PERENCANAAN PERSEDIAAN BAHAN BAKU AKAR TANJUNG DENGAN METODE MATERIAL REQUIREMENTS PLANNING (MRP) PADA UPT. MAKARTI POMOSDA

2020 ◽  
Vol 16 (3) ◽  
pp. 1-12
Author(s):  
Khoirul Hidayah ◽  
Sukarni Sukarni ◽  
Achmad Syaichu
Keyword(s):  
Standard Error ◽  
Mean Squared Error ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Material Requirements Planning ◽  
Safety Stock ◽  
Mean Error ◽  
Squared Error ◽  
Material Requirement

Suatu produksi yang direncanakan dengan baik akan menghasilkan efektivitas dan efisiensi produksi bagi perusahaan. Pentingnya perencanaan material pada perusahaan diharapkan dapat menghasilkan sistem yang baik terhadap proses produksi. Tujuan dari penelitian ini adalah untuk mengetahui penerapan Material Requirement Planning (MRP) sehingga kebutuhan bahan baku selama proses produksi di UPT MAKARTI POMOSDA dapat terpenuhi dengan menggunakan metode peramalan forecasting dalam satu tahun yaitu, moving average dan weighted moving average.  Metode ini terpilih untuk mengetahui safety stock nya produk setiap bulan dan setiap tahun. Berdasarkan detail dan analisa kesalahan metode moving average dengan menggunakan program POM QM forWindows Versi 3 Basic (Mean Error) 42,455, MAD (Mean Absolute Deviation) 259,545, MSE (Mean Squared Error) 118490,6, Standard Error (denom=n-2=9) 380,555, MAPE (Mean Absolute Percent Error) 643, dan next period 480. Sedangkan detail dan analisa kesalahan metode ini dengan menggunakan program POM QM For Windows Versi 3 Basic (Mean Error) 38,827, MAD (Mean Absolute Deviation) 212,257, MSE (Mean Squared Error) 83586,58, Standard Error (denom=n-2=9) 323,239, MAPE (Mean Absolute Percent ) 495, dan next period 464,893. Berdasarkan hasil proses diatas juga diketahui (safety stock) pada UPT MAKARTI POMOSDA pada tahun 2017 yaitu sejumlah 5209 unit, setelah dilakukan penelitian mengalami kenaikan sebesar 6758 dengan prosentase sebesar 129,7%, sehingga tidak ada penumpukan barang digudang. Hal ini juga didukung dengan penurunan biaya simpan bahan baku dari Rp 120.850/Periode (bulan) menjadi Rp 109.350/Periode (bulan).

PERENCANAAN PERSEDIAAN BAHAN BAKU AKAR TANJUNG DENGAN METODE MATERIAL REQUIREMENTS PLANNING (MRP) PADA UPT. MAKARTI POMOSDA

2020 ◽  
Vol 16 (2) ◽  
pp. 22-33
Author(s):  
Khoirul Hidayah ◽  
Sukarni Sukarni ◽  
Achmad Syaichu
Keyword(s):  
Standard Error ◽  
Mean Squared Error ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Material Requirements Planning ◽  
Safety Stock ◽  
Mean Error ◽  
Squared Error ◽  
Material Requirement

Suatu produksi yang direncanakan dengan baik akan menghasilkan efektivitas dan efisiensi produksi bagi perusahaan. Pentingnya perencanaan material pada perusahaan diharapkan dapat menghasilkan sistem yang baik terhadap proses produksi. Tujuan dari penelitian ini adalah untuk mengetahui penerapan Material Requirement Planning (MRP) sehingga kebutuhan bahan baku selama proses produksi di UPT MAKARTI POMOSDA dapat terpenuhi dengan menggunakan metode peramalan forecasting dalam satu tahun yaitu, moving average dan weighted moving average.  Metode ini terpilih untuk mengetahui safety stock nya produk setiap bulan dan setiap tahun. Berdasarkan detail dan analisa kesalahan metode moving average dengan menggunakan program POM QM forWindows Versi 3 Basic (Mean Error) 42,455, MAD (Mean Absolute Deviation) 259,545, MSE (Mean Squared Error) 118490,6, Standard Error (denom=n-2=9) 380,555, MAPE (Mean Absolute Percent Error) 643, dan next period 480. Sedangkan detail dan analisa kesalahan metode ini dengan menggunakan program POM QM For Windows Versi 3 Basic (Mean Error) 38,827, MAD (Mean Absolute Deviation) 212,257, MSE (Mean Squared Error) 83586,58, Standard Error (denom=n-2=9) 323,239, MAPE (Mean Absolute Percent ) 495, dan next period 464,893. Berdasarkan hasil proses diatas juga diketahui (safety stock) pada UPT MAKARTI POMOSDA pada tahun 2017 yaitu sejumlah 5209 unit, setelah dilakukan penelitian mengalami kenaikan sebesar 6758 dengan prosentase sebesar 129,7%, sehingga tidak ada penumpukan barang digudang. Hal ini juga didukung dengan penurunan biaya simpan bahan baku dari Rp 120.850/Periode (bulan) menjadi Rp 109.350/Periode (bulan).

scholarly journals PERAMALAN PERMINTAAN PRODUK SALE PISANG PADA INDUSTRI “SAHABAT” DI DUSUN CIJOHO DESA MARGAJAYA KECAMATAN SUKADANA KABUPATEN CIAMIS

2020 ◽  
Vol 7 (3) ◽  
pp. 634
Author(s):  
Nisa Aprilianti ◽  
Iwan Setiawan ◽  
Muhamad Nurdin Yusuf
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Squared Error

Perkembangan industri sale pisang yang ada di Desa Margajaya cukup penting dan menarik untuk diteliti, Sahabat merupakan industri yang berdiri relatif baru dengan memproduksi sale pisang yang cukup besar, jumlah penjualan produk sale pisang pada industri Sahabat belum efesien, karena produk yang dijual sering dikembalikan dalam jumlah cukup banyak oleh reseller. Penelitian ini bertujuan mengetahui ramalan permintaan produk sale pisang pada industri “Sahabat” di Dusun Cijoho Desa Margajaya Kecamatan Sukadana Kabupaten Ciamis pada bulan Maret samapai Desember tahun 2020. Penelitian ini dilaksanakan pada bulan Februari 2020. Responden dalam penelitian ini adalah pemilik agroindustri “Sahabat”. Jenis penelitian yang digunakan adalah penelitian kuantitatif  dan metode penelitian yang digunakan yaitu data primer dan data sekunder. Analisis yang digunakan adalah Single moving average (Rata-rata bergerak tunggal). Hasil penelitian menunjukkan bahwa dengan metode single moving average (rata-rata bergerak) untuk Forecast adalah 12.744 bungkus, dengan Mean Absolute Deviation sebesar 1.639 dan Mean Squared Error sebesar 7.658. Hasil ramalan pada bulan Maret dapat dihasilkan pula hasil peramalan atau perkiraan pada tahun 2020.

scholarly journals ARDL METHOD: Forecasting Data Curah Hujan Harian NTB

Jurnal Varian ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 73-82
Author(s):  
Ulul Azmi ◽  
Zilullah Nazir Hadi ◽  
Siti Soraya
Keyword(s):  
Mean Squared Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Root Mean Squared Error ◽  
Absolute Percentage Error ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Squared Error ◽  
Parameter Error

Penelitian ini berisi tentang prediksi atau forecasting data iklim di Nusa Tenggara Barat (NTB) tahun 2011, yakni jumlah hari terjadinya hujan dengan menggunakan metode Autoregressive Distributed Lag (ARDL). Data yang digunakan yaitu data iklim di Nusa Tenggara Barat (NTB) dari tahun 2006 -2010, dengan menggunakan beberapa parameter error seperti Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) dan Mean Absolute Percentage Error (MAPE). Berdasarkan hasil simulasi data iklim di Nusa Tenggara Barat (NTB) tersebut, diperoleh prediksi jumlah hari terjadinya curah hujan pada tahun 2011 sebesar 226 hari dengan nilai MAD 20,8069, MSE 3,5569, RMSE 1,88597, dan MAPE 11,9297 . Dan prediksi jumlah hari terjadinya hujan pada tahun 2011 sebanyak 225,928 hari atau jika di bulatkan menjadi 226 hari dengan nilai parameter error MAD sebesar 20,8069, sehingga dapat disimpulkan pada tahun 2011 terjadi peningkatan jumlah hari terjadinya hujan di Nusa Tenggara Barat (NTB).

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. 

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 Estimating modulus of elasticity (MOE) of particleboards using artificial neural networks to reduce quality measurements and costs

2019 ◽  
Vol 70 (3) ◽  
pp. 257-263
Author(s):  
Rıfat Kurt ◽  
Selman Karayilmazlar
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modulus Of Elasticity ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Squared Error ◽  
Artificial Neural

There are a large number of costs that enterprises need to bear in order to produce the same product at the same quality for a more affordable price. For this reason, enterprises have to minimize their expenses through a couple of measures in order to offer the same product for a lower price by minimizing these costs. Today, quality control and measurements constitute one of the major cost items of enterprises. In this study, the modulus of elasticity values of particleboards were estimated by using Artificial Neural Networks (ANN) and other mechanical properties of particleboards in order to reduce the measurement costs in particleboard enterprises. In addition to that, the future values of modulus of elasticity were also estimated using the same variables with the purpose of monitoring the state of the process. For this purpose, data regarding the mechanical properties of the boards were randomly collected from the enterprise for three months. The sample size (n) was: 6 and the number of samples (m): 65 and a total of 65 average measurement values were obtained for each mechanical property. As a result of the implementation, the low Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Squared Error (MSE) performance measures of the model clearly showed that some quality characteristics could easily be estimated by the enterprises without having to make any measurements by ANN.

scholarly journals Previsão dos modelos univariados e rede neural da demanda turística do estado de Mato Grosso do Sul

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Bruno Matos Porto ◽  
Daniela Althoff Philippi ◽  
Vanessa Aline Wagner Leite
Keyword(s):  
Mean Squared Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Mato Grosso ◽  
Absolute Percentage Error ◽  
Squared Error ◽  
Mato Grosso Do Sul

O objetivo deste artigo foi gerar previsões de curto, médio e longo prazos e comparar a precisão dos modelos em cada horizonte de previsão. Para atender o objetivo foram aplicados os modelos univariados e rede neural (NNAR) nos dados da demanda turística do estado de Mato Grosso do Sul (MS). A amostra foi coletada na ferramenta base de dados extrator do Instituto Brasileiro de Turismo (Embratur) referente as chegadas turísticas por todas as vias registradas no MS entre janeiro de 2007 a dezembro de 2017. As previsões dos modelos de previsão ARIMA, Holt-Winters (HW) versões aditiva e multiplicativa e NNAR foram projetadas, por meio da linguagem de programação R, com uso do software R Studio. O procedimento empírico de execução dos scripts de todos os modelos foi disponibilizado. As predições fora da amostra da procura do turismo abrangeram o intervalo de janeiro até dezembro de 2018, sendo então comparadas aos dados reais do mesmo período. As previsões dos modelos foram comparadas no curto, médio e longo prazo mediante os critérios Mea Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) e Mean Squared Error (MSD). A rede neural (NNAR) superou os modelos testados em diferentes horizontes de previsão e as medidas de erros mostraram que a NNAR é altamente precisa. Em segundo lugar no ranking de acuracidade destacou-se ARIMA. Os resultados mostraram que as previsões da rede neural auxiliam na tomada de decisão dos planejadores turísticos de MS. Para pesquisas futuras recomenda-se realizar previsões fora da amostra num amplo número de séries temporais.

scholarly journals Prediksi Tingkat Produksi Buah Kelapa Sawit dengan Metode Single Moving Average

2021 ◽  
pp. 251-256
Author(s):  
Feri Irawan ◽  
S Sumijan ◽  
Y Yuhandri
Keyword(s):  
Oil Palm ◽  
Mean Squared Error ◽  
Moving Average ◽  
Fruit Production ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Palm Fruit ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Absolute Percent Error

Palm oil is one of the largest agricultural products in Indonesia and has a high economic value and can improve the welfare of oil palm farmers. The amount of oil palm fruit production is not always stable or increasing, but increases up and down which is influenced by many factors. This study aims to estimate the average amount of oil palm fruit production every year and prepare anticipatory steps in the event of a decrease in oil palm fruit production. The image processed in this study was the production of palm fruit in a few years which was generated from the results of oil palm plantations. Furthermore, data is processed using the Single Moving Avarage method. This method is a method of forecasting or predictions using a number of actual data to generate predictive values ​​in the future. The results of testing on the single moving average method can be seen forecasts of oil palm fruit production in 2021 using Moving Averge 3 of 200.749 tons with Mean Absolute Deviation 19.604, Mean Squared Error  456.963.281  and Mean Absolute Percent Error 10,0%. Moving Averge 4 was  206.771 tons with the Mean Absolute Deviation  27.333, Mean Squared Error  752.202.579 and Mean Absolute Percent Error 14,2%. Moving Averge 5 was  210.908 tons with Mean Absolute Deviation  26.890, Mean Squared Error  723.072.100 and Mean Absolute Percent Error 14.1%. The test results using the Single Moving Average method can be concluded that forecasting using Moving Average 3 can be used because the relative error level is smaller than Moving Average 4 and 5, with the value of the Mean Absolute Percent error of 10.0% and Mean Absolute Deviation 19.604.

scholarly journals Determining The Optimal Values Of Exponential Smoothing Constants Does Solver Really Work?

2013 ◽  
Vol 6 (3) ◽  
pp. 347-360 ◽  
Author(s):  
Handanhal V. Ravinder
Keyword(s):  
Operations Management ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Management Science ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Squared Error ◽  
Optimal Values ◽  
The Impact

A key issue in exponential smoothing is the choice of the values of the smoothing constants used.One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE).We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants.We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants.

scholarly journals Applied Exponential Smoothing Holt-Winter Method for Predict Rainfall in Mataram City

2020 ◽  
Vol 1 (2) ◽  
pp. 45
Author(s):  
Dewi Darma Pertiwi
Keyword(s):  
Mean Squared Error ◽  
Secondary Data ◽  
Weather Conditions ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Data Smoothing ◽  
Squared Error ◽  
The City

Weather conditions in the city of Mataram tend to be erratic and difficult to predict, such as the condition of rainfall data in 2018 which changes over a certain period of time so that the weather is difficult to predict accurately. In this study, we propose the Exponential Smoothing Holt-Winter method to forecast rainfall in the city of Mataram, so that it can be a decision support for various interested sectors. This method has been tested using secondary data from the Mataram City Central Bureau of Statistics for the period January 2014 to 2018 and evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results of this study indicate that using the Exponential Smoothing Holt-Winter method yields better results, each of which is MAPE 142.3, MAD 95.6 and MSD value 24988.7 and the data smoothing value is obtained for the smallest combination value of α 0.2, β 0.1, and γ 0.1. It can be concluded that the proposed method can provide better information and can be used to predict rainfall in Mataram City for the next 12 periods.

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