scholarly journals Peramalan Perencanaan Produksi Batu Tela Dengan Metode Exponential Smoothing (Studi Kasus Pada CV. Sinar Sowi Kabupaten Manokwari)

2019 ◽  
Vol 2 (2) ◽  
pp. 54-59
Author(s):  
Suwoko ◽  
Dirarini Sudarwadi ◽  
Nurwidianto
Keyword(s):  
Mean Square Error ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Forecasting Error ◽  
Alpha Models ◽  
The Mean ◽  
Exponential Smoothing Method

This study aims to find out how much forecasting the production of concrete brick at CV. Sinar Sowi. The data analysis method used is the Exponential Smoothing method by using forecasting error measurements namely Mean Square Error (MSE) and Mean Absolute Deviation (MAD). From the data that has been analyzed, the writer can conclude that the use of alpha model 0.1 Exponential Smoothing method, the value of the Exponential Smoothing method, the value of Mean Square Error is 11,114,950 and the value of Mean Absolute Deviation is 962. The use of alpha 0.5 model Exponential Smoothing method, the value of Mean Square Error is 1,114,776 and the value of Mean Absolute Deviation is 305. While the use of the alpha 0.9 model is Exponential Smoothing, the Mean Square Error value is -9.374 and the Mean Absolute Deviation value is -28. Of the three existing alpha models, namely 0.1; 0.5 and 0.9, then what will be used in forecasting is alpha 0.9 because the error value is the lowest, namely the Mean Square Error of -9,374 and Mean Absolute Deviation is -28. From the calculation of concrete brick forecasting at CV. Sinar Sowi in Manokwari Regency, the forecasting results were 39,698 units.

scholarly journals Peramalan Penerbitan Ijin Mendirikan Bangunan Dengan Single Moving Average Dan Exponential Smoothing

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Yogha Pramana ◽  
Rukmi Sari Hartati ◽  
Komang Oka Saputra
Keyword(s):  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Mean Error ◽  
Absolute Percent Error

Ijin Mendirikan Bangunan adalah ijin yang diberikan oleh Kepala Daerah pada pemilik bangunan untuk mendirikan bangunan, mengubah, memperluas, mengurangi atau merawat bangunan sesuai dengan persyaratan administratif dan persyaratan teknis yang berlaku. Peramalan adalah merupakan perkiraan mengenai terjadinya suatu kejadian pada masa depan. Peramalan merupakan sebuah alat bantu yang penting dalam perencanaan yang efesien dan efektif. Prosesnya untuk mengetahui kebutuhan di masa datang antara lain kebutuhan ukuran kuantitas, kualitas, waktu dan lokasi untuk pemenuhan permintaan barang ataupun jasa. Peramalan merupakan bagian awal dari pengambilan suatu keputusan akhir. Data Ijin Mendirikan Bangunan (IMB) di hitung dengan metode Simple Moving Average dan Exponential Smoothing untuk mengetahui nilai dari Mean Error, Mean Absolute Deviation, Mean Square Error, Standar Error, Mean Absolute Percent Error.

scholarly journals PREDIKSI JUMLAH PENERIMAAN MAHASISWA BARU DENGAN METODE SINGLE EXPONENTIAL SMOOTHING (STUDI KASUS: AMIK ROYAL KISARAN)

JURTEKSI ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 125-132
Author(s):  
Wiwin Handoko
Keyword(s):  
Mean Square Error ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Square ◽  
Computer Engineering ◽  
Study Program ◽  
History Of ◽  
Data History ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstract: A problem requires a solution to solve it. One of them is by using Prediction (Forcasting). Prediction is used to assess the prediction of conditions in the future. at AMIK Royal Kisaran, when it comes to making lecture schedules often hampered because there is no estimated number of students. The data used in this study is the data history of the last 15 Academic Years, from 2003/2004 to 2017/2018. Then the data is processed with the Single Exponential Smoothing Method. Alpha value 0 <α <1. Single Exponential Smoothing makes a comparison with the alpha value until alpha is found which has the minimum error. To find the value of the error, the MSE (Mean Square Error) method is used. The results of the testing of this method are in the academic year 2018/2019 prediction of the number of students for the Informatics Management Study Program as many as 89 people and for Students for the Computer Engineering Study Program as many as 30 people. The Single Exponential Smoothing method can predict the number of students in the next period. Keywords: Prediction; Number of Students; Single Exponential Smoothing; Alpha Value; MSE Abstrak: Suatu masalah memerlukan sebuah solusi untuk menyelesaikannya. Salah satunya dengan menggunakan Prediksi (Forcasting). Prediksi digunakan untuk menilai prakiraan keadaan dimasa. di AMIK Royal Kisaran, ketika akan membuat jadwal kuliah sering terhambat karena tidak adanya perkiraan jumlah mahasiswa. Data yang digunakan pada penelitian ini adalah histori data 15 Tahun Akademik terakhir, mulai 2003/2004 sampai dengan 2017/2018. Kemudian data diolah dengan Metode Single Exponential Smoothing. Nilai alpha 0<α<1. Single Exponential Smoothing melakukan perbandingan dengan nilai alpha tersebut sampai ditemukan alpha yang memiliki error paling minimum. Untuk mencari nilai Error digunakan Metode MSE (Mean Square Error). Hasil dari pengujian terhadap metode ini adalah pada Tahun akademik 2018/2019 prediksi jumlah Mahasiswa untuk Program Studi Manajemen Informatika sebanyak 89 orang  dan untuk Mahasiswa untuk Program Studi Teknik Komputer sebanyak 30 orang. Metode Single Exponential Smoothing dapat membantu prediksi jumlah mahasiwa pada satu periode kedepan Kata kunci: Prediksi; Jumlah Mahasiswa; Single Exponential Smoothing; Nilai Alpha; MSE 

scholarly journals Metode Peramalan moving Average, 1000 ANALISIS PERBANDINGAN PERAMALAN PERMINTAAN PELUMAS PT XYZ DENGAN METODE MOVING AVERAGE, EXPONENTIAL SMOOTHING DAN NAIVE METHOD

2021 ◽  
Vol 2020 (1) ◽  
pp. 1000-1010
Author(s):  
Destia Anisya Ramdani ◽  
Fahriza Nurul Azizah
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Naive Method

Pelumas merupakan produk dari PT XYZ yang digunakan untuk kendaraan dan mesin-mesin industri. Peramalan umumnya dilakukan untuk meramalkan jumlah produksi di masa mendatang dengan menggunakan data historis atau data-data pada permintaan sebelumnya terhadap produk perusahaan. Penelitian ini dilakukan untuk menguji enam metode peramalan agar dapat mengetahui metode mana yang tepat untuk diterapkan pada PT XYZ. Peramalan pada PT XYZ ini menggunakan data historis permintaan tahun 2019 dari bulan januari hingga bulan desember yang telah merepresentasikan pola permintaan setiap tahun di PT XYZ. Data ini digunakan untuk meramalkan setahun kedepan.Penelitian kali ini akan membandingkan enam metode peramalan diantaranya metode moving average 3 bulanan, moving average 5 bulanan, exponential smoothing dengan α=0,1, exponential smoothing dengan α=0,5, exponential smoothing dengan α=0,9 dan naive method. Untuk bahan perbandingan dari keenam metode yang telah disebutkan maka diberikan peramalan yaitu dengan metode penyimpangan Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), dan Absolute Presentage Error (MAPE).Hasil penelitian ini menunjukkan metode peramalan exponential smoothing dengan α=0,9 dengan nilai penyimpangan MAD 2.364,50, MSE 12.448.875,06, RMSE 3.528,30 dan MAPE 0,60 dapat dikatakan metode yang lebih optimal untuk diterapkan di PT XYZ karena memiliki nilai penyimpangan paling rendah dari metode moving average 3 bulanan, moving average 5 bulanan, exponential smoothing dengan α=0,1, exponential smoothing dengan α=0,5 dan naive method.Sehingga PT XYZ untuk menentukan tingkat permintaan konsumen dapat menggunakan metode exponential smoothing dengan α=0,9, karena setelah dilakukan perbandingan dari hasil penyimpangan setiap metode dan telah terbukti bahwasannya metode exponential smoothing dengan α=0,9 memiliki nilai penyimpangan MAD 2.364,60, MSE 12.448.875,06, RMSE 3.528,30 dan MAPE 0,60 yang artinya merupakan nilai penyimpangan terkecil dari metode moving average 3 bulanan, moving average 5 bulanan, exponential smoothing dengan α=0,1, exponential smoothing dengan α=0,5, dan naive method.

scholarly journals COMPARISON OF SOME STATISTICAL FORECASTING TECHNIQUES WITH GMDH PREDICTOR: A CASE STUDY

2018 ◽  
Vol 47 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Syed Misbah Uddin ◽  
Aminur Rahman ◽  
Emtiaz Uddin Ansari
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Demand forecasts are extremely important for manufacturing industry and also needed for all type of business and business suppliers for distribution of finish products to the consumer on time. This study is concerned with the determination of accurate models for forecasting cement demand. In this connection this paper presents results obtained by using a self-organizing model and compares them with those obtained by usual statistical techniques. For this purpose, Monthly sales data of a typical cement ranging from January, 2007 to February, 2016 were collected. A nonlinear modelling technique based on Group Method of Data Handling (GMDH) is considered here to derive forecasts. Forecast were also made by using various time series smoothing techniques such as exponential smoothing, double exponential smoothing, moving average, weightage moving average and regression method. The actual data were compared to the forecast generated by the time series model and GMDH model. The mean absolute deviation (MAD, mean absolute percentage error (MAPE) and mean square error (MSE) were also calculated for comparing the forecasting accuracy. The comparison of modelling results shows that the GMDH model perform better than other statistical models based on terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square error (MSE).

scholarly journals PERBANDINGAN HASIL PERAMALAN JUMLAH WISATAWAN MANCANEGARA DENGAN METODE BOX-JENKINS DAN EXPONENTIAL SMOOTHING

2021 ◽  
Vol 2 (1) ◽  
pp. 1-13
Author(s):  
EMMA NOVITA SARI ◽  
BAMBANG SUSANTO ◽  
ADI SETIAWAN
Keyword(s):  
Mean Square Error ◽  
Original Data ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Square ◽  
Smoothing Methods ◽  
The Future ◽  
Exponential Smoothing Method

Forecasting the number of tourist visits is needed by tourism businesses to provide an overview of the number of tourists in the future so that problems that might occur can be overcome properly. This study aims to compare the results of forecasting the number of foreign tourists using the Box-Jenkins and Exponential Smoothing methods. There are two data used, namely data on the number of foreign visitors visiting Indonesia from January 2008 to December 2017 (Data I) and Bali according to the entrance of Ngurah Rai Airport from January 2009 to March 2020 (Data II). The best forecast results are obtained by comparing the Root of Mean Square Error (RMSE) values. The comparison of forecasting results in Data I shows that the Holt-Winters Exponential Smoothing method is more appropriate to predict the number of foreign tourists visiting Indonesia because it has a smaller RMSE value. While, the results of forecasting periods 2 and 3 in Data II show results that are far different from the original data. After tracking, it turns out this is caused by an unexpected factor, the Covid-19 pandemic which caused the number of tourists to drop significantly during this period.

scholarly journals Forecasting of Groundwater Tax Revenue Using Single Exponential Smoothing Method

2019 ◽  
Vol 125 ◽  
pp. 23006
Author(s):  
Dyna Marisa Khairina ◽  
Aqib Muaddam ◽  
Septya Maharani ◽  
Heliza Rahmania
Keyword(s):  
Reference Value ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Percentage Error ◽  
Tax Revenue ◽  
Regional Office ◽  
Forecasting Error ◽  
The Government ◽  
Exponential Smoothing Method ◽  
Single Exponential

Setting the target of groundwater tax revenues for the next year is an important thing for Kutai Kartanegara Regional Office of Revenue to maximize the regional income and accelerate regional development. Process of setting the target of groundwater tax revenue for the next year still using estimation only and not using a mathematical calculation method that can generate target reference value. If the realization of groundwater tax revenue is not approaching the target, the implementation of development in the Government of Kutai Kartanegara can be disrupted. The mathematical method commonly used to predict revenue value is the Single Exponential Smoothing (SES) method, which uses alpha constant value which is randomly selected for the calculation process. Forecasting of groundwater tax revenue for 2018 using groundwater tax revenue data from 2013 to 2017. Single Exponential Smoothing method using alpha constant value consists of 0.1, 0.2, 0.3, 0.4 and 0.5. The forecasting error value of each alpha value is calculated using the Mean Absolute Percentage Error (MAPE) method. The best result is forecasting using alpha value 0.1 with MAPE error value was 45.868 and the best forecasting value of groundwater tax for 2018 is Rp 443.904.600,7192.

scholarly journals Penerapan Single Exponential Smoothing (SES) dalam Perhitungan Jumlah Permintaan Air Mineral Pada PT. Akasha Wira International

2020 ◽  
Vol 3 (2) ◽  
pp. 87
Author(s):  
Lolyka Dewi Indrasari
Keyword(s):  
Mineral Water ◽  
Forecast Error ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Forecasting Error ◽  
Data Value ◽  
Forecast Data ◽  
Projection Error ◽  
Single Exponential

Daily needs that are priceless but useful for health one of which is mineral water. The need for mineral water increases with the high demand in the market. The purpose of this study was to determine the forecasting of the number of requests for 330 ml shortneck mineral water products in the future using the Single Exponential Smoothing (SES) method. Limitation of the problem is discussing the number of requests in the first half of 2020, the data used were obtained from PT. Akasha Wira International from January 2014 to December 2019. The analytical method is to calculate the forecast error value of the different 𝛼 values to find one value that produces the smallest error with the calculation method Mean Absolute Deviation (MAD) and Single Exponential Smoothing (SES) can interpreted based on the calculation stage where the forecast data value in the period 𝑡 + 1 is the actual value in the period t plus the adjustment derived from forecasting error that occurred in the period t. The results obtained on the value of Mean Absolute Deviation (MAD) are taken at a = 0.9 because it produces the smallest value of the projected data projection error of 1860 units. Whereas in forecasting requests using Single Exponential Smoothing (SES), 330 ml shortneck mineral water in the first half of 2020 amounted to 2177634 units. Keyword : Mean Absolute Deviation, Single Exponential Smoothing, shortneck.Kebutuhan sehari – hari yang tidak ternilai harganya tapi berguna bagi kesehatan salah satunya adalah air mineral. Kebutuhan air mineral meningkat seiring dengan tingginya permintaan pada pasar. Tujuan penelitian ini, yaitu untuk mengetahui peramalan jumlah permintaan pada produk air mineral 330 ml shortneck dimasa mendatang menggunakan metode Single Exponential Smoothing (SES). Batasan masalah yaitu membahas jumlah permintaan dimasa mendatang semester I 2020, data yang digunakan diperoleh dari PT. Akasha Wira International pada Januari 2014 sampai dengan Desember 2019. Metode analisis yaitu Menghitung nilai kesalahan peramalan terhadap nilai 𝛼 yang berbeda beda untuk menemukan satu nilai 𝛼 yang menghasilkan kesalahan terkecil dengan metode perhitungan Mean Absolute Deviation (MAD) dan Single Exponential Smoothing (SES) dapat diartikan berdasarkan tahapan perhitungannya dimana nilai data ramalan pada periode 𝑡 + 1 merupakan nilai actual pada periode t ditambah dengan penyesuaian yang berasal dari kesalahan nilai peramalan yang terjadi pada periode t. Didapatkan hasil pada nilai Mean Absolute Deviation (MAD) diambil pada a = 0,9 karena menghasilkan nilai kesalahan proyeksi data pemrintaan paling kecil yaitu 1860 unit. Sedangkan pada peramalan permintaan menggunakan Single Exponential Smoothing (SES), air mineral 330 ml shortneck pada semester I tahun 2020 sebesar 2177634 unit.  Kata Kunci: Mean Absolute Deviation, Single Exponential Smoothing, shortneck 

scholarly journals Analysis Comparative Method Shari’a Compliant Asset Pricing Model

SENTRALISASI ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 132
Author(s):  
Bekti Wiji Lestari ◽  
Erma Setiawati ◽  
Noer Sasongko
Keyword(s):  
Stock Returns ◽  
Mean Square Error ◽  
Comparative Method ◽  
Explanatory Power ◽  
Sampling Technique ◽  
Coefficient Of Determination ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Risk Free Rate

The researcher's view of the CAPM model is not in accordance with the Islamic economy because there is an Rf element as an instrument that contains an interest element, so a modified model of CAPM based on sharia is introduced, namely SCAPM. This study aims to analyze the differences in the SCAPM method according to Tomkins & Karim (1987) SCAPM non Rf, Ashker (1987) SCAPMZ, Shaikh (2010) SCAPM NGDP, and Hanif (2011) SCAPMI. The sampling technique used was purposive sampling and obtained 19 samples. Data analysis used is the calculation of Mean Absolute Deviation (MAD), Mean Square Error (MSE), and the coefficient of determination. The results of the calculation of Mean Absolute Deviation (MAD) and Mean Square Error (MSE) explain that there are differences from the SCAPM models without risk free rate, SCAPMZ, SCAPM NGDP, and SCAPMI. Meanwhile, SCAPMI has the best explanatory power than the other four SCAPM models. It is recommended that Islamic and conventional investors use SCAPMZ modeling in predicting stock returns.

ANALISIS PERENCANAAN PENJUALAN DENGAN METODE TIME SERIES (STUDI KASUS PADA PD. SUMBER JAYA ALUMINIUM)

2019 ◽  
Vol 3 (7) ◽  
pp. 780-789
Author(s):  
Lingga Yuliana
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation

Penjualan dan produksi adalah dua hal yang saling berkaitan dan tidak dapat terpisahkan didalam suatu pengoperasian perusahaan, didalam memproduksi suatu produk perusahaan harus melihat tersedia dalam gudang / penyimpanan serta beberapa jumlah yang akan dijual. Disebabkan dengan hal tersebut maka perusahaan perlu melakukan peramalan penjualan (sales forecasting). Tujuan dalam penelitian ini menentukan metode peramalan yang tepat berdasarkan tingkat kesalahan terkecil berdasarkan nilai Mean Absolute Deviation (MAD), Mean Square Error (MSE) dan Mean Absolute Procentage Error (MAPE). Penelitian ini menunjukkan bahwa plot data peramalan penjualan rak piring menunjukkan kecenderungan naik (trend). Metode peramalan yang dianggap terbaik terdapat pada metode winter multiplikatif, karena tiga nilai kesalahan (error) yang diuji yaitu MAD, MSE dan MAPE menunjukkan tingkat kesalahan yang paling kecil dengan metode tersebut. Nilai kesalahan yang ditunjukkan dalam penelitian ini, dimana nilai MAD sebesar 73,5, nilai MSE sebesar 10137,7 dannilai MAPE sebesar 4,9.

scholarly journals Sistem Informasi Peramalan Persediaan Barang Menggunakan Metode SES Dan DES

2019 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
Ratih Yulia Hayuningtyas
Keyword(s):  
Medical Equipment ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Equal Weight ◽  
Mean Square ◽  
Double Exponential ◽  
Forecasting System ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstract: Sales is an activity in selling products that provide information about inventory. Arga Medical is a shop engaged in the sale of medical equipment, many of sales transactions in the Arga Medical will affect the inventory. Problems in the Arga Medical is predicting many of product that must available for the next month. Therefore this research makes inventory information forecasting system using Single Exponential Smoothing and Double Exponential Smoothing method. This inventory forecasting information system will result a inventory forecasting for next month. Single Exponential Smoothing Method gives equal weight to each data while Double Exponential Smoothing method is smoothing twice. The Data used in this research is the sales data during 2016. Both of these methods resulted inventory forecasting in the next month is Januari 2017 of 52 with Single Exponential Smoothing and 60 with Double Exponential Smoothing. Each method has a Mean Square Error value the smallest error value is the best method for forecasting inventory. Keywords: Forecasting, Inventory, Single Exponential Smoothing, Double Exponential Smoothing.

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