scholarly journals ANALISIS PERAMALAN DAN PERMINTAAN KREDIT SEKTOR PERTANIAN PADA PT.BRI (PERSERO) UNIT KEPAHIANG I

2004 ◽  
Vol 3 (2) ◽  
pp. 82-91
Author(s):  
Redy Badrudin ◽  
Bambang Sumantri ◽  
Meiliza Cecilia
Keyword(s):  
Agricultural Sector ◽  
Moving Average ◽  
Secondary Data ◽  
Regression Function ◽  
Agricultural Credit ◽  
Factors Influencing ◽  
Double Exponential ◽  
Interest Level ◽  
Double Exponential Smoothing ◽  
Credit Data

The study  was aimed to Know   growth and forecast the amount of demand for agricultural sector credit at PT. BRI (Persero) Unit Kepahiang I, and (2) examine factors influencing for agricultural sector credit. Data  was secondary data from 1993  up to 2003. Ratio  to Moving Average Method, Double Exponential Smoothing with moved period 3 monthly and regression function of Non Doubled Linear were used. Results of research indicates that growth of demand for agricultural credit at PT. SRI (Persero) Unit Kepahiang I tend to fluctuate. Forecasting of demand for agricultural sector creditb to period quarterly at 2003 till 2004, tend   to have experience decreasing significantly compare to previous period. Overall of factors influencing demand of agricultural sector is rate of interest level (X1), price level (X2), and agriculture product exchange rate (X3), while earning level (X4) does not have an effect on demand Key Words: BRI, Forecasting, demand for credits,agricultura/s , sectors

scholarly journals USULAN PERMINTAAN PRODUK SN 5 ML DI PT. XYZ DENGAN METODE TIME SERIES

2021 ◽  
Vol 8 (2) ◽  
pp. 117-122
Author(s):  
Sambas Sundana ◽  
Destri Zahra Al Gufronny
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential ◽  
Weighted Moving Average

Permasalahan yang dihadapi PT. XYZ yaitu kesulitan dalam menentukan jumlah permintaan produk yang harus tersedia untuk periode berikutnya agar tetap dapat memenuhi kebutuhan pelanggan dan tidak menyebabkan penumpukan barang dalam jangka waktu yang lama terutama produk SN 5 ML yang memiliki permintaan jumlah paling besar dari produk lainnya. Tujuan dari penelitian ini yaitu menentukan metode peramalan yang tepat untuk meramalkan jumlah permintaan produk SN 5 ml periode Januari sampai dengan Desember 2021 Metode yang digunakan dalam penelitian ini yaitu metode peramalan Moving Average (MA), Weighted Moving Average (WMA), Single Exponential Smoothing (SES), dan Double Exponential Smoothing (DES). Adapun langkah langkah peramalan yang dilakukan yaitu menentukan tujuan peramalan,memilih unsur apa yang akan diramal, menentukan horizon waktu peramalan (pendek, menengah, atau panjang), memilih tipe model peramalan, mengumpulkan data yang di perlukan untuk melakukan peramalan, memvalidasi dan menerapkan hasil peramalan Berdasarkan perhitungan didapat metode peramalan dengan persentase tingkat kesalahan terkecil dibandingkan dengan metode lainnya yaitu  metode Moving Average (MA) dengan hasil yang diperoleh permintaan produk SN 5 ML pada bulan Januari sampai dengan Desember 2021 yaitu sebanyak 22.844.583 unit

scholarly journals PREDIKSI KREDIT DAN INDEKS MUSIMAN (SEASONAL INDEX) PADA BANK PERKREDITAN RAKYAT DI KABUPATEN JEMBER

BISMA ◽  
2020 ◽  
Vol 14 (3) ◽  
pp. 210
Author(s):  
Hari Sukarno ◽  
Ratna Pratiwi Nugroho ◽  
Susanti Prasetiyaningtiyas
Keyword(s):  
Moving Average ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Development Pattern ◽  
Research Approach ◽  
Financial Reports ◽  
Double Exponential ◽  
Study Results ◽  
Double Exponential Smoothing ◽  
Seasonal Index

This research aims to analyze the credit's predictive value, the development pattern of credit distribution, and the credit fluctuations of 13 Rural Banks in Jember, influenced by seasonal index variables, credit interest, NPL, LDR, ROA, CAR, and operational efficiency ratio. This study used an explanatory research approach. The sample consisted of all Rural Banks' quarterly financial reports in 2014-2019 taken by a purposive sampling method. Data were analyzed using three methods, i.e., double exponential smoothing, moving average ratio, and multiple linear regression analysis methods. Results showed that, according to each data analysis method, ten Rural Banks experienced increased credit distribution. However, the other three Rural Banks experienced a decrease in credit distribution. The study results also indicated an increasing trend in the development pattern of credit distribution. Meanwhile, the NPL and LDR variables partially influenced credit fluctuations. Keywords: credit prediction, rural bank, seasonal index

scholarly journals Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Rendra Gustriansyah ◽  
Wilza Nadia ◽  
Mitha Sofiana
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Standard Type ◽  
The Public ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Hotel is  a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.</em></p><p><strong><em>Keywords</em></strong><em>: forecasting, linier regression, moving average, exponential smoothing.</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Hotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di  masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.</em></p><strong><em>Kata kunci</em></strong><em>: peramalan, regeresi linier, moving average, exponential smoothing.</em>

scholarly journals PREDIKSI INFLASI DI INDONESIA MENGGUNAKAN METODE MOVING AVERAGE, SINGLE EXPONENTIAL SMOOTHING DAN DOUBLE EXPONENTIAL SMOOTHING

2020 ◽  
Vol 1 (2) ◽  
pp. 123-129
Author(s):  
Nugroho Arif Sudibyo ◽  
Ardymulya Iswardani ◽  
Arif Wicaksono Septyanto ◽  
Tyan Ganang Wicaksono
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential

Tujuan dari penelitian ini adalah untuk mengetahui model peramalan yang paling baik digunakan untuk meramalkan inflasi di Indonesia dengan data inflasi Januari 2015 sampai dengan Mei 2020. Penelitian ini menggunakan beberapa metode peramalan. Berdasarkan metode peramalan yang dilakukan didapatkan hasil peramalan yang paling baik dilihat dari MAPE, MAD dan MSD adalah single exponential smoothing. Selanjutnya, hasil peramalan menunjukkan bahwa tingkat inflasi di Indonesia pada Agustus 2020 sebesar  1,41746%.

scholarly journals Peramalan Rate of Return Saham Menggunakan Metode Brown’s Weighted Exponential Moving Average dengan Optimasi Levenberg-Marquardt

2019 ◽  
Vol 8 (3) ◽  
Author(s):  
Nur Hasanah Abdullah ◽  
Junaidi ◽  
Lilies Handayani
Keyword(s):  
Stock Prices ◽  
Moving Average ◽  
Limited Liability ◽  
Rate Of Return ◽  
Average Value ◽  
Daily Data ◽  
Double Exponential ◽  
Closing Price ◽  
Double Exponential Smoothing ◽  
A Company

Stocks can be defined as a sign of the participation of unilateral capital in a company or a limited liability company in the form of proof of a company's assets and it is formed as a valueble proof letters as a statement of participating in capital. Return stockis one of the factors that motivates investors to invest or interact and also a reward for the courage of investors to take risks or investments. PT Bank Rakyat Indonesia is one of the largest banks in Indonesia that experiences an increase in stock prices every year. The Brown's Weighted Exponential Moving Average (BWEMA) method which is a combination of the Weighted Exponential Moving Average (WEMA) and Brown's Double Exponential Smoothing (BDES) methods will be used in this study. The data used is the daily data of closing price of the stock (closing price) of PT. Bank Rakyat Indonesia on February 6, 2018 until February 6, 2019. MSE and MAPE BWEMA values ​​were obtained at 6124.222 and 1.831685%, while MSE and MAPE WEMA values ​​were 7559.211 and 1.998439% respectively. The results obtained show that the BWEMA method has smaller MSE and MAPE values. This shows that the BWEMA method is better than the WEMA method in terms of forecasting. As a results the BWEMA method is continued to calculate the forecasts rate of return for the next 7 days. The average value returns obtained is 0.111497% stock  which means during the period of the investment, investors get a profit of 0.111497%% per day of the total funds invested in the shares of PT Bank Rakyat Indonesia (Persero) Tbk.

scholarly journals PERAMALAN PENERIMAAN BEA CUKAI INDONESIA

2018 ◽  
Vol 2 (1) ◽  
pp. 137
Author(s):  
Yolanda Sari ◽  
Nurlia Fusfita
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Rational Approach ◽  
Ordinary Least Square ◽  
Export Duty ◽  
Double Exponential ◽  
Double Exponential Smoothing

The revenue of customs and excise is very important in APBN. By making accurate estimation, target of revenue can be better determined. In addition, the revenue of customs and excise is also influenced by many external factors that are difficult to predict therefore a rational approach is needed to estimate revenue. This research uses Double Exponential Smoothing, Ordinary Least Square (OLS) model and Moving Average in predicting customs and excise revenue. Data used in this research is secondary data in time coherent pattern. The data includes import duty, export duty and excise obtained from the Directorate General of Customs and excise (DJBC) in the form of annual and quarterly data. This data starts from 2002 to 2016 with out of sample from 2017 to 2019. Some of these models are compared to each other to obtain the best model, and from the best model is also obtained estimating results in 3 years ahead. This study shows that the Double Exponential Smoothing model is better for predicting import duties compared to OLS and Moving Average models, which are models that have the smallest Sum Square Error (SSE) value. While the export and excise duty is best estimated by using OLS model which is shown with coefficient of determination value (R2)  regression model of export duty is 0.8, while the excise regression model has coefficient of determination of 0.9.Keywords:  Customs Estimation, Double Exponential Smoothing, Ordinary Least Square, Moving Average

scholarly journals Perbandingan Metode Exponential Smoothing dan Moving Average Pada Peramalan Jumlah Produksi Garam di Madura

2021 ◽  
Vol 6 (1) ◽  
pp. 17-23
Author(s):  
Mahrus Mahrus ◽  
Tony Yulianto ◽  
Faisol Faisol
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing

Madura merupakan salah satu penghasil garam terbesar di Indonesia, produksi garam di Madura pada musim produksi tahun 2015 mencapai 914.484 ton, dari empat kabupaten di wilayah Madura. Produksi garam tersebut untuk memenuhi kebutuhan garam nasional, untuk memenuhi produksi garam di Madura diperlukan peramalam jumlah produksi agar mendapatkan hasil yang maksimal. Salah satu metode peramalan adalah metode time series. Pada penelitian ini membandingkan hasil peramalan menggunakan metode double exponential smoothing dan moving average, yang menghasilkan bahwa metode double exponential smoothing lebih baik dengan nilai RMSE = 664313,1792 dan MAPE = 5.720599.

scholarly journals PEMODELAN METODE BROWN’S DOUBLE EXPONENTIAL SMOOTHING (B-DES) DAN BROWN’S WEIGHTED EXPONENTIAL MOVING AVERAGE (B-WEMA) MENGGUNAKAN OPTIMASI LEVENBERG-MARQUARDT PADA JUMLAH WISATAWAN DI JAWA TENGAH

2020 ◽  
Vol 9 (3) ◽  
pp. 316-325
Author(s):  
Dilla Retno Deswita ◽  
Abdul Hoyyi ◽  
Tatik Widiharih
Keyword(s):  
Foreign Exchange ◽  
Optimal Parameter ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Forecasting Accuracy ◽  
Central Java ◽  
Tourism Sector ◽  
Double Exponential ◽  
Levenberg Marquardt ◽  
Double Exponential Smoothing

The tourism sector is one of the national development priority sectors because it contributes to foreign exchange earnings, the development of business areas, and the absorption of investment and labor. In 2018 the tourism sector will become the second largest foreign exchange earner after oil palm. Foreign exchange contributed by the tourism sector in 2018 was US $ 19.29 billion, an increase of 15.4%. The increase in contributions was driven by an increase in the number of foreign tourist arrivals by 12.58%, domestic tourists by 12.37%, and from investment. Therefore it is necessary to study the forecasting of the number of tourists after seeing the great potential generated from the tourism sector. The data forecast is data on the number of tourists in Central Java, both foreign and domestic data. Both data shows the tendency of an upward trend pattern. So that both data can be analyzed using B-DESmethods (Brown's Double Exponential Smoothing) and B-WEMA (Brown's Weighted Exponential Moving Average)that are optimized with LM (Levenberg-Marquardt). Both methods are able to analyze trend patterned data without assumptions making it easier in the analysis process. In addition, the two methods in previous studies were able to produce a small forecasting accuracy. The MAPE (Mean Absolute Percentage Error) value out sample is used to compare the forecasting results of the two methods. The results of the implementation of LM optimization on the data of the number of domestic tourists obtained the optimal parameter value of the B-DES method is 0.21944386 with MAPE out sample 16.26516% and B-WEMA method is 0.219441 with MAPE out sample 16.26515%. While the data on the number of foreign tourists obtained the optimal parameter value of the B-DES method was 0.26213368 with the MAPE out of the sample 23.61278% and the B-WEMA method was 0.26213367 with the MAPE out the sample 23.61278%. This means that both methods have a good level of forecasting accuracy in the data on the number of domestic tourists and an adequate level of accuracy in the data on the number of foreign tourists. Keywords : B-DES, B-WEMA, Levenberg-Marquardt, Tourists in Central Java

scholarly journals Pemilihan Metode Peramalan Jumlah Permintaan Koran dengan Tingkat Kesalahan Terendah

2021 ◽  
Vol 21 (2) ◽  
pp. 91
Author(s):  
Wahyudi Sutopo ◽  
Azizah Hadny Quarrota A'yun ◽  
Hanif Ardian ◽  
Maulidina Khairannisa Nunuh ◽  
Sherlinta Immanuella ◽  
...  
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Trend Line ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Line Analysis

Pada era digital saat ini, banyak sekali industri yang mengalami dampak dari digitalisasi, salah satunya adalah industri surat kabar (koran). Adanya digitalisasi menyebabkan permintaan koran semakin fluktuatif dan sulit diprediksi. Hal ini juga menyebabkan tingkat retur atau pengembalian koran dari agen-agen yang cukup tinggi dan tentu saja akan memberikan kerugian yang cukup besar bagi perusahaan. Untuk itu, perlu dilakukan penentuan métode peramalan jumlah permintaan koran yang memiliki tingkat kesalahan terkecil sehingga dapat membantu perusahaan mengurangi kerugian akibat retur koran. Penelitian ini menghitung peramalan permintaan menggunakan beberapa metode antara lain trend line analysis, double exponential smoothing, dan two months moving average. Selain itu, penelitian ini juga membandingkan hasil peramalannya dengan penelitian terdahulu yang menggunakan metode ARIMA. Pemilihan metode peramalan yang terbaik dilakukan dengan membandingkan tingkat kesalahan (MAPE) dari tiap-tiap metode kemudian dipilih metode dengan tingkat kesalahan terkecil. Berdasarkan perbandingan yang dilakukan, dapat diketahui bahwa metode peramalan yang memiliki tingkat kesalahan terkecil adalah metode trend line analysis dengan nilai MAPE sebesar 2,94%. Oleh karena itu, metode peramalan yang terbaik untuk melakukan peramalan permintaan jumlah koran di Kota Surakarta adalah metode trend line analysis.

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