from rivers, and settlement, were analyzed. The livelihood aspect related to land use change was described descriptively and became a major analysis. The study area is located in the buffer zone of GLNP in Langkat District. The results showed that the Eigen weight value for the slope, elevation, and land cover factor was greater than the distance to the river and settlement. The spatial analysis also revealed that Langkat Landscape was in the medium risk of HTC by covering 60% area, while 40% was in the interval of high to very high risk. Furthermore, the high dependence on land for agriculture and the activity of livestock has become a trigger for more massive HTC in the last three years. It is a challenge in human-tiger conflict mitigation to involve the resolution of people’s livelihoods surrounding the national park. The livelihood approach could be a future solution that bridges human and tiger aspects, in addition to habitat management and protecting conservation areas.

Robot navigation is determination position as well as direction movement in a trip . Planning track is matter most important in the world of mobile robots. in short , determination track will produce dynasty that can traversed (feasible) with availability information that has there is that is map robotic environment . modelling environment map is step first for determine path planning. on the contrary for environment that doesn't known , modeling will done with the information obtained from the sensors on the robot . on the environment actually , noise and limitations accuracy from the sensor leads to the most basic problem in modeling, that is accuracy , and speed , where influence system navigation live in real-time. For resolve matter the needed the existence of a Fuzzy Description Environment which consists of over the fuzzy model of information obtained in the environment around robots. of these models will Becomes base reference path planner that is used reference to path planning. From the map environment modeling will processed by Steepest Ascent Hill Climb so produce track going to point end . From the results program simulation is obtained waypoints with _ distance shortest that is 363.2724 units distance

In K-Means Clustering, the number of attributes of a data can affect the number of iterations generated in the data grouping process. One of the solutions to overcome these problems is by using a reduction technique on the dimensions of the dataset. In this study, the authors apply the Gini Index to perform attribute reduction on the data set to reduce attributes that have no effect on the dataset before clustering with K-Means Clustering. The dataset used to be tested as a testing instrument in this research is Absenteeism at work obtained from the UCI Machine Learning Repository, with 20 attributes, 740 data records and 4 attribute classes. The results of the tests in this research indicate that the number of iterations obtained from the comparison of tests using the K-Means in a Conversional (Without Attribute Reduction) is obtained by the number of 9 iterations, while the K-Means with attribute reduction with the Gini Index obtains the number of iterations totaling 6 iterations. Clustering evaluation was calculated using Sum of Square Error (SSE). The SSE value in K-Means Clustering in a Conversional (Without Attribute Reduction) is 1391.613, while in K-Means Clustering with attribute reduction with a Gini Index, it is 440.912. From the results of the proposed method, it is able to reduce the percentage of errors and minimize the number of iterations in K-Means Clustering by reducing the dimensions of the dataset using the Gini Index

Earthquake research has not yielded promising results, either in the form of causes or revealing the timing of their future events. Many methods have been developed, one of which is related to data mining, such as the use of hybrid neural networks, support vector regressor, fuzzy modeling, clustering, and others. Earthquake research has uncertain parameters and to obtain optimal results an appropriate method is needed. In general, several predictive data mining methods are grouped into two categories, namely parametric and non-parametric. This study uses a non-parametric method with multivariate adaptive regression spline (MARS) and conic multivariate adaptive regression spline (CMARS) as the backward stage of the MARS algorithm. The results of this study after parameter testing and analysis obtained a mathematical model with 16 basis functions (BF) and 12 basis functions contributing to the model and 4 basis functions not contributing to the model. Based on the level of variable contribution, it can be written that the epicenter distance is 100 percent, the magnitude is 31.1 percent, the location temperature is 5.5 percent, and the depth is 3.5 percent. It can be concluded that the results of the prediction analysis of areas in Lombok with the highest earthquake hazard level are Malaka, Genggelang, Pemenang, Tanjung, Tegal Maja, Senggigi, Mangsit. Meninting, and Malimbu.

Penelitian ini bertujuan untuk mengetahui perbedaan peningkatan kemampuan komunikasi dan self efficacy siswa terhadap materi pokok Lingkaran sebelum dan sesudah diajarkan menggunakan model pembelajaran berbasis masalah dan contextual teaching and learning (CTL) dikelas VIII SMP Negeri 17 Medan. Populasi penelitian adalah seluruh siswa kelas VIII yang berjumlah 315 orang. Dari populasi ini ditetapkan 70 siswa sebagai sampel yakni kelas VIII-5 dan VIII-6. Metode penelitian ini adalah metode eksperimen. Tes yang digunakan adalah pretes dan posttes pada meteri pokok Lingkaran masing-masing 5 soal untuk mengukur kemampuan komunikasi menggunakan angket untuk mengukur kemampuan self efficacy. Pengumpulan data dilakukan melalui tes. Nilai gain diperoleh dari rumus Meltzer dengan hasil : untuk kelas eksperimen 1 nilai gain minimum = 0,32 dan maksimum = 0,96, sedangkan untuk kelas eksperimen 2 nilai gain minimum = 0,16 dan maksimum = 0,63. Dari hasil penelitian disimpulkan bahwa (1) terdapat perbedaan signifikan dalam peningkatan kemampuan komunikasi siswa antara yang pembelajarannya menggunakan model pembelajaran berbasis masalah dan contextual teaching and learning (CTL), (2) terdapat perbedaan signifikan dalam peningkatan kemampuan komunikasi dan sefl efficacy siswa pada level pengetahuan awal matematika tinggi, sedang dan rendah, (3) tidak terdapat interaksi antara pendekatan pembelajaran dan level pengetahuan awal matematika siswa terhadap peningkatan kemampuan komunikasi dan self efficacy.

Evacuation is characterized by rapid movement of people in unsafe locations or disaster sites to safer locations. The traffic management strategy for commonly used evacuations is the use of Shoulder-Lane, contraflowing traffic and gradual evacuation. Contra-flow has been commonly used in traffic management by changing traffic lanes during peak hours. To implement the contra-flow operation, there are two main problems that must be decided, namely Optimal Contraflow Lane Configuration Problem (OCLCP) and Optimal Contraflow Scheduling. Within the OCSP there are two approaches that can be used: zone scheduling and flow scheduling. Problem of Contra Flow and Zone Scheduling Problem is basically an Emergence Evacuation Route Planning (EERP) issue. This research will discuss EERP with ContraFlow and Zone Scheduling which can guarantee the movement of people in disaster area to safe area in emergency situation.

Community Service Activities carried out by the Service Team are intended to achieve the output target of reducing the spread of covid-19 in the people of Laut Dendang Village. The community service team conducts outreach regarding improving the quality of health and education in this village, in this case the Village Head, Hamlet Head, and PKK women become participants in this outreach, so that further all participants can socialize the issue of the importance of preventing Covid-19 and how to do it. prevention. It is hoped that the improvement of the quality of health in this village can be a lesson for the village community to understand and implement all knowledge or rules in reducing or stopping the spread of this deadly virus. This service is aimed at Community Service for the 2020 USU Lecturer Service Scheme is to solve problems together with the community through observation, problem identification, problem formulation, preparation of alternative solutions and evaluation of their implementation.

Data Mining is the process of finding certain patterns and knowledge from big data. In general, the data mining process can be grouped into two categories, namely descriptive data mining and data mining prediction. There are several Math functions that can be used in the data mining process, one of which is the Classification and Regression function. Regression Analysis is also called Prediction analysis, which is a statistical method that is widely used to investigate and model relationships between variables. Regression analysis to estimate the regression curve can be done by analyzing Nonparametric Regression. One well-known method in non-parametric regression is MARS (Multivariate Adaptive Regression Spline). The MARS method is used to overcome the weaknesses of the Linear Regression method. The use of a stepwise backward algorithm with the CQP quadratic programming framework (CQP) from MARS resulted in a new method called CMARS (Conic Multivariate Adaptive Regression Splines). The CMARS method is able to model high dimensional data with nonlinear structures. The flexible nature of the CMARS model can be used in the process of analyzing earthquake predictions, especially in Lombok, West Nusa Tenggara. Test results Obtained a mathematical model of four independent variables gives significant results to the dependent variable, namely Peak Ground Acceleration (PGA). Contributions of independent variables are the distance of the epicenter 100%, magnitude 31.1%, the temperature of the incident location 5.5% and a depth of 3.5%.

An increasing number of practical scenarios continue to experience problems associated with multistage stochastic programming. This study proposes a decomposition method through which they can be a successful solution to multi-stage stochastic nonlinear programs. The proposed method entails the scenario analysis method. The proposed method also performs its role via search direction generation in such a way that sets of quadratic programming sub-issues are solved in a parallel way, especially when the size is significant lower, compared to the case involving original problems at the respective iterations. Relative to the dual multiplier derivation, which focuses on non-anticipativity constraints, the proposed system advocates for the introduction of generalized reduced gradient approaches.

Nowadays, due to the rapid growth of air traffic and airspace congestion flight delays are becoming serious problems. This situation happens due to the demand capacity imbalances in air traffic management system. The deterioration of the expected service and operational costs would be the direct impact of this problem. Network-wide air traffic flow management can be regarded as an effective way to ease demand-capacity imbalances globally and would be able to reduce airspace congestion and flight delays. This paper proposes an integer programming model to solve the problem. The objective function consists of minimizing operational costs and risk costs.