19 research outputs found
Peramalan Butuhan Hidup Minimum Menggunakan Automatic Clustering dan Fuzzy Logical Relationship
Get PDFKebutuhan hidup minimum (KHM) adalah standar kebutuhan seorang pekerja atau lanjang untuk dapat hidup layak secara fisik untuk kebutuhan satu bulan. Selain itu KHM berpengaruh terhadap upah minum provinsi dan kota. Oleh karena itu diperlukan suatu peramalan KHM untuk mengetahui nilai KHM di tahun yang akan datang. Peramalan ini bermanfaat untuk perusahaan dalam merencanakan keuangan perusahaan tahun depan. Dalam melakukan peramalan KHM menggunakan metode automatic clustering dan fuzzy logical relationship. Automatic clustering digunakan untuk membentuk sub-interval dari data time series yang ada. Sedangkan fuzzy logical relationship digunakan untuk melakukan peramalan KHM berdasarkan relasi fuzzy yang telah dikelompokan. Automatic clustering dapat menghasilkan cluster-cluster yang sangat baik sehingga dalam melakukan peramalan dalam fuzzy logical relationship memberikan akurasi yang tinggi. Dalam menghitung kesalahan menggunakan mean squere error (MSE), nilai kesalahan semakin berkurang ketika diterapkan automatic clustering dalam fuzzy logical relationship. Hasil peramalan memiliki nilai koefisien korelasi yang hampir mendekati satu
Optimization of Dempster-Shafer's Believe Value Using Genetic Algorithm fo Identification of Plant Diseases Jatropha Curcas
Get PDFJatropha curcas is a plant that can be used as a substitute for diesel fuel. Lack of knowledge of farmers and the limited number of experts and extension agents to deal with the disease of the plant will result lower quality of Jatropha curcas. Dempster-Shafer method can be a solution for decision making based on previous research. The difference in beliefs of every expert in seeing Jatropha diseases may reduce the accuracy of the method. A set of numerical experiment prove that optimization of belief values using genetic algorithms can improve the accuracy Dempster-Shafer
Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows
Get PDFVehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1
K-Means Clustering and Genetic Algorithm to Solve Vehicle Routing Problem with Time Windows Problem
Get PDFDistribution is an important aspect of industrial activity to serve customers on time with minimal operational cost. Therefore, it is necessary to design a quick and accurate distribution route. One of them can be design travel distribution route using the k-means method and genetic algorithms. This research will combine k-means method and genetic algorithm to solve VRPTW problem. K-means can do clustering properly and genetic algorithms can optimize the route. The proposed genetic algorithm employs initialize chromosome from the result of k-means and using replacement method of selection. Based on the comparison between genetic algorithm and hybrid k-means genetic algorithm proves that k-means genetic algorithm is a suitable combination method with relative low computation time, are the comparison between 2700 and 3900 seconds
Belief Value Development In Dempster-Shafer With Particle Swarm Optimization(PSO) For Determining Of The Provision On Cases Of Persecution
Get PDFThe problem of uncertainty often becomes an obstacle, especially in terms of diagnosing the provision in this study. There is an algorithm that can solve this problem is Dempster-shafer (D-S), this algorithm has belief value that serves to determine the influence between symptoms. Belief value is obtained from experts, when there are new symptoms must have to ask the experts to know the value of belief, and takes time as well. So the Particle Swarm Optimization (PSO) algorithm will help generate and optimize the value of belief in D-S. PSO is able to produce optimal belief value. This research is to generate value belief D-S with PSO
Improve Interval Optimization of FLR using Auto-speed Acceleration Algorithm
Get PDFInflation is a benchmark of a country's economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimization with hybrid automatic clustering and particle swarm optimization (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results
Belief Value Development In Dempster-Shafer With Particle Swarm Optimization(PSO) For Determining Of The Provision On Cases Of Persecution
No full textThe problem of uncertainty often becomes an obstacle, especially in terms of diagnosing the provision in this study. There is an algorithm that can solve this problem is Dempster-shafer (D-S), this algorithm has belief value that serves to determine the influence between symptoms. Belief value is obtained from experts, when there are new symptoms must have to ask the experts to know the value of belief, and takes time as well. So the Particle Swarm Optimization (PSO) algorithm will help generate and optimize the value of belief in D-S. PSO is able to produce optimal belief value. This research is to generate value belief D-S with PSO.</jats:p
Optimization of FIS Tsukamoto using particle swarm optimization for dental disease identification
No full textA Novel Forecasting Based on Automatic-optimized Fuzzy Time Series
Get PDFIn this paper, we propose a new method for forecasting based on automatic-optimized fuzzy time series to forecast Indonesia Inflation Rate (IIR). First, we propose the forecasting model of two-factor high-order fuzzy-trend logical relationships groups (THFLGs) for predicting the IIR. Second, we propose the interval optimization using automatic clustering and particle swarm optimization (ACPSO) to optimize the interval of main factor IIR and secondary factor SF, where SF = {Customer Price Index (CPI), the Bank of Indonesia (BI) Rate, Rupiah Indonesia /US Dollar (IDR/USD) Exchange rate, Money Supply}. The proposed method gets lower root mean square error (RMSE) than previous methods
