scholarly journals Simulasi Monte Carlo dalam Mengidentifikasi Peningkatan Penjualan Tanaman Mawar (Studi Kasus di Toko Bunga 5 Bersaudara Kota Solok)

2020 ◽  
Author(s):  
Dian Cyntia Dewi ◽  
S Sumijan ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distribution ◽  
Monte Carlo Simulations ◽  
Random Numbers ◽  
Cumulative Frequency ◽  
Simulation Process ◽  
The Monte Carlo Method ◽  
Simulation Results

Roses are one of the most popular types of plants in the community. The sale of roses at the flower shop of 5 siblings is increasingly in demand. Identifying the increase in sales is important in analyzing sales progress. At the present time the seller can only see a manual increase in sales that are most in demand. This study aims to determine predictions of the increase in sales of rose flowers with a monte carlo simulation accurately and accurately. The data that will be processed in this study in the last 2 years, namely 2018 and 2019, rose plants obtained at the 5 Brothers Flower Shop in Solok City. There are several types of roses in the predicted sales level. Then the data will be converted into the probability distribution into cumulative frequency and followed by generating random numbers so that they can determine random numbers. Next, we will group the boundary intervals of the random numbers that have been obtained and continue with the simulation process so that the simulation results and percentage accuracy are obtained using the Monte Carlo method. The results of this study on data processing from 2019 to 2020 have an accuracy of 90%. So this research is very appropriate in identifying the increase in sales for the following year. The design of this system determines the amount of increased sales of goods using the monte carlo method in a flower shop of 5 siblings. Monte Carlo simulations can be used to identify specific sales increases. The results obtained are quite accurate using the Monte Carlo method.

scholarly journals Prediksi Tingkat Penerimaan Lulusan Siswa Kejuruan dalam Dunia Usaha dan Industri Menggunakan Metode Monte Carlo

2020 ◽  
pp. 84-89
Author(s):  
Hasanatul Iftitah ◽  
Y Yuhandri
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distribution ◽  
Prediction Accuracy ◽  
Vocational School ◽  
Vocational Schools ◽  
Random Numbers ◽  
Vocational High School ◽  
The Monte Carlo Method ◽  
Academic Year

Vocational High School (SMK) Negeri 4 Kota Jambi is one of the favorite vocational schools in Jambi City which is also the only pure tourism vocational school in Jambi Province. SMK Negeri 4 Kota Jambi has several vocational majors, namely culinary, beauty, fashion and hospitality. In general, students who choose to attend vocational schools have the hope of being able to work immediately after graduating from school, they do not need to continue to study to be able to work. In this study, researchers will predict the level of acceptance of students from SMK Negeri 4 Kota Jambi in the business and industrial world using the Monte Carlo method. Monte Carlo is a method that can find values ​​that are close to the actual value of events that will occur based on the distribution of sampling data. The technique of this method is to select random numbers from the probability distribution to perform the simulation. The data used in this study is the data of students from SMK Negeri 4 Kota Jambi who worked from the 2015/2016 Academic Year to the 2018/2019 Academic Year. Furthermore, the data will be processed using the Monte Carlo method. The simulation will be implemented using PHP programming. The result of this research is the level of prediction accuracy of students of SMK Negeri 4 Kota Jambi who are accepted in the business and industrial world using the Monte Carlo method is 84%.

scholarly journals Prediksi Tingkat Penerimaan Lulusan Siswa Kejuruan dalam Dunia Usaha dan Industri Menggunakan Metode Monte Carlo

2020 ◽  
pp. 84-89
Author(s):  
Hasanatul Iftitah ◽  
Yuhandri Yunus
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distribution ◽  
Prediction Accuracy ◽  
Vocational School ◽  
Vocational Schools ◽  
Random Numbers ◽  
Vocational High School ◽  
The Monte Carlo Method ◽  
Academic Year

Vocational High School (SMK) Negeri 4 Kota Jambi is one of the favorite vocational schools in Jambi City which is also the only pure tourism vocational school in Jambi Province. SMK Negeri 4 Kota Jambi has several vocational majors, namely culinary, beauty, fashion and hospitality. In general, students who choose to attend vocational schools have the hope of being able to work immediately after graduating from school, they do not need to continue to study to be able to work. In this study, researchers will predict the level of acceptance of students from SMK Negeri 4 Kota Jambi in the business and industrial world using the Monte Carlo method. Monte Carlo is a method that can find values ​​that are close to the actual value of events that will occur based on the distribution of sampling data. The technique of this method is to select random numbers from the probability distribution to perform the simulation. The data used in this study is the data of students from SMK Negeri 4 Kota Jambi who worked from the 2015/2016 Academic Year to the 2018/2019 Academic Year. Furthermore, the data will be processed using the Monte Carlo method. The simulation will be implemented using PHP programming. The result of this research is the level of prediction accuracy of students of SMK Negeri 4 Kota Jambi who are accepted in the business and industrial world using the Monte Carlo method is 84%.

scholarly journals Technical-Economic Analysis of Grapple Saw

2020 ◽  
Vol 41 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Ricardo Hideaki Miyajima ◽  
Paulo Torres Fenner ◽  
Gislaine Cristina Batistela ◽  
Danilo Simões
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distributions ◽  
Simulation Method ◽  
Forest Harvesting ◽  
Production Costs ◽  
Wood Harvesting ◽  
Food And Agriculture ◽  
The Monte Carlo Method

The processing of Eucalyptus logs is a stage that follows the full tree system in mechanized forest harvesting, commonly performed by grapple saw. Therefore, this activity presents some associated uncertainties, especially regarding technical and silvicultural factors that can affect productivity and production costs. To get around this problem, Monte Carlo simulation can be applied, or rather a technique that allows to measure the probabilities of values from factors that are under conditions of uncertainties, to which probability distributions are attributed. The objective of this study was to apply the Monte Carlo method for determining the probabilistic technical-economical coefficients of log processing using two different grapple saw models. Field data were obtained from an area of forest planted with Eucalyptus, located in the State of São Paulo, Brazil. For the technical analysis, the time study protocol was applied by the method of continuous reading of the operational cycle elements, which resulted in production. As for the estimated cost of programmed hour, the applied methods were recommended by the Food and Agriculture Organization of the United Nations. The incorporation of the uncertainties was carried out by applying the Monte Carlo simulation method, by which 100,000 random values were generated. The results showed that the crane empty movement is the operational element that most impacts the total time for processing the logs; the variables that most influence the productivity are specific to each grapple saw model; the difference of USD 0.04 m3 in production costs was observed between processors with gripping area of 0.58 m2 and 0.85 m2. The Monte Carlo method proved to be an applicable tool for mechanized wood harvesting for presenting a range of probability of occurrences for the operational elements and for the production cost.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

scholarly journals C-beta energy converter efficiency modeling

2019 ◽  
Vol 222 ◽  
pp. 02012
Author(s):  
Oleg Kuznetsov ◽  
Viktor Chepurnov ◽  
Albina Gurskaya ◽  
Mikhail Dolgopolov ◽  
Sali Radzhapov
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Theoretical Models ◽  
Maximum Efficiency ◽  
Equivalent Circuits ◽  
Optimal Parameters ◽  
Energy Converter ◽  
Electron Hole ◽  
The Monte Carlo Method

To construct beta converters with maximum efficiency it is necessary to carry out the theoretical calculation in order to determine their optimal parameters - the geometry of the structure, the thickness of the deposition of the radioisotope layer, the depth and the width of the p-n junction, and others. To date, many different theoretical models and calculations methods had been proposed. There are fairly simple theoretical models based on the Bethe-Bloch formula and the calculation of the rate of generation of electron-hole pairs, and on calculations by equivalent circuits. Also, the Monte-Carlo method is used for theoretical modeling of beta converters. This paper explores beta converter optimization using the Monte-Carlo method. The purpose of the study is to conduct Monte-Carlo simulation of the beta converter to determine its optimal parameters.

Spreading Characteristics of Molecularly Thin Lubricant on Surfaces With Groove-Shaped Textures: Effects of Molecular Weight and End-Group Functionality

2003 ◽  
Vol 125 (2) ◽  
pp. 350-357 ◽  
Author(s):  
Hedong Zhang ◽  
Yasunaga Mitsuya ◽  
Maiko Yamada
Keyword(s):  
Molecular Weight ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Ising Model ◽  
Monte Carlo Simulations ◽  
Simulation Method ◽  
Solid Surfaces ◽  
The Monte Carlo Method ◽  
End Groups ◽  
End Group

Effects of molecular weight and end-group functionality on spreading of molecularly thin perfluoropolyether (PFPE) film over solid surfaces with groove-shaped textures have been studied by experiments and Monte Carlo simulations. In the experiments, lubricant spreading on a surface with groove-shaped textures was measured by making use of the phenomenon in which diffracted light weakens in the lubricant-covered region. It is found that grooves serve to accelerate spreading and this effect increases for deeper grooves, and also the accelerating rate becomes larger for a lubricant having a larger molecular weight or functional end-groups. In the simulations, the Monte Carlo method based on the Ising model was extended to enable us to evaluate the effect of molecular weight on the spreading of non-functional lubricant inside a groove. The validity of the newly developed simulation method was well confirmed from the agreement between the simulation and experimental results.

scholarly journals Analysis of the Accuracy of the Calculation of the Diaphragm Area by the Monte Carlo Method

2020 ◽  
pp. 22-26
Author(s):  
O. D. Kupko
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Monte Carlo Method ◽  
Probability Distribution ◽  
Circular Aperture ◽  
The Monte Carlo Method ◽  
Relative Standard ◽  
Uniform Probability Distribution ◽  
Standard Deviations ◽  
The Difference

The process of measuring the area of a circular diaphragm using a device that determines the coordinates of the boundary of the diaphragm is theoretically considered. The Monte Carlo method with a small number of implementations was used. The procedure for calculating the area is described in detail. We considered a circular aperture with a precisely known radius. On the circumference of the diaphragm, the coordinate measuring points vibrated through 0.1, 0.3, 0.6, and π/2 radians vibrated. To simulate random deviations (uncertainties) when measuring coordinates, random additives were used with a uniform probability distribution and a given standard deviation. For each case, the areas were calculated in accordance with the proposed procedure. The difference in the results of calculating the area from the true area depending on the number of measurement points and the standard deviation of random additives is analyzed. It is shown that the ratio of the relative standard deviations of the area to the relative standard deviations of the coordinates is approximately the same for each number of measurements. The dependence of this relationship on the number of measurements is determined. The results obtained are analyzed.

scholarly journals Simulasi Monte Carlo dalam Memprediksi Penerimaan Peserta Pelatihan Dasar CPNS

2021 ◽  
pp. 134-138
Author(s):  
Faisal Roza ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Civil Servant ◽  
Random Numbers ◽  
Basic Training ◽  
Internal Affairs

The implementation of basic training recruit (latsar) of civil servant (CPNS) at Pusat Pengembangan Sumber Daya Manusia (PPSDM) Ministry of Internal Affairs regional Bukittinggi. The leader takes decision in doing the implementation of latsar CPNS recruit in PPSDM scope regional Bukittinggi. Latsar CPNS is one of requirements to be civil servant. Therefore, it is necessary to collect data by doing observation, interview questionings with related party in the implementation of latsar CPNS recruit from 2018 to 2020. It can be predicted for the next recruit. After doing library references by reading some books and journals, the basic training recruit of CPNS sources from PPSDM regional Bukittinggi, and Monte Carlo simulation. By using Monte Carlo simulation in predicting data, it can get closer value of actual value. Based on distribution of sampling data, the method is by choosing random numbers from probability distribution to do simulation. The Monte Carlo result’s examination has got 173 participants for year 2019, 158 participants for year 2020, and 157 participants for year 2021 clearly. Although the rate of the accurate just reaches 81%, but it has been able to be recommended to help PPSDM regional Bukittinggi, Ministry of Internal Affairs in taking decision and planning for basic training recruit of CPNS for the next.

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