This study aims to provide the right solution in overcoming the problem of selection for prominent class students senior high school at SMAN 2 Pematangsiantar. The solution is to make a decision support system for the selection of prominent class XI IPA students in determining rankings. The method used is the TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) algorithm by using several criteria, namely: Mid Test Score, Final Exam Score, Extracurricular, Personality, and Attitude. The results of student data processing using the decision support system obtained weights of 1, 0.57188272655161, and 0.56259472567445 for rank 1 to rank 3. The implementation of this Decision Support System is beneficial and makes it easier for the school and the homeroom teacher to determine ranking for the selection of prominent class students.
Control of population is one of the tasks of the government in Indonesia. The increase and movement of population in each region makes a certain area to defeat changes in population surging, and this can affect the economic level of the area. This study aims to process the population of Pematangsiantar City in 2018 which is divided into age groups, namely: Toddlers, Young Children, Early Adolescents, Late Adolescents, Early Adolescents, Late Adulthood, Early Adulthood, Elderly, Late Elderly, and Upper Seniors. Data processing is done by using K-Means method clustering in accordance with the population of Pematangsiantar City per district. With this grouping, we can see that the number of population in each sub-district is based on each age group so that we can implement programs that are more appropriate in improving human resources.
This study aims to overcome the problem of selecting the best customer at Pematangsiantar Subur Graphic Printing. For the smooth running of the printing business, Subur Graphics maintains good relations with consumers by giving rewards to the best customers. In determining the best customer for graphic fertile printing, it is still done manually. To help overcome the problem of selecting the best customer, a decision support system is designed. Web-based decision support system built using the Weighted Product method. This decision support system uses criteria consisting of total shopping, payment method, length of subscription, payment status and total visits. The result of this research is a web-based decision support system with an output consisting of recommendations for the three best customers, namely the first best alternative Pd_Pphn with a vector value of V 0.085, the second best alternative GPIB Maranatha with a vector value of V 0.080, and the third best alternative SPTI_Ib_Mrni with a vector value of V 0.077. With a decision support system for determining the best customer using the weighted product method, Subur Graphic Printing can easily select the best customer.
This research was conducted to solve the problem of decision making to determine the feasibility of giving loans to CUM Caritas HKBP Pematangsiantar. The applicant's assessment includes an administrative assessment and the survey is still being conducted without using a computer-based decision support system. To simplify the decision making to determine the feasibility of giving the loan, a decision support system was designed using the Topsis method (Technique for Order Preference by Similarity to Ideal Solution). The criteria used consist of: Income, Dependent, Loan Size, Guarantee Value and Number of Installments. The results of the calculation of the case of this study that are eligible for a loan in order are NA2 with a value of 0.6390, NA1 with a value of 0.5737, the third is NA3 with a value of 0.5053, and the last is NA4 with a value of 0.3102. The results of this study can be concluded that the Topsis method can answer or resolve problems faced by CUM Caritas HKBP Pematangsiantar in determining creditworthiness.
This study aims to overcome the problems in predicting student achievement at the Polytechnic Business Indonesia Pematangsiantar. To predict student achievement is done by applying Backpropagation algorithm and implement it into Matlab software. Backpropagation algorithm is one of the methods on artificial neural networks that is quite reliable in solving problems including prediction. In this study conducted on the object of students semester One with a lot of data samples 26 samples. The data sample is divided into two parts, 70% of the data is used as training data and 30% of the data is used as testing data. This study uses ten architectural models, namely 9-2-1, 9-3-1, 9-4-1, 9-5-1, 9-6-1, 9-7-1, 9-8-1, 9-9-1, 9-10-1, 9-11-1. Of the ten Backpropagation network architecture models implemented in predicting student achievement in Matlab software obtained the best output is 9-2-1 pattern with epoch 8149, time duration for 17 seconds, and MSE (error rate) value of 2.80 e-05 for training and MSE (error rate) of 0.1248 with accuracy of 87.5% for testing. The best architecture obtained is expected to be used as a picture by the academic Polytechnic Business Indonesia (PBI) in predicting student achievement.
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