scholarly journals Analisis Peramalan Permintaan Produk Garam Konsumsi Beryodium Pada UD Garam Samudra

2020 ◽  
Vol 3 (2) ◽  
pp. 309-323
Author(s):  
Roudlotul Badi’ah ◽  
Wiwik Handayani
Keyword(s):  
Linear Regression ◽  
Exponential Smoothing ◽  
Moving Averages ◽  
Naive Method

Dalam dunia bisnis konsumen merupakan faktor penentu agar perusahaan tetap bertahan, sehingga sangat diperlukan pertimbangan untuk pemenuhan permintaan. Strategi perlu diterapkan untuk mengelola permintaan dengan baik yakni melalui peramalan permintaan, karena menyangkut pengambilan keputusan dalam perencanaan untuk memenuhi konsumen. UD Garam Samudra merupakan perusahaan yang bergerak dibidang pengolahan dan perdagangan garam ditahun 2019 mengalami masalah belum terpenuhinya permintaan karena ketidakpastian jumlah permintaan disetiap bulannya. Penelitian ini bertujuan memperoleh metode peramalan permintaan garam konsumsi beryodium di UD Garam Samudra yang tepat untuk memenuhi seluruh permintaan konsumen dimasa akan datang. Data yang digunakan yaitu data permintaan periode Januari–Desember 2019. Teknik analisis peramalan menggunakan Naive Method, Moving Averages, Weighted Moving Averages, Exponential Smoothing, dan Linear Regression dibantuan program QM for Windows. Hasil penelitian menunjukkan metode peramalan terpilih untuk memenuhi permintaan garam konsumsi beryodium di UD Garam Samudra yaitu Linear Regression karena memiliki tingkat kesalahan peramalan berdasarkan kriteria MSE terkecil dibanding metode lainnya.

scholarly journals Analisis Peramalan Penjualan dan Penggunaan Metode Linear Programming dan Decision Tree Guna Mengoptimalkan Keuntungan pada PT Primajaya Pantes Garment

The Winners ◽  
2013 ◽  
Vol 14 (2) ◽  
pp. 113
Author(s):  
Inti Sariani Jianta Djie
Keyword(s):  
Linear Programming ◽  
Linear Regression ◽  
Decision Tree ◽  
Production Capacity ◽  
Exponential Smoothing ◽  
Programming Method ◽  
Linear Regression Method ◽  
Moving Averages ◽  
Linear Programming Method ◽  
Naive Method

Primajaya Pantes Garment is a company that runs its business in garment sector. However, due to various numbers of requests each month, the company is difficult to determine the amount of production per month that is appropriate to maximize profits. The purpose of this study is to determine the appropriate forecasting method that can be used as a reference to determine the amount of production in the next period and to find a combination of products to maximize profits. Research used forecasting methods, including naive method, moving averages, weighted moving averages, exponential smoothing, exponential smoothing with trend, and linear regression. In addition, this study also used Linear Programming method with Simplex method to determine the best combination of products for the company and to choose a decision using a decision tree to determine which alternative should be done by the company. Results of this study found that the linear regression method is the most appropriate method in determining the forecast demand in the next period. While in the Linear Programming method, constraints used were the constraints of raw materials, labor hours, and limited demand for the product. The result of the decision tree is to increase production capacity.

scholarly journals PERAMALAN PENJUALAN AIR MINUM ISI ULANG 19 LITER PADA USAHA DEPOT TIRTA ASRI UNTUK MENINGKATKAN VOLUME PENJUALAN TAHUN 2016 DI DAERAH TAJUR HALANG BOGOR DENGAN METODE FORECASTING

2018 ◽  
Vol 12 (02) ◽  
pp. 60-72
Author(s):  
Rizka Fernanda Rumai Damayanti
Keyword(s):  
Linear Regression ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Naive Method ◽  
Weighted Moving Average

Tujuan penelitian ini untuk mengetahui perkembangan usaha dengan melakukan peramalan penjualan pada depot air minum isi ulang Tirta Asri di Tajur Halang Bogor. Metode yang digunakan dalam penelitian ini adalah pendekatan enam metode forecasting. Penelitian ini menunjukkan hasil peramalan untuk exponential smoothing dengan MAD = 186,9520 dan MSE = 44017,0091, weighted moving average dengan MAD = 192 dan MSE = 52418,2866, moving average dengan MAD = 182,8886 dan MSE = 50966,1063, linear regression dengan MAD =134,2571 dan MSE = 22649,1809, naive method dengan MAD =246,4 dan MSE = 73564,8, exponential smoothing with trend dengan MAD = 177,2625 dan MSE = 46714,1544. Dengan demikian metode linear regression yang paling tepat digunakan untuk melakukan peramalan penjualan, karena hasil kesalahan lebih kecil dibandingkan dengan lima metode lainnya.

scholarly journals Prediksi Jumlah Tamu Menginap di Hotel Karlita International, Tegal, Jawa Tengah

2013 ◽  
Vol 4 (2) ◽  
pp. 661-675
Author(s):  
Haryadi Sarjono ◽  
Irwan Zulkifli
Keyword(s):  
Comparative Analysis ◽  
Linear Regression ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Multiplicative Decomposition ◽  
Additive Decomposition ◽  
Central Java ◽  
Forecasting Methods ◽  
Naive Method ◽  
Weighted Moving Average

Article is forecasting comparative analysis of number of guess room occupancy at Karlita International Hotel, Tegal, Central Java using 11 forecasting methods: linear regression, moving average, weighted moving average, exponential smoothing, exponential smoothing with trend, naïve method, trend analysis, additive decomposition – CMA, additive decomposition – average all, multiplicative decomposition – CMA, multiplicative decomposition – average All. Article used 17 data from January 2012 to Mei 2013, and results after using those 11 methods were the smallest MAD is 101.69 and the smallest MSE is 15,163.95. From additive decomposition – average all method, data showed guess room occupancy forecast at Karlita International Hotel for June 2013 is 960 guess.

scholarly journals INVENTORY CONTROL USING STATISTICS FORECASTING ON MANUFACTURE COMPANY

2014 ◽  
Vol 1 (2) ◽  
Author(s):  
Rizki Tri Prasetio
Keyword(s):  
Linear Regression ◽  
Inventory Control ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Order Quantity ◽  
Naive Method ◽  
Forecasting Method ◽  
Weighted Moving Average

Abstract - Inventory Control is a main and the most crucial factor for company that can cause an efficient production process. A lot of research use different method to support inventory control. This research use several forecasting method such as, Naïve Method, Exponential Smoothing, Exponential Smoothing with Trend, Moving Average, Weighted Moving Average and Linear Regression. Economic Order Quantity is used to calculate raw materials inventory. This research results suggest that company use Linear Regression as it has the smallest MAD and MSE of the six other methods. The company also has to implement Economic Order Quantity to minimalize loss profit due to excess inventory. Keywords : Inventory Control, Forecasting Method, Economic Order Quantity Abstrak - Pengendalian inventory merupakan salah satu faktor utama dan sangat penting bagi perusahaan karena sangat berpengaruh terhadap terciptanya proses produksi yang efisien. Banyak penelitian yang menggunakan beberapa metode guna mendukung pengendalian inventory. Penelitian ini menggunakan beberapa metode peramalan (forecasting method) diantaranya, Naïve Method, Exponential Smoothing, Exponential Smoothing with Trend, Moving Average, Weighted Moving Average dan Linear Regression. Serta Economic Order Quantity (EOQ) yang digunakan untuk menghitung persediaan bahan baku yang dibutuhkan dalam proses produksi. Hasil penelitian menghasilkan bahwa metode peramalan Linear Regression memiliki tingkat kesalahan yang dihitung menggunakan MAD dan MSE paling kecil diantara 6 metode lainnya. Serta mengimplementasikan Economic Order Quantity untuk meminimalisir kerugian akibat kelebihan persediaan. Kata Kunci : Pengendalian Persediaan, Metode Peramalan, Economic Order Quantity

scholarly journals Peramalan Kurs IDR Terhadap USD Menggunakan Double Moving Averages Dan Double Exponential Smoothing

2019 ◽  
Vol 2 (2) ◽  
pp. 48-55
Author(s):  
Padrul Jana ◽  
Rokhimi Rokhimi ◽  
Ismi Ratri Prihatiningsih
Keyword(s):  
Exponential Smoothing ◽  
Moving Averages ◽  
Double Exponential ◽  
Double Exponential Smoothing

Kurs IDR terhadap USD yang fluktuatif sangat mempengaruhi ekonomi Indonesia saat ini, dibutuhkan suatu metode untuk meramalkan Kurs IDR terhadap USD agar bisa diprediksi. Diharapkan  para pemangku kepentingan segera mengambil kebijakan strategis demi stabilitas ekonomi nasional. Metode peramalan dalam tulisan ini menggunakan Double Moving Averages dan Double Exponential Smoothing dengan . Hasil peramalan menggunakan metode Double Moving Averages diperoleh IDR/USD, IDR/USD, IDR/USD dan Double Exponential Smoothing diperoleh IDR/USD, IDR/USD, IDR/USD. 14"> Kata Kunci: IDR, USD, Double Moving Averages, Double Exponential Smoothing.

scholarly journals Kajian Peramalan dan Pengaruh Profitabilitas terhadap Harga Saham pada Perusahaan Makanan dan Minuman Tahun 2011-2016

2017 ◽  
Vol 8 (1) ◽  
pp. 75-88
Author(s):  
Octaviani Hutahaean ◽  
Abdul Basith
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Profit Margin ◽  
Moving Averages ◽  
Return On Assets ◽  
Return On Equity ◽  
Purposive Sampling ◽  
Net Profit ◽  
Weighted Moving Average

Laju pertumbuhan industri terbesar selama tahun 2011-2015 yaitu 8,48 persen terhadap Produk Domestik Bruto (PDB) mencerminkan perusahaan yang termasuk dalam industri makanan dan minuman memiliki kinerja bisnis yang baik. Penelitian ini bertujuan untuk mengetahui kondisi harga saham dan profitabilitas pada tahun 2011-2015, mengetahui peramalan harga saham dan profitabilitas pada tahun 2016 dan untuk menganalisis pengaruh profitabilitas terhadap harga saham pada tahun 2011-2016. Analisis profitabilitas dipresentasikan oleh beberapa rasio keuangan yaitu Return On Equity (ROE), Return On Assets (ROA), Net Profit Margin (NPM), dan Earning Per Share (EPS). Penelitian ini menggunakan teknik purposive sampling dan data yang digunakan merupakan data sekunder. Peramalan menggunakan metode moving averages, weighted moving average, dan exponential smoothing dengan nilai MAD terkecil menggunakan aplikasi POM-QM for windows-3. Model analisis yang digunakan dalam penelitian ini adalah regresi linier berganda dengan menggunakan SPSS 18. Hasil penelitian menunjukkan bahwa PT Delta Djakarta, Tbk (DLTA) memiliki kondisi harga saham, ROE, ROA, dan EPS dengan rata-rata tertinggi selama 2011-2015. PT Tiga Pilar Sejahtera Food, Tbk (AISA) memiliki rata-rata NPM tertinggi selama 2011-2015. PT Delta Djakarta, Tbk (DLTA) dan PT Indofood Sukses Makmur, Tbk (INDF) menunjukkan peramalan tahun 2016 terhadap harga saham dan profitabilitas mengalami peningkatan dari tahun sebelumnya. Profitabilitas berpengaruh simultan dan signifikan terhadap harga saham dan secara parsial menunjukkan bahwa ROE dan EPS berpengaruh dan signifikan terhadap harga saham.

scholarly journals PREDIKSI JUMLAH PENERIMAAN SISWA SMK SWASTA TAHUN AJARAN 2011/2012

2012 ◽  
Vol 3 (2) ◽  
pp. 923
Author(s):  
Haryadi Sarjono
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Vocational High School ◽  
Absolute Deviation ◽  
Squared Error ◽  
Forecasting Methods ◽  
Naive Method ◽  
Manual Calculation ◽  
Weighted Moving Average

This study aims to determine prediction number of modern private Vocational High School (SMK) students in a province in Borneo with the approach of six forecasting methods: Linear Regression, Exponential Smoothing with Trend, Exponential Smoothing, Weighted Moving Average, Moving Average, and the Naive Method, besides using Manual calculation, the approach of QM for windows is used as a comparison. The result will be determined by the six forecasting methods which is used as a proper basis for the next calculating based on the smallest MAD (Mean Absolute Deviation) and MSE (Mean Squared Error) approach. The data in this study were made by the writer alone. 

Innovation in Scientific Knowledge Based on Forecasting Assessment

2019 ◽  
pp. 247-263
Author(s):  
Ignacio Aranís Mahuzier ◽  
Pablo A. Viveros Gunckel ◽  
Rodrigo Mena Bustos ◽  
Christopher Nikulin Chandía ◽  
Vicente González-Prida Díaz
Keyword(s):  
Scientific Knowledge ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Moving Averages ◽  
Intermittent Demand ◽  
Automotive Market ◽  
Knowledge Based ◽  
Automotive Company ◽  
Forecasting Methods ◽  
Historical Series

This chapter presents a study of forecasting methods applicable to the spare parts demand faced by an automotive company that maintains a share of nearly 25% of the automotive market and sells approximately 13,000 parts per year. These parts are characterized by having intermittent demand and, in some cases, low demand, which makes it difficult for such companies to perform well and to obtain accurate forecasts. Therefore, this chapter includes a study of methods such as the Croston, Syntetos and Boylan, and Teunter methods, which are known to resolve these issues. Furthermore, the rolling Grey method is included, which is usually used in environments with short historical series and great uncertainty. In this study, traditional methods of prognosis, such as moving averages, exponential smoothing, and exponential smoothing with tendency and seasonality, are not neglected.

scholarly journals The use of Holt–Winters method for forecasting the amount of sewage inflowing into the wastewater treatment plant in Nowy Sącz

2016 ◽  
Vol 27 (2) ◽  
pp. 7-12
Author(s):  
Ewa Wąsik ◽  
Krzysztof Chmielowski
Keyword(s):  
Time Series ◽  
Wastewater Treatment ◽  
Linear Regression ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Exponential Smoothing ◽  
Model Fit ◽  
Daily Amount

Abstract The aim of the study was to determine changes of daily amount of sewage inflowing into a wastewater treatment plant in Nowy Sącz in the years 2008-2014. To this end, the data in the form of time series corresponding to the investigated multi-year period were analysed. Daily volume of sewage for annual periods was forecast using a seasonal method of Holt and Winters based on the exponential smoothing algorithms. The model fit to actual daily amount of sewage for 2014 was assessed using linear regression. The results of fit for the additive Holt-Winters model confirmed the usefulness of this tool for forecasting the amount of sewage inflowing into the wastewater treatment plant.

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