Prediksi Tingkat Kriminalitas Menggunakan Metode Single Moving Average (Studi Kasus Polres Asahan Sumatera Utara)

2021 ◽  
pp. 181-186
Author(s):  
Mustopa Husein Lubis ◽  
S Sumijan
Keyword(s):  
Sexual Intercourse ◽  
Crime Rate ◽  
Average Method ◽  
Moving Average ◽  
Prediction System ◽  
Rate Data ◽  
Criminal Cases ◽  
Security And Protection ◽  
Know How ◽  
Forecasting Method

Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and endanger their lives and minds. The research location is the Asahan Police which is an agency that can provide security and protection for the community, especially those in Asahan Regency. The problem that occurs in this location is that there is no prediction system in Asahan Regency, due to the lack of knowledge factor in processing crime rate data. So it is difficult to know how much increase or decrease in criminal cases committed at the Asahan Police. The data used is data on cases of murder, sexual monitoring, tracing, violent theft, weight theft, motorcycle theft, fraud and counterfeiting money for the last 5 years in 2016 as many as 2020. Based on these problems, create a system for the crime rate in the Regency. Asahan to anticipate future crimes. The system that will be made uses forecasting or forecasting. With the Single Moving Average forecasting method. The Single Moving Average method is a forecast for the time in the future. The results of the calculation of criminal acts in 2021 found that there were 3 cases of murder, 2 cases of sexual intercourse in 2021, 252 cases of the 2021 return, 27 cases of violent theft in 2021, and 348 cases of bicycle theft in 2021. motorbikes in 2021 which open 90 cases, in 2021 which opens 85 cases, and money counterfeiting in 2021 which opens 1 case.

scholarly journals Prediksi Tingkat Kriminalitas Menggunakan Metode Single Moving Average (Studi Kasus Polres Asahan Sumatera Utara)

2021 ◽  
pp. 183-188
Author(s):  
Mustopa Husein Lubis ◽  
S Sumijan
Keyword(s):  
Sexual Harassment ◽  
Crime Rate ◽  
Average Method ◽  
Moving Average ◽  
Prediction System ◽  
Rate Data ◽  
Criminal Cases ◽  
Accuracy Rate ◽  
Know How ◽  
Forecasting Method

Crime is all kinds of actions and actions that are economically and psychologically harmful that violate the laws in force in the State of Indonesia as well as social and religious norms. Ordinary criminal acts affect the security of the community and threaten their inner and outer peace. The research location is the Asahan Police which is an agency that can provide security and protection for the community, especially those in Asahan Regency. The problem that occurs in this location is that there is no prediction system in Asahan Regency, due to the lack of knowledge factor in processing crime rate data. So it is difficult to know how much the increase or decrease in criminal cases carried out at the Asahan Police. The data used are cases of murder, sexual harassment, assault, violent theft, weight theft, motorcycle theft, fraud and counterfeiting money for the last 5 years from 2016 to 2020. This study aims to predict the crime rate in Asahan Regency in order to anticipating the upcoming spike in crime. The system that will be made uses forecasting or forecasting. With the Single Moving Average forecasting method. The Single Moving Average method is a forecast for the time in the future. The results of the calculation of predictions for criminal cases in 2021 obtained 3 cases of murder, 2 cases of sexual harassment in 2021, 252 cases of maltreatment in 2021, 27 cases of violent theft in 2021, 348 thefts with a weight in 2021. cases, motorcycle theft in 2021, totaling 90 cases, fraud in 2021, amounting to 85 cases, and counterfeiting money in 2021, totaling 1 case. This result has an accuracy rate of 99% from the reality of the crime that occurred, so this study is very appropriate to be used to predict the crime rate.

scholarly journals Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

2018 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
M. Tirtana Siregar ◽  
S. Pandiangan ◽  
Dian Anwar
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Production Capacity ◽  
Mean Absolute Percentage Error ◽  
Programming Method ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Forecasting Method ◽  
Planning Production

The objectives of this research is to determine the amount of production planning capacity sow talc products in the future utilizing previous data from january to december in year 2017. This researched considered three forecasting method, there are Weight Moving Average (WMA), Moving Average (MA), and Exponential Smoothing (ES). After calculating the methods, then measuring the error value using a control chart of 3 (three) of these methods. After find the best forecasting method, then do linear programming method to obtain the exact amount of production in further. Based on the data calculated, the method of Average Moving has a size of error value of Mean Absolute Percentage Error of 0.09 or 9%, Weight Moving Average has a size error of Mean Absolute Percentage Error of 0.09 or 9% and with Exponential Method Smoothing has an error value of Mean Absolute Percentage Error of 0.12 or 12%. Moving Average and Weight Moving Average have the same MAPE amount but Weight Moving Average has the smallest amount Mean Absolute Deviation compared to other method which is 262.497 kg. Based on the result, The Weight Moving Average method is the best method as reference for utilizing in demand forecasting next year, because it has the smallest error size and has a Tracking Signal  not exceed the maximum or minimum control limit is ≤ 4. Moreover, after obtained Weight Moving Average method is the best method, then is determine value of planning production capacity in next year using linier programming method. Based on the linier programming calculation, the maximum amount of production in next year by considering the forecasting of raw materials, production volume, material composition, and production time obtained in one (1) working day is 11,217,379 pcs / year, or 934,781 pcs / month of finished product. This paper recommends the company to evaluate the demand forecasting in order to achieve higher business growth.

Menghitung Penjualan Sepeda Motor Domestic Tahun 2019 Menggunakan Metode Simple Moving Average

2020 ◽  
Author(s):  
Rendy Alfianto ◽  
Indah Mustika Sary ◽  
Ade Azhari
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Knowing How ◽  
Know How ◽  
Margin Of Error ◽  
The Future

Knowing how many products are sold in a few months or years is helpful to know if the products sold by the company are experiencing a downturn or decline. In helping to know how many products will be sold in the future can use the Moving Average method, here we want to forecast motorcycle sales with moving average method and get different results with what has been predicted. These different results are commonly referred to as Margin of Error.

scholarly journals Analisis Penjualan Baju Seragam Sekolah di Konveksi Hanifah Collection

2017 ◽  
Vol 3 (2) ◽  
pp. 61
Author(s):  
Ahmad Fazarudin ◽  
Ahmad Nalhadi ◽  
Gerry Anugrah Dwiputra
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Exponential Smoothing ◽  
School Uniforms ◽  
A Value ◽  
Forecasting Method ◽  
A Company

Hanifah Collection is a company engaged in the convection of school uniforms. The fluctuating number of requests each month creates its problems in determining the amount of production. This study aims to find a method that matches the data pattern as the basis for determining the amount of output in the next period. The technique used in this study is the forecasting method of Moving Average, Exponential Smoothing and Triple Exponential Smoothing with parameter level errors of each way using MAD, MSE, and MAPE. From the results of this study, there is a moving average method with the most appropriate method in determining demand forecasting in the next period with a value of MAD of 172.22, MSE of 46624.34 and MAPE 46624.34.

scholarly journals Prediction system of carbon monoxide toxic gas monitoring using double moving average method

2019 ◽  
Vol 1373 ◽  
pp. 012001
Author(s):  
Eriansyah Hasibuan ◽  
H Harahap ◽  
S Hardi ◽  
S Suherman ◽  
F Fahmi
Keyword(s):  
Carbon Monoxide ◽  
Average Method ◽  
Moving Average ◽  
Prediction System ◽  
Gas Monitoring ◽  
Toxic Gas

scholarly journals Pemisahan Anomali Regional-Residual pada Metode Gravitasi Menggunakan Metode Moving Average, Polynomial dan Inversion

2016 ◽  
Vol 3 (01) ◽  
pp. 10 ◽  
Author(s):  
Jarot Purnomo ◽  
Sorja Koesuma ◽  
Mohtar Yunianto
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Continuation Method ◽  
Inversion Method ◽  
Polynomial Method ◽  
Minimum Error ◽  
Gravity Method ◽  
Upward Continuation ◽  
And Inversion ◽  
Separation Results

<span>It has been done a research about separation of regional-residual anomaly in Gravity method. <span>This research compares the result of three methods i.e. moving average method, polynomial <span>method, and inversion method. The computer program is created using a computer programming <span>Matlab 7. From three methods that have been made, the separation results are compared with<br /><span>results of separation by using Upward Continuation method. From the results of these <span>comparisons will be available an excellent program of regional-residual anomali separation. The <span>results show that in polynomial method of the order 4 obtained similar contour to the separation <span>by Upward Continuation Software. So that the output of this separation will be treated again <span>with Grav2DC software. The output of this software is the density of rock Grav2DC of the study<br /><span>area. Processing results obtained the minimum error of 1.85% for the separation by polynomial <span>method, while for the method of Upward Continuation obtained minimum error of 2.22%. The <span>results obtained show that the separation of regional-residual anomali by polynomial method is <span>similar to separation by Upward Continuation method.</span></span></span></span></span></span></span></span></span></span></span></span><br /></span>

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

Sign in / Sign up

Export Citation Format

Share Document