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The problem faced by Muslims today in a constitutional context is a complex one. A revolutionary solution is needed to answer the problem and it is certainly not enough to use the classical Islamic legal methods. When viewed in the Islamic constitution, many of the problems that arise today are textually absent from the provisions of the law. Thus, to answer these problems requires a systematic methodology and legal formulation. In answering and formulating legal provisions in contemporary cases, it can be done by the qiyas method. This study aims to formulate the qiyas methodology as the fourth constitution in Islam to answer all problems of Muslims in the context of siyasah. Various things that are the object of research are in the fields of siyasah harbiyah, siyasah dusturiyah, and siyasah maliyah. The three siyasah in the national constitutional system are known as the state defense system, the state legislative system, and the state revenue and taxation system. To facilitate this research, the author utilizes the theory of literature research with a qualitative research approach. Researchers first formulate one problem and then collect various literature models related to the theme. Then the researcher analyzes each of these literatures to formulate qiyas formulations in the context of siyasah. The main problem in this study is how the formulation of qiyas in formulating Islamic law and how to implement qiyas in the issue of siyasah. Therefore, the results of this study can be described as showing that the formulation of qiyas must meet four pillars, namely the initial case, the initial case law, the branch case, and the 'illat law between the two cases. Then qiyas can be implemented in answering problems in the field of siyasah harbiyah, siyasah dusturiyah, and siyasah maliyah Keywords: Constitution, Qiyas, Siyasah

Vehicle routing problem (VRP) has a key role in logistics management. VRP plays a role in designing the optimal route used by a number of vehicles placed at the depot to serve a number of customers with known requests. To solve the VRP, the nearest neighbor algorithm used to get the most optimal results. The Nearest Neighbor algorithm is a heuristic method which is done by starting the starting point than looking for the nearest point. In this paper, algorithm nearest neighbour can save the distance of 13,14% and cost 13,17%.

One crucial step in detecting intra-cerebral hemorrhage automatically is to develop a midline setting that will divide the brain structure into 2 parts: the right hemisphere and the left hemisphere. This is useful for the process of detecting bleeding in the brain. By comparing the right hemisphere and the left hemisphere, bleeding detection can be done quickly and automatically on the application system. In this study, we proposed a method that was developed based on the pixel scan algorithm and the determination of the center of weight of the segmented part of the brain. The midline was set as the longest line possible drawn from edges of the brain skull. The result was then compared to manual reading by conducting user agreement surveys to radiology experts. The result shows that more than 75% of the cases experts strongly agree and agree with the midline setting automatically set by the algorithm, but 65% of them were dissatisfied and suggested improvement of the algorithm.

This paper provides a classical interpretation of mathematics from terms to categories such as arithmetic, geometry, algebra, and trigonometry. The background of interpretation is based on mathematics itself as reasoning, which contains systematic and structure. However, the mathematical term is recognized by them, but it comes with a variety of symbols and ways of interpretation. That interpretation is accumulated into universe of discourse which is a means of mathematics itself, and it states the mathematic.

According to the theft report received by Indonesian National Police in 2014, 9112 motorcycles were stolen, the number of motorcycle thefts decreased by 43.87% in 2015. And in 2016, motorcycle theft again increased by 1.23% from the previous year. Due to this problem, an alternative solution is needed to improve motorcycle security. One way to improve the security of the motorcycle is to modernize the motorcycle security system from conventional way to high-tech by utilizing the development of information technology (IT). In this study, we explored ATMega328 microcontroller on Arduino Promini board which is controlled using a smartphone with Android platform to build a motorcycle security device. Controls made through the smartphone to activate or to disconnect the electric motorcycle circuit through the relay. We conducted tests to measure the effectiveness and the time required by the device to be active based on different distances. The results of this test indicate that the device took a longer time if it is turned on from a longer distance. As for turning on or off a motorcycle, the device took 0.5 seconds at a minimum distance of 1 meter and took 1.5 seconds at a maximum distance of 15 (fifteen) meters. It is also found that to turn on a motorcycle engine, it took 0.5 seconds at a minimum distance of 1 (one) meter and 3 seconds at a maximum distance of 15 (fifteen) meters, and it took 0.5 seconds at a minimum distance of 1 (one) meter and 4 seconds at a maximum distance of 15 (fifteen) meters. The results obtained shows that the proposed method can be potential motorcycle security as an alternative solution.

The main concern of this paper is to decide the locations and allocations of facilities that impact not only the profitability of an organization but the fast ability to serve customers. Generally the location-routing problem is to minimize the overall cost by simultaneously selecting a subset of candidate facilities and constructing a set of delivery routes that satisfy some restrictions. In this paper we consider the periodic routing of vehicle to be used to distribute goods. Integer programming model is built to solve the problem. A feasible neighbourhood search is developed to solve the result model.

This research is about smooth support vector machine (SSVM) and Decision Tree in data mining. Many researchers conduct and develop methods to improve the accuracy and classification of data on good results. This research was conducted by conducting an experiment on STMIK Neumann Medan student data. In this study, it was concluded that Decision Tree performance is better than SSVM, Decision Tree gets very good results that promise to help find the best students to get scholarships. This study is better than SSVM. The training process has a difference of 11.04% and the testing process is 10.08% with each Accuracy.

Data Envelopmemt Analysis (DEA) serves to assess efficiency in the use of resources (input) to achieve results (output) whose purpose is to maximize efficiency. DEA was first introduced by Charnes, Cooper dan Rhodes in 1978. DEA is a nonparametric approach which is basically the development of Linear Programming (LP). The characteristic of DEA CCR model is that it allows the DMU to measure the maximum efficiency value with the most favorable weight. The efficiency of the DMU obtained differs from the different sets of weight may not be comparable and rank on the same basis. In DEA model optional sets of input weight and output weight are usually assumed to represent the DMU that is considered the best to compare with all DMUs. This set of weights is usually different for each DMU. In this journal to determine the set of weights in the DEA is performed with a linear programs. After the linear program is determined, the value of the output weight and input weight is solved using the software POM-QM for Windows.

The progress and success of the company can be seen from the consumer’s response to production from all aspects of each company’s activities and is a key success factor. In this case describes the problem of supply chain optimization from the perspective of providing logistics including a mathematical model of cost optimization for supply chains in the form of MILP (Mixed Integer Linear). Production, transportation and distribution costs are chosen as optimization criteria. Time, volume, capacity and mode of transportation are also taken into account. The model was developed based on supply chain management theory to achieve the lowest cost, responsive and to achieve common goals. The results state that product inventories, loading patterns, modes of transportation, and minimum order quantities significantly affect the total logistics costs. Shorter production processing time, and produce optimal solutions. However, the right limits must be carefully defined to get a viable solution.

In this study using a comparative analysis of SMART and SAW methods. The results of the SAW method calculation analysis have a faster processing time with a time of 0.40 s. While the SMART method calculation analysis has a longer processing time difference of 0.47 s. In this study the authors suggest using the SMART method. Where the process of calculating the SMART method has more varied results, because the criteria value between one candidate and another candidate influences each other. While the calculation of the SAW method results are less varied, because the criteria value between one candidate and another candidate does not affect each other. Weighting is done without an algorithm. In this study the authors suggest using the SMART method because there is a final value of

Dyslexia disorders commonly occur in children. Dyslexia is a difficulty in recognizing letters and numbers. Helpful tools are needed to detect and treat dyslexic children so that early anticipation can be taken. So that later this disorder will no longer be suffered by the child when he is an adult. For this reason, information technology-based learning media was created in this study with the aim of helping therapy for children with dyslexia. This study utilizes the hidden Markov method for speech recognition and multisensory methods for learning media. In this study learning is divided into three levels, namely learning to recognize numbers, learning to recognize letters, and learning to understand words. Each level uses a pattern that is generally difficult to accept children with dyslexia disorders. During the testing process, level one and level three efficiency has reached 80%, while for level two it is still worth 60%.

Software testing is a significant activity of the Software Development Life Cycle (SDLC) that can overcome possibility of error in every single development step. Software testing is most often used technique for verifying and validating the quality of software. Software testing that concern with the internal mechanism of a systems and mainly focus on control flow or data flow a program is called White box testing. White box testing is classified into static and structural testing. Type of white box testing that is used in this paper is structural testing. Path coverage and code complexity is a sub testing within the scope of structural testing. Path coverage split a program into a number of distinct paths. In this paper, structural graph and data flow graph is used to ease the tester to find path in a program by mapping out the program and software testing metrics is used to describe the effectiveness and quality of that processes that produce the software product. This paper did manual testing for genetic algorithm program, such as mapping the genetic algorithm program into the control flow graph, create test cases to provide some possibility inputs that can be handled by program, execute test cases, analyze, and calculate the software manual test process metrics.

Hydroponic farming system is an agricultural system that uses direct water as a nutrient without using soil as a planting medium. This system allows smallholder farmers to have the opportunity to develop their crop production with less capital. In addition, hydroponic planting has also been widely adapted by individuals as a personal hobby. Application of technology has penetrated various fields including agricultural fields. One of the technologies that can be applied in a hydroponic farming system is the sensor. Sensors are devices that used to convert a physical quantity into a quantity of electricity so that it can be analyse with a certain electrical circuit. In this study, the technology to be applied is wireless sensor technology applied in human life to help get information quickly and accurately. Sensors to be used in this study are pH sensors, conductivity sensors, temperature sensors and humidity. In addition to sensors, the study also involved Arduino technology. Arduino is a microcontroller board that is used to interact with the environment based on programs that have been made. The final results of the application testing show that the system success to display diagram in real-time in an environment from Arduino board to database and web server.

Backpropagation is a good artificial neural network algorithm used to predict, one of which is to predict the rate of Consumer Price Index (CPI) based on the foodstuff sector. While conjugate gradient fletcher reeves is a suitable optimization method when juxtaposed with backpropagation method, because this method can shorten iteration without reducing the quality of training and testing result. Consumer Price Index (CPI) data that will be predicted to come from the Central Statistics Agency (BPS) Pematangsiantar. The results of this study will be expected to contribute to the government in making policies to improve economic growth. In this study, the data obtained will be processed by conducting training and testing with artificial neural network backpropagation by using parameter learning rate 0,01 and target error minimum that is 0.001-0,09. The training network is built with binary and bipolar sigmoid activation functions. After the results with backpropagation are obtained, it will then be optimized using the conjugate gradient fletcher reeves method by conducting the same training and testing based on 5 predefined network architectures. The result, the method used can increase the speed and accuracy result.

Back-propagation is one of the learning algorithms on artificial neural networks that have been widely used to solve various problems, such as pattern recognition, prediction and classification. The Back-propagation architecture will affect the outcome of learning processed. BFGS Quasi-Newton is one of the functions that can be used to change the weight of back-propagation. This research tested some back-propagation architectures using classical back-propagation and back-propagation with BFGS. There are 7 architectures that have been tested on glass dataset with various numbers of neurons, 6 architectures with 1 hidden layer and 1 architecture with 2 hidden layers. BP with BFGS improves the convergence of the learning process. The average improvement convergence is 98.34%. BP with BFGS is more optimal on architectures with smaller number of neurons with decreased epoch number is 94.37% with the increase of accuracy about 0.5%.