scholarly journals Perbandingan Metode Moving Average dan Metode Naïve Dalam Peramalan Data Kemiskinan

2019 ◽  
Vol 3 (1) ◽  
pp. 65
Author(s):  
Ais Kumila ◽  
Baqiyatus Sholihah ◽  
Evizia Evizia ◽  
Nur Safitri ◽  
Safama Fitri
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Naive Method ◽  
Forecasting System ◽  
Simulation Results

Abstrak: Penelitian ini bertujuan untuk memprediksi metode terbaik pada sistem peramalan dengan menggunakan metode Moving Average (SMA, WMA, dan EMA) dan metode Naive. Pada tahap simulasi, kami menggunakan data jumlah kemiskinan penduduk Provinsi Nusa Tenggara Barat (NTB) tahun 2002-2018 untuk memprediksi data tahun 2019. Adapun, model dievaluasi untuk melihat tingkat akurasi masing-masing metode berdasarkan nilai MAD, MSE, RMSE dan MAPE. Berdasarkan hasil simulasi data dari metode-metode yang diuji, diketahui bahwa metode Naive paling akurat dengan hasil prediksi tahun 2019 sebesar 737.460 dengan MAD, MSE, RMSE dan MAPE berturut-turut sebesar 41.427,188; 2.711.468.146; 52.071,760; dan 0.043. Abstract: This study aims to predict the best method on the forecasting system using the Moving Average method (SMA, WMA, and EMA) and the Naive method. In the simulation phase, we used data on the Number of Poverty of the Population of West Nusa Tenggara (NTB) in 2002-2018 to predict the Poverty of Population of the Province of West Nusa Tenggara (NTB) in 2019. Meanwhile, the model was evaluated to see the accuracy of each method based on the value MAD, MSE, RMSE and MAPE. Based on the simulation results of the data from the methods tested, it can be seen that the Naive method is most accurate with the results of the 2019 prediction of 737,460 with MAD, MSE, RMSE and MAPE in the amount of 41.427,188; 2.711.468.146; 52.071,760; and 0.043.

scholarly journals Konstruksi Forecasting System Multi-Model untuk pemodelan matematika pada peramalan Indeks Pembangunan Manusia Provinsi Nusa Tenggara Barat

2018 ◽  
Vol 4 (2) ◽  
pp. 114 ◽  
Author(s):  
Lalu Sucipto ◽  
Syaharuddin Syaharuddin
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Graphical User Interface ◽  
Interpolation Method ◽  
Moving Average ◽  
Back Propagation ◽  
Naive Method ◽  
Forecasting System ◽  
Exponential Smoothing Method

Penelitian ini bertujuan untuk mengembangkan produk Forecasting System Multi-Model (FSM) guna menentukan metode terbaik dalam sistem peramalan (forecast) dengan mengkonstruksi beberapa metode dalam bentuk Graphical User Interface (GUI) Matlab dengan menghitung semua indikator tingkat akurasi guna menemukan model matematika terbaik dari data time series pada periode tertentu. Pada tahap simulasi, tim peneliti menggunakan data Indeks Pembangunan Manusia (IPM) Provinsi Nusa Tenggara Barat (NTB) tahun 2010-2017 guna memprediksi IPM NTB tahun 2018. Adapun metode yang diuji adalah Moving Average (SMA, WMA dan EMA), Exponential Smoothing Method (SES, Brown, Holt, dan Winter), Naive Method, Interpolation Method (Newton Gregory), dan Artificial Neural Network (Back Propagation). Kemudian model dievaluasi untuk melihat tingkat akurasi masing-masing metode berdasarkan nilai MAD, MSE, dan MAPE. Berdasarkan hasil simulasi data dari 10 metode yang diuji diketahui bahwa metode Holt paling akurat dengan hasil prediksi tahun 2018 sebesar 67,45  dengan MAD, MSE, dan MAPE berturut-turut sebesar 0,22654; 0,075955 dan 0,34829.   The purpose of this research is to develop a product was called Forecasting System Multi-Model (FSM) to determine the best method in the forecasting system by constructing several methods in the form of Graphical User Interface (GUI) Matlab. It was done by all indicator accuration to find the best mathematical model of time series data in a certain period. In the simulation phase, this research used the Human Development Index (HDI) data of West Nusa Tenggara (NTB) Province in 2010 - 2017 to predict the HDI data of NTB in 2018. The methods tested were Moving Average (SMA, WMA and EMA), Exponential Smoothing Method (SES, Brown, Holt, and Winter), Naive Method, Interpolation Method (Newton Gregory), and Artificial Neural Network (Back Propagation). Then the models/methods were evaluated to see the level of accuracy of each method based on the value of MAD, MSE, and MAPE. Based on data simulation result from 10 tested method known that Holt method is most accurate with prediction result of 2018 equal to 67,45 with MAD, MSE, and MAPE respectively equal to 0.22654, 0.075955 and 0.34829.

Penerapan Metode SMA Untuk Peramalan Tingkat Produksi Tanaman Pangan di Dinas Pertanian

2021 ◽  
Vol 1 (1) ◽  
pp. 27-32
Author(s):  
Novita Sari Sitorus ◽  
Yessica Siagian ◽  
Romy Aulia
Keyword(s):  
Production Rate ◽  
Average Method ◽  
Crop Production ◽  
Moving Average ◽  
Visual Basic ◽  
Food Crops ◽  
Food Crop ◽  
Forecasting System ◽  
Food Crop Production

Abstract: Food is the most important foodstuff needed by humans, in addition to consumption, the production of food crops can also be used as a source of business in the economy. This study aims to predict the production of food crops in the Agriculture Office of Asahan Regency. Based on data on food crop production from 2012 to 2019 at the Asahan District Agriculture Office experienced ups and downs, food crop production sometimes does not meet the consumption of people in Asahan Regency (devisit) and sometimes overproduction (surplus). So far, the Agriculture Office of Asahan Regency has predicted the production of food crops only with estimates so that the results are not accurate. Based on these problems, a proper forecasting system is needed so that it is able to predict the production rate of food crops quickly and accurately. Applications are built using Visual Basic Net 2010 and MySQL databases and use the Single Moving Average method. The results of this study can help the Agriculture Office of Asahan Regency in predicting the amount of food crop production in the next period according to the needs. Keywords : Forecasting, Production, Food Crops, Single Moving Average  Abstrak: Pangan merupakan bahan makanan paling utama yang di butuhkan oleh manusia, selain untuk dikomsumsi, hasil produksi dari tanamana pangan juga dapat dijadikan sebagai sumber usaha dalam perekonomian. Penelitian ini bertujuan untuk melakukan prediksi produksi tanaman pangan pada Dinas Pertanian Kabupaten Asahan. Berdasarkan data produksi tanaman pangan dari tahun 2012 sampai dengan tahun 2019 pada Dinas Pertanian Kabupaten Asahan mengalami naik turun, produksi tanaman pangan terkadang tidak memenuhi konsumsi masyarakat di Kabupaten Asahan (devisit) dan terkadang kelebihan produksi (surplus). Selama ini Dinas Pertanian Kabupaten Asahan melakukan prediksi produksi tanaman pangan hanya dengan perkiraaan-perkiraan saja sehingga hasilnya tidak akurat. Berdasarkan permasalahan tersebut maka dibutuhkan sistem peramalan yang tepat sehingga mampu meramalkan tingkat produksi tanaman pangan dengan cepat dan akurat. Aplikasi dibangun menggunakan Visual Basic Net 2010 dan database MySQL serta menggunakan metode Single Moving Average. Hasil dari penelitian ini dapat membantu Dinas Pertanian Kabupaten Asahan dalam memprediksi jumlah produksi tanaman pangan pada periode berikutnya sesuai dengan kebutuhan. Kata Kunci : Peramalan, Produksi, Tanaman Pangan, Single Moving Average

scholarly journals Sistem Informasi Persediaan Barang Dengan Metode Weight Moving Average Berbasis Android Di Toko Awd Mranggen

2021 ◽  
Vol 14 (2) ◽  
pp. 208-215
Author(s):  
Zaenal Mustofa Zaenal ◽  
Muhammad Sholikhan ◽  
Bachtiar Aziz Mulki
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Sales Data ◽  
The Future ◽  
Forecasting System ◽  
Period Data

The AWD Mranggen store is a store that is engaged in the sale of bags, belts, shoes with sales developments increasing from year to year, with fairly tight business competition, the AWD Mranggen store must be able to calculate the estimated number of items to be purchased based on previous sales data, the prediction is very influential on the decision to determine the number of items to be provided by the AWD Mranggen Store for the next sales period data. Inventory of goods that are not right cause some losses in terms of time and also costs, it is necessary to have a forecasting system. Forecasting is a technique to identify a model that can be used to predict conditions in the future. By using the weight moving average method, it can be seen that the error value is more than smaller than other methods and the estimated results can be more precise so that it can help owners make decisions in carrying out inventory.

An Improved Tracking Architecture for Weak GNSS Signal Based on Moving Average Method

2014 ◽  
Vol 602-605 ◽  
pp. 1571-1575
Author(s):  
Jun Liang Liu ◽  
Hua Bing Wu ◽  
Zai Min He ◽  
Yong Hui Hu
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Weak Signal ◽  
Robust Tracking ◽  
Tracking Loop ◽  
Loop Filter ◽  
Signal Tracking ◽  
Frequency Discriminator ◽  
Simulation Results ◽  
Stability Issues

The research of robust tracking GNSS signal has been focused on increasingly in hash environment. In order to tracking weak GNSS signal, what an efficient approach is increasing coherent time. However, increasing coherent time brings several problems. It causes coherent loss and loop stability issues. This paper proposed an improved tracking architecture based on moving average method, a complex loop filter for the architecture is not considered, but the moving average method before frequency discriminator for a frequency lock loop is introduced. Compared with typical tracking loop, the coherent time is different from loop update interval in the method. Increasing the coherent time reduces the output error of frequency discriminator, and the loop noise bandwidth is increased in shorter loop update interval. Hence, use of such type of scheme provides higher dynamics for weak signal tracking. The simulation results are shown to verify the performance of the methods.

scholarly journals Implementasi Sistem Peramalan Persediaan Barang Menggunakan Metode Moving Average

2020 ◽  
Vol 4 (3) ◽  
pp. 707
Author(s):  
Wulandari Wulandari
Keyword(s):  
Information System ◽  
Data Analysis ◽  
Data Collection ◽  
Average Method ◽  
Moving Average ◽  
Analysis Process ◽  
The Future ◽  
Forecasting System ◽  
System Requirements ◽  
A Company

PT. XYZ is a company engaged in the sale of drugs and vitamins for livestock, each period the number of requests for goods always changes. Problems faced by PT. XYZ, which is expired goods because in a certain period the amount of stock is piling up due to the small amount of demand that causes the company to lose. The purpose of this study is to forecast the inventory process in order to minimize the company's losses against the estimates made so far. In order to minimize these problems, the authors model the forecasting information system for procurement of goods using VB.Net and MYSQL and combine the Moving Average method. The method used is the data collection and processing, and continued with the data analysis process, to model the system requirements of the writer using UML. The final result of this study is that the value of accuracy reaches 88% so that the inventory forecasting system using the moving average method can help managers in making decisions to determine the process of inventory in the future

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>

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