Search

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.

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.

This study analyzes Sentiment to see opinions, points of view, judgments, attitudes, and emotions towards creatures and aspects expressed through texts. One of Social Media is like Twitter is one of the most widely used means of communication as a research topic. The main problem with sentiment analysis is voting and using the best feature options for maximum results. Either, the most widely known classification method is Naive Bayes. However, Naive Bayes is very sensitive to significant features. That way, in this test, a comparison of feature selection is carried out using Particle Swarm Optimization and Genetic Algorithm to improve the accuracy performance of the Naive Bayes algorithm. Analyses are performed by comparing before and after testing using feature selection. Validation uses a cross-validation technique, while the confusion matrix ??is appealed to measure accuracy. The results showed the highest increase for Naïve Bayes algorithm accuracy when using the feature selection of the Particle Swarm Optimization Algorithm from 60.26% to 77.50%, while the genetic algorithm from 60.26% to 70.71%. Therefore, the choice of the best characteristics is Particle Swarm Optimization which is superior with an increase in accuracy of 17.24%.

Clustering is one of the data analysis activities for grouping data into several categories with the same characteristics based on certain criteria. The problem that often arises in the clustering process is getting optimal clustering results. So far there is no fixed provision to regulate the number of clusters and the type of data that must be placed in each cluster and also there is no optimal size for data grouping. By using a combinatorial optimization approach, a model that is able to group data optimally was developed. The solution was presented as a decision in the form of 0 and 1. The cluster data model was linearized to obtain cluster optimization. To obtain accurate information from a group of data, the results of this study can be used as an alternative solution for cluster optimization problems.

This study analyzes the performance of the k-Nearest Neighbor method with the k-Fold Cross Validation algorithm as an evaluation model and the Analytic Hierarchy Process method as feature selection for the data classification process in order to obtain the best level of accuracy and machine learning model. The best test results are in fold-3, which is getting an accuracy rate of 95%. Evaluation of the k-Nearest Neighbor model with k-Fold Cross Validation can get a good machine learning model and the Analytic Hierarchy Process as a feature selection also gets optimal results and can reduce the performance of the k-Nearest Neighbor method because it only uses features that have been selected based on the level of importance for decision making.

The paper demonstrates epileptic seizure detection by analyzing the performance of k-nearest neighbor (KNN) classification technique. The writer applies this classifier on epileptic patient seizure datasets based on extracted feature from Discrete Wavelet Transform (DWT). The research indicates that DWT improved the KNN classifier with average 93.42% on the datasets and less chance of false detection rate for epileptic seizure datasets.

Data mining was the data processing technique to be obtained knowledge or important pattern of data. One of the popular methods was the KNN (K-Nearest Neighbor) which was computational simplicity. There was some weakness of KNN, vulnerable in the data high dimensionality. It was caused of data high dimensionality, so that space can be occupied in instance to be greater. The method approach would be proposed in the research was the method to be omitted several numbers of features were irrelevant to the KNN method by using similarity measures (Euclidean distance, Correlation distance and Cosine similarity). The testing was done by testing the different three datasets and computed the average of accurate results. The results of testing have successes to be omitted the data features without decreasing accuracy, that the accuracy average of feature selection using the KNN algorithm: KNN without selection was 88.804%, KNN-Euclidean distance was 89.120%, KNN-Correlation distance is 89.567% and KNN-Cosine similarity was 89.134%.

Research in the field of disaster management is done by utilizing information and communication technology. Where disaster management is discussed is about evacuation planning issues. The evacuation stage is a very crucial stage in the disaster evacuation process. There have been many methods and algorithms submitted for the evacuation planning process, but no one has directly addressed evacuation planning on dynamic issues concerning time-varying and volume-dependent. This research will use the Relaxation-Based Algorithm combined with the Route Choice Model to produce evacuation models that can be applied to dynamic issues related to time-varying and volume-dependent because some types of disaster will result in damage as time and evacuation paths are volume-dependent so as to adjust to the change in the number of people evacuated. Disaster data that will be used in this research is sourced from Disaster Information Management System sourced from DesInventar. The results of this study are expected to produce an evacuation planning model that can be applied to dynamic problems that take into account the time-varying and volume-dependent aspects.

Data mining is an analytical process of knowledge discovery in large and complex data sets. Many studies wish to explore data, to find information so that knowledge can be obtained through the grouping process, classification, rules discovery, associations and data mining visualization which shows similarity. Periodic data often occurs in business applications and sciences that has big size, high dimension and continuously updated. The similarity in periodic data is based on several approaches. One of common approaches is to transform periodic series into other domains so that dimensions are reduced, followed by index mechanism. Many studies of time series do not give optimal result because limited to extracting data not able to represent time series and its pattern which is then change into rules. Rules can be found in time series data, but they are still constrained by over fitting and difficult to present. It causes time series data and non linier function of data mining decision can’t be optimal. The basic idea in the method proposed is to do periodic discretization for sub-sequential formation. These sub-sequences are grouped through a measure of similarity. The simple rule-finding technique is applied to obtain hidden rules in the temporal pattern. The optimal time series data expected to generate the uncertainty trend, previously unknown and can be used to make decisions or forecasting in the future.

Disaster management can be grouped into three stages with several activities that can be carried out starting from pre-disaster, during emergency response and post-disaster. In the pre-disaster stage one of which is mitigation, which is a series of efforts to reduce disaster risk, both through physical development and awareness raising and capacity to face the threat of disaster. In mitigation one important factor is about evacuation. Evacuation locations in disaster transportation management are urgently needed for an effective evacuation process. Evacuation location determination is a benchmark in handling evacuation that can minimize the impact of losses from the disaster. To determine the evacuation location dynamically using game theory can solve the problem by providing several alternative locations by showing the dominant factor in the variables in the selection of the evacuation site. Game theory will use pure and mixed strategies to provide the most optimum choice of criteria. With this method will produce a decision support system to determine the optimal evacuation location or safe area in disaster mitigation

In order to transport customers’ request economically, such as, goods, meals etc it is frequently the delivery company depend on a model which is called vehicle routing problem (VRP). This model consists of a customer population with deterministic demands, and a central station which acts as the base of a set of vehicles. The purpose is to design the vehicle routes starting and terminating at the assigned central station, such that the customer demands is met, within a time windows This paper develops an optimization model for the management of periodic deliveries of a given commodity. Due to the geographic location of customers the company requires to have diverse type of vehicles with diverse capacity to serve the commodity for customers. This kind of problem is called Periodic Heterogeneous Vehicle Routing Problem (PHVRP). The purpose is to schedule periodically the deliveries according to feasible combinations of delivery days and to determine the scheduling of fleet and driver and routing policies of the vehicles. The aim is to minimize the total costs of all routes over the planning horizon. We propose a combined approach of reduced gradient and heuristic algorithm to solve the problem.

Security of data text file on a computer can be done by utilizing encryption and decryption techniques. One technique is encryption and decryption of data encryption system text file with cryptography. Cryptography is the science or art to randomize and secure the message to avoid the manipulation of data by performing encryption and decryption on the data of the text file. Cryptographic algorithm consists of several types of one example is the algorithm Rivest Shamir Adleman (RSA). The RSA algorithm is one of asymmetric cryptography technique ie riptografi modern technique that has two keys of the public key used for encryption and a private key techniques to make the process deskripsi. The purpose of this research is to design and implement RSA cryptography algorithm on text file data security. The results of this design is a dekstop based software. This research is expected to provide benefits to the users, individually as well for other organizations.

Undoubtedly that the huge business data could make data analysis becomes more complicated such that the decision-making process would be out of reach. This condition happens. In the fields of consumer buying behavior, A well-known method called sentiment analysis can help in extracting information about the up-to-date trends and is able to increase market value of product through improving its quality. One of the approaches in solving the sentiment analysis is feature selection technique. However, this technique contains a combinatorial behavior and the analysis of the huge data can experience uncertainty parameter. This paper describes a framework for solving the sentiment analysis based on feature selection approach using a stochastic combinatorial programming.

Production planning is the most important part of the production process in Tempe Babe. The material consideration in the production planning in this research is the optimizing of the amount of production in fulfilling the number of tempe demand, optimizing of the amount of profit Tempe obtained and the minimization of the amount of raw materials used for Tempe production. The implementation of the goal programming method can provide solutions for optimizing production planning on Tempe Babe, where the deviational variables in goal programming serve to demonstrate the possibility of negative and positive deviations from right side value of the destination function. The results of calculation analysis with goal programming, indicating the excess amount of product production will be added to the product that has the biggest advantage and the excess amount of production will be added to the product that has the number of requests Optimal profit from each product resulting in the amount of prediction of Tempe production. The results of this research provide the optimum solution for production planning on Tempe Babe every month. In addition, this study also resulted in a new mindset to find production planning models in the IoT environment as a continuation of this research.

The Integrated Population Decision Support Model in the village needs to be developed according to the rules of Law number 6 of 2014 with a decision information system model that can assist one in making accurate and targeted decisions in choosing a university. Many problems can be solved using a model, one of which is the Decision Making System, for the Determination of Human Resources in an area. There are several models that have been built including Analytical Hierarchy Process (AHP). which is the most widely used research method in solving multi-criteria problems, such as in the Determination of Human Resource Selection. This research model uses Analytical Hierarchy Process (AHP) in determining the University for a superior place of learning for human resources. In determining the University, there are several basic decision-making, including the place of the educational model, the model of expertise / skills taught, reputation, quality, teaching and learning process. The final results in this study are the priority results of university criteria, which are sorted from highest to lowest, so that prospective student users who wish to continue their higher education in choosing a regional university (rural) can easily make decisions by determining the university in the village which, and finally this model is almost 80% accurate.

Self-regulation is a process to turn and arrange thoughts, behavior and deep emotion of achieving goal, predict the outcome of student learning achievements with the criteria for assessing which cognitive and affective. This can be seen in the students who has not reached minimum criteria 100 % of the final test which received by students. Research now applied performance and looking for value k best with the nearest distance related the ability self-regulation students to learning achievements. Learning outcomes low is something that should not be left, because bad for the future students. Therefore need to followed up in improving self-regulation learning. So, did analysis by using the method k-nearest neighbor.

This paper study a new approximation model to solving stochastic data envelopment analysis (SDEA) problem. The proposed approach is based on problems that might occur in everyday life. This paper discusses the approach in determining the efficiency and super efficiency ratings of a decision making unit (DMU) in the DEA model with stochastic data. In determining efficiency, SDEA is first transformed into an equivalent deterministic DEA by changing its chance constraints in such a way that the SDEA problem can be solved easily. The author proposes an approach technique called a sample median approximation (SMA) to change the chance constraints so that it will be easy to get the optimal solution in determining the efficiency of DMUs. In the process, the data to be processed first is determined by the median average which will later be considered to represent the actual sample average. As a numerical example, the author resolves the vendor selection problem as presented by Wu and Olson (2006) in their paper. By taking the same parameter value (α = 0.2 and beta = 0.9), the efficiency score and super efficiency of the problem are obtained.

Verification and identification of a person using biometrics have been widely used as the retina of the eye, the face and voice. In this experiment, such as voice biometrics to identify a person who is used to the system presence. Voice recognition is done by pattern matching between training data and test data. In this study used methods Dynamic Time Warping (DTW), K-Nearest neigbors (KNN) and Fast Frequency Transform (FFT) for voice recognition. DTW is used as a method of pattern recognition, while KNN is used for sound classification. Before testing conducted prior extraction using FFT method. This study uses 100 votes out of 10 people with the amount of each 10 people. Presentations were used as training data by 70% and 30% of test data. Results obtained by dividing the recognized voice to the overall sound. From the results 83.33 % voice recognition.

Nowadays flight scheduling is very important for profitability, but it has a complex problem when compiled traditionally. This study develops an integrated model for scheduling passenger and cargo scheduling simultaneously aiming to maximize profits. Network flow techniques are used to build the model, includes a low flow, the flow of passengers and goods flow. The model is formulated using integer commodity network flow with the characteristics of NP-Hard. Because of the problems predicted a very large, this model is more difficult to solve than the problem of scheduling traditional airlines or cargo. This model is able to directly manage the relationship between passengers and cargo and is expected to be a useful planning tool to determine the appropriate fleet and schedule in the short-term operations

Combinatorial problems are problems that have a finite set of feasible solutions. Although in principle the solution to this problem can be obtained with complete enumeration, in complex problems it takes time that cannot be practically accepted, combinatorial analysis is a mathematical study of the arrangement, grouping, ordering, or selection of discrete objects, usually limited in number (finite in number) , many combinatorial optimization problems have been generated by research in computer design, computational theory, and by computer applications on numerical problems that require new methods, new approaches, and new mathematical insights, on combinatorial optimization, the function g (x) is defined as mapping g: {0,1} n → {0,1}, obtaining feasible regions Most methods for solving combinatorial optimization problems propose feasible regions, namely areas that are constrained by problem constraints after relaxation such as counts or binary variables, Field slices generally use the metaheuristic method proposed by para Other researchers, for example Genetic algorithms, simulated annealing, taboo search, plant propagation, methods do not use feasible regional concepts, but proposing the starting point for abstract completion is a brief summary of the paper to help readers quickly ascertain the research objectives and match the research needs.

The concept of intelligent health is part of the realization of a smart city. Most hospitals experience shortages in the availability of resources, such as the lack of truly professional and qualified personnel in the health care field, limitations on hospital facilities and infrastructure. Capacity planning at the hospital is done by ensuring a balance between quality given to health care provided and the costs provided. The measuring instrument used in hospital capacity planning is to calculate the availability of resources at the hospital. In general, the capacity model is intended to calculate the amount of resources needed to be able to serve patients’ needs quickly. The problem that is the main description of providing optimal health services based on the concept of “smart health” is how to provide services as early as possible with limited use of health resources. The main assumptions of modeling are only one type of health service can be met by each patient at a time, the number of medical staff is sufficient to fulfill each request, and the type of disease experienced by patients can be handled by available medical staff. The model produced in this study is expected to minimize travel costs, service costs and other costs arising from inaccuracies in providing services to patients.

Line Follower Robot is a wheeled robot that has two wheels on the right side and two on the left side that can follow the line automatically. The robot is designed to navigate and move automatically following a line made and this robot should be able to maintain its stability. This line follower robot uses fuzzy logic method with accelerometer and gyroscope. To gain a good noiceless accelerometer response, Kalman filter is used. Kalman filter can minimize the error of the box in the noice sensitive control and level of noise in sensor before entering the control system. The results shows that robots are succesfully balances then selves, though interruped.

The waterwheel is a component of the spinning water that energizes the rotating axis. Waterwheel is a means to convert water energy into mechanical energy in the form of torque on the shaft of the mill. One of the functions of water is to improve water quality. But the use of waterwheels manually is inefficient, because the mill works continuously even though the water quality is good. So it takes intelligent algorithm to be able to produce waterwheels that can benefit the local community for the needs of high protein foods. By adjusting some variables in determining water quality to improve the water quality of shrimp habitat, consisting of: water pH, water temperature, disoolved oxygen and salinity. And in uncertainty input from existing parameters can form a probability distribution that determines water quality and the movement of the mill. The sensor reading results into a reference microcontroller in sending PWM signals on the boost converter as DC motor drivers to run waterwheels slowly or quickly based on inputs. And with the Internet of Things (IoT) technology will make it easier to control, more practical, effective and efficient.

The development of information technology is very rapid and is supported by the development of storage media technology and its application to all fields that produce huge amounts of data stacks generated from various sources, therefore need new techniques in managing data stacks. Data mining has become very important as an object and research study at this time because there are many data stacks found in agencies. Data mining is an analytical process of knowledge discovery in large and complex data sets. In this study the technique used is to conduct time series data mining clusters, using proximity to manhattan city. The time series graph is carried out by the sliding window to produce an analysis of the window for each cluster result. Based on cluster results, an analysis of knowledge transformation is carried out into new knowledge obtained from data mining time series data.

Health services are an integral part of hospitals or health clinics. Maximum service can get if the availability of resources in the service center is very adequate, but the availability of resources cannot be ascertained that it will always be adequate, and excessive availability of resources can also result in waste. The problem that often occurs is the lack of optimal services provided to patients due to limited available resources. Various obstacles, such as services that are not permitted are repeated, uncertain service distances, and service time are optimal barriers to service. This study aims to solve the problem of optimizing health care services for patients in hospitals using a number of variables in the hospital environment such as available resources, namely doctors, nursing medical personnel, technicians, technical equipment. This study is subject to the aim of minimizing all costs incurred to perform services, namely travel time from places to provide health services to patients, medical staff costs to provide services of the type of service, and so on. These variables are explained in the form of mathematical models that are able to explain existing constraints and minimize costs and time when performing services. The modeling results were tested using Linear Ineraktive Discrete Optimizer (LINDO) programming to determine errors that might occur in the model. The test results provide information that the maximum value of the objective function is 88.00 at the 25th iteration step so that the new model is expected to optimize health services for hospital patients and existing health clinics.

The problem of evacuation is very important when a disaster occurs. Evacuation must have systematic planning and good calculation. The planning is related to determining the route and flow of the vehicle. The shortest route problem is a matter of how to travel from one point of source through the paths to the destination with the shortest distance. The shortest path is the minimum trajectory needed to reach a certain point. In addition to the trajectory that has passed the shortest route, it needs to be taken into account about the maximum flow of a network. The maximum flow problem aims to maximize the movement of a number of items or objects through certain paths from the point of source to the end point. In this paper, in the future a model of vehicle movement will be developed to evacuate victims with maximum flow and through the shortest evacuation route by taking into account the paths to be followed in order to implement an effective evacuation process.

A proper strategies in designing and optimizing supply chains can help enterprises to stay competitive in this volatile economy. Identifying various type of products that run through the supply chain and aligning several parts of the supply chain to these product types are considered to be effective strategies adapted by successful companies. In this paper we address an optimal design of a multi-echelon supply chain problem with various commodity that is of significance to the business enterprise, along with the associated inventory systems. By using the guaranteed service approach to model the multi-echelon inventory system, we develop an MINLP model to simultaneously optimize the transportation, inventory and network structure of a multi-echelon supply chain which involve multi commodity. The optimization model is then solved using a strategy of releasing nonbasic variables from their bounds, combined with the “active constraint” method.

Forest management planning is placed not only on managing timber production but also on maintaining forest ecosystem values, such as water preservation. Thus, the meaning of sustainable forest management has broadened from sustainable timber management to ecosystem-based multipurpose forest management. This paper discusses the need to preserve water by forest in such a way the water will flow to rivers to be used as a resource to generate hydroelectric power plant. The problem is then modeled as a multi-objective integer programs.

To improve patient care requires intelligent and better Health Care, where it collaboration between many people is needed and resources. The use of data together by each agency needs to be done, to avoid repetition when doing data entry. In smart hospitals patient data can be received in real-time at various offices in smart hospitals or even smart hospitals in villages and towns. Medical technicians, nurses, and doctors and patients can also have access to test data without losing time in physically transferring the same information from different hospitals to another hospital. Implementation of IPV6 as a tool for the numbering of regional region and region class of hospital services, where the type of hospital in Indonesia itself consists of 5 classes of hospitals. The results in this research are IPV6 can be applied to patients who provide location information, which is integrated with the patient’s social media or can be tracked using GPS, patients will be very helpful in utilizing better hospital service facilities, the patient data in a shared database can be assessed jointly by the authorized party, in this case the hospital or the doctor handling of the patient is referred to and can be done more optimally.

Open vehicle routing problems, as a variant of vehicle routing problem (VRP), define that the vehicles are not required to return to the depot after completing service. In this paper, we extend the problem to include process control due to the uncertainty of customer demands. Each customer has a demand and each customer must be serviced by a single vehicle and no vehicle may serve a set of customers whose total demand exceeds its capacity. Each vehicle route must start at the depot and end at the last customer it serves. The objective is to define the set of vehicle routes that minimizes the total costs. We solve the stochastic model using a strategy of releasing nonbasic variables from their bounds, combined with the “active constraint” method. This strategy is used to force the appropriate non-integer basic variables to move to their neighborhood integer points.

The nonlinear goal programming problem focused in this paper has a special structure characterized by a subset of variables restricted to assume discrete values, which are linear and separable from the continuous variables. The strategy of releasing nonbasic variables from their bounds, as used in linear goal program, combined with the reduced gradient method, has been developed. This strategy is used to force the appropriate non-integer basic variables within a priority level to move to their neighbourhood integer points.

A graph is a pair of two sets. A graph is a representation of a system that uses two basic elements of vertices and edges, a node represented by a circle and edge represented by a line connecting two nodes. Graph theory is widely used in every field of engineering. In this paper, an application description of the graph is presented to find the shortest path and circuit network. The purpose of this paper is to connect graphs with engineering and electrical subjects. But beforehand, some fundamental mistakes of thinking in understanding mathematics, also called as fallacies are also introduced.

In logistic system, a Vehicle Routing Problem with time windows (VRPTW) is defined as a problem to decide the optimal set of logistic routes used by homogeny vehicles to deliver customers' demand within scheduled service time. The objective is to minimize the total travel cost (related to the travel times or distances) and operational cost (related to the number of vehicles used). In this paper we address a variant of the VRPTW in which the time to deliver and the time departing from a customer are restricted. The problem also include service disruption. We use integer programming model to describe the problem. A feasible neighbourhood approach is proposed to solve the model.

Recently, knowledge, as a strategic resource, has become an essential driving forces for business success, in particular in competitive situation. Accordingly, the existence of knowledge management (KM) is to support the business firm to locate, select, organize, distribute, and transfer vital information within the firm. One of the method to increasing effectiveness of knowledge management is to optimize its processes. However, during the process in order to increase the firm performance some disruption may occur. This paper describes a stochastic programming model to optimize the knowledge management processes problem within a firm, considering uncertainty disruption. We adopt scenario generation approach for tackling the stochastic problem.

Decision problems of stochastic or probabilistic optimization arise when certain coefficient of an optimization model are not fixed or known but are instead, to some extent, stochastic(or random or probabilistic) quantities. This paper focused on multiobjective stochastic optimization. We propose a method for solving a multiobjective chance constraints integer programming problem based on interactive approach. We assume that there is randomness in the right-hand sides of the constraints only and that the random variables are normally distributed. Some examples are presented.

The quadratic assigment problem is a combinatorial problem of deciding the placement of facilities in specified locations in such a way as to minimize a nonconvex objective function expressed in terms of distance between location and flows between facilities. Due to the non-convexity nature of the problem, therefore to get a ‘good’ starting point is necessary in order to obtain a better optimal solution. In this paper we propose a meta heuristic strategy combined with feasible neighbourhood search to get ‘optimal’ solution. From computational experience in solving some backboard wiring problems, it turns out that the approach proposed is promising.

Backpropagation method is an artificial neural network method that is often used for prediction. However, the use of activation functions and training functions greatly affects the accuracy of a prediction. In this study will discuss the backpropagation method by applying the activation function of Sigmoid bipolar and linear to predict population growth in Simalungun regency, Indonesia. The purpose of this paper is to look at the level of population growth in the district so that the government has a benchmark in determining policies so that a surge in population growth can be minimized and that the government pays more attention to the level of welfare of its population. As for academics, this research can be used as input if you want to do a prediction or forecasting with different cases. The data used in this paper is population density data in Indonesia’s Simalungun district, which is sourced from the Simalungun regency statistics center of Indonesia. This study uses 5 architectural models, namely 3-5-1, 3-10-1, 3-5-10-1, 3-5-15-1 and 3-10-15-1. Of these 5 models, the best architectural model is 3-5-10-1 with an accuracy of 97% and an MSE value of 0.00034833. Minimum Error 0,001-0,01 and learning rate 0,01.

Decision Tree is a classification technique in data mining that aims to predict behaviour from database. This goal is supported by several algorithms, one of which is Iterative Dichotomiser 3 (ID3) that displays predictions in a tree structure. With the application of decision trees, warehouses or heaps of data can be processed so as to produce rules or decision trees as decision support in solving problems faced by agencies. In fact, the information or rules produced by decision trees are limited to rules using the logic of propositions. The challenge in making decisions on decision trees is how to determine algorithms with a high degree of accuracy from various algorithms in the decision tree and how to find support and confidence for each rule produced by the decision tree to add support value and confidence level of each rule produced. The resulting rule has weaknesses, namely the unavailability of support and confidence, all rules are considered equal in strength based on data before being processed, found records that vary or different amounts of data. By making support and confidence, it will be easier to make decisions based on the results obtained.

This study describes the Fuzzy Logic Control for the movement of Solar Cells that use fuzzy logic algorithms to achieve intelligent and flexible knowledge-based systems in the design of hardware to achieve efficiency in controlling the movement of solar cells that are better and maximize the function of the solar cell motor works based on time input. Fuzzy Logic allows mapping from the input given based on time to an output. involves components of variables, membership functions, Fuzzy Logic operators and if-then rules. And with an analysis it can produce optimum output of Solar cell rotation.

Forecasting is one of the important topics in the data mining field, such as, predictions, weather forecasting, predictions of academic achievement. Another topic associated with forecasting through a series of data that depends on the time period is called time series. The problem in data mining time series is how to present the data. A common approach is to transform periodic series into other domains so that the reduced dimensions are followed by an index mechanism. However, research on time series has not been successful optimal yet, because it is still limited to mining data yet to represent time series, this pattern needs to be developed to change the pattern into a rule. The main problem that needs to be addressed in a periodic series is to present the results of visualization which includes more than thousands of observations are very difficult in order to present time series data in multidimensional to be mined. Working with high-dimensional data will be very expensive in terms of process and storage costs, because it requires high-level data representation or abstraction. The method used is the cluster window. The results obtained are the discovery of patterns based on the frequency of symbol behavior

Integrity is important aspect on the process of exchange data, because the originality of the data sent must be kept confidentiality from the irresponsible attacker. One of the algorithm on cryptography is Vernam Cipher which main focus on XOR operation for encryption and decryption process. The key length and the complexity of algorithm for key determination are very influential to avoid attacks. This aim of study is to modify Vernam Cipher with addition 2’s complement operation on encryption, decryption and key optimizing. Modified Vernam cipher generate random key by genetic approach to improve level of security and complexity of the algorithm.

The study aims to analyze the selection of exemplary teachers using C4.5 algorithm, which is one of the existing decision tree methods in data mining theory. Teacher data was obtained from the school in SMA Negeri 2 Pematangsiantar (2010-2016). The data used contains information about teacher history and teacher assessment data. This research uses interview technique and questionnaire in obtaining data. There are 11 attributes used in the assessment process: NUPTK, name, age, education, status, Appointment Letter, competence, award, work, teaching load, personality, Position, and label. The system has been tested using the Rapidminer application with 16 data samples. Where the rules are obtained as many as 24 rules. The level of accuracy of the system states that four of the ten attributes have a very significant correlation to the model of relationship rules in determining the proposed model teacher, such as (Position, competence, education, and personality). The four attributes (Position, competence, education, and personality) contribute 82.8% to the model of the rule of connectedness rules in determining the best teacher.

Implementation of Neuron Network model using Perceptron has not given optimal result in real time learning. The large number of inputs expressed in matrix form makes the process slower in pattern recognition. So, it takes characteristic to represent all the input matrices by using the partition method. By partitioning each input and with the best weight and using the activation function will produce an output value. And learning is valid in recognizing patterns only 1 iteration only. Further validation is done on the water mill control module with dissolved oxygen input, water pH, salinity and water temperature. With Perceptron Partition learning algorithm more real-time than perceptron model. Testing on the waterwheel input whether rotating or stopping by Matrix Laboratory software simulation.